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Методы оптимизации в машинном и глубоком обучении. От простого к сложному

В данной статье представлен обзор различных популярных (и не только) оптимизаторов, которые применяются в машинном и глубоком обучении, в частности для обучения нейронных сетей. Мы рассмотрим их основную идею и ключевые особенности, переходя от простых к более сложным концепциям. Помимо этого, в самом конце вы сможете найти большое количество дополнительных источников для более детального ознакомления с материалом.

Ноутбук с данным материалом можно загрузить на GitHub (rus).

Содержание

Пару слов про функции потерь
Классический градиентный спуск
Более быстрые и точные оптимизаторы
Обучение и визуализация оптимизаторов
Нарушение работы адаптивных методов со скользящим средним
Стратегии изменения скорости обучения
Работа с большими мини-пакетами
Проксимальные методы
Методы второго порядка
Дополнительные источники

Пару слов про функции потерь

Функции потерь можно разделить на выпуклые и невыпуклые. Первый вариант чаще всего встречается в классическом машинном обучении из-за относительной простоты моделей, а также из-за того, что у выпуклых функций локальный минимум по определению является и глобальным (но не обязательно единственным). Невыпуклые функции, в свою очередь, чаще всего встречаются в нейронных сетях из-за их высокой сложности и в данном случае поиск глобального минимума является трудной задачей, поэтому на практике здесь также используются методы из случая с выпуклыми функциями из-за их способности хорошо аппроксимировать стационарные точки в невыпуклом случае. Проще говоря, методы для выпуклого случая могут хорошо сходиться в локальном оптимуме для невыпуклого случая за приемлемое время, значение которого близко к глобальному.

Классический градиентный спуск

Начнём с того, что если градиент — это вектор наибыстрейшего возрастания функции, то антиградиент — вектор наибыстрейшего убывания, и именно при движении в данную сторону будет расположена минимальная ошибка модели. Тогда градиентный спуск можно определить как численный метод итеративной оптимизации для нахождения весов (коэффициентов) модели путём минимизации её ошибки, представленной в виде функции потерь. Пример работы данного алгоритма на линейной и логистической регрессий с нуля можно посмотреть здесь и здесь.

Градиентный спуск для выпуклого и невыпуклого случая

Алгоритм строится следующим образом:

1) изначально происходит инициализация весов с нулевыми значениями;
2) далее на основе установленных весов модель делает прогноз;
3) на основе полученного прогноза рассчитывается градиент ошибки, после чего происходит обновление весов в сторону антиградиента функции потерь;
4) шаги 2-3 повторяются до тех пор, пока градиент не станет нулевым, то есть пока алгоритм не сойдётся в минимуме (как правило, в данном случае используется критерий останова, например, пока разность градиентов на текущей и предыдущей итерациях не станет меньше заранее установленного порогового значения).

Если для вычисления градиентов функции потерь используется полный обучающий набор, то такой градиентный спуск называется пакетным. Не смотря на то, что пакетный градиентный спуск (batch gradient descent) практически всегда хорошо масштабируется в отношении количества признаков, данный алгоритм работает очень медленно на больших наборах данных и требует значительных затрат в виде дополнительной памяти для хранения всех градиентов.

Для увеличения производительности на больших датасетах применяются 2 подхода:

1) Мини-пакетный градиентный спуск (mini-batch gradient descent), когда на каждом шаге вычисление градиентов происходит на небольших случайных поднаборах (мини-пакетах). Обычно размер мини-пакета берётся в виде и может достигать нескольких десятков тысяч образцов: выбор размера зависит от особенностей задачи и применяемой модели.
2) Стохастический градиентный спуск (stochastic gradient descent), когда на каждом шаге из обучающего набора берётся лишь один образец. Очевидно, что такой вариант должен работать гораздо быстрее предыдущего, но с другой стороны, из-за ещё более стохастический природы данный алгоритм менее стабилен и может потребоваться больше итераций для сходимости, а также mini-batch дает прирост в производительности из-за аппаратной оптимизации матричных вычислений на GPU. Стоит также отметить, что на данный момент под стохастическим градиентный спуском очень часто подразумевается mini-batch.

В отличие от пакетного градиентного спуска, в данных случаях функция потерь будет снижаться скачками вверх-вниз, приближаясь к минимуму, но так и не достигнув его. В случае с мини-пакетным градиентным спуском функция потерь будет расположена чуть ближе к минимуму за счёт меньшего размера скачков. С другой стороны, в отличие от стохастического градиентного спуска, ему может быть труднее пройти локальные минимумы в невыпуклом случае.

Более быстрые и точные оптимизаторы

Помимо того, что градиентный спуск в своём классическом представлении плохо подходит для невыпуклых случаев из-за вышеописанных проблем, может также возникнуть проблема с "седловыми точками", когда на поверхности функции потерь в виде седла имеются точки, в которых значение функции максимально по одним направлениям и минимально по другим, то есть в данных точках будет практически нулевое снижение градиентов и они ошибочно могут быть приняты моделью за оптимум. Поэтому для быстрого поиска оптимального решения используются более интересные стохастические (mini-batch) оптимизации, про самые популярные из которых сейчас и пойдёт речь.

Momentum

Предположим, что со склона с кочками катится мяч, который сначала будет двигаться медленно, но по мере накопления кинетической энергии его скорость будет расти, что ему поможет преодолеть небольшие кочки и остановить своё движение в более глубокой. Именно данный принцип лежит в основе моментной оптимизации. Momentum принимает во внимание значения предыдущих градиентов, которые используются для определения направления и скорости перемещения в пространстве параметров v_t (называется вектором первого момента, поскольку масса в данном случае равна 1), а для предотвращения её быстрого увеличения используется коэффициент ослабления импульса $\beta$ , который отвечает за силу влияния предыдущего градиента на текущий, то есть выступает в качестве механизма трения мяча о поверхность: значение 0 означает высокое трение, а 1 его отсутствие. Обычно значение $\beta$ устанавливается 0.9.

Алгоритм обновления параметров моментной оптимизации

$v_t \leftarrow \beta v_{t-1} - \alpha dw_{t-1}$

$w_t \leftarrow w_{t-1} + v_t$

Momentum способен решить сразу две проблемы: плохую обусловленность матрицы Гессе и дисперсию стохастического градиента. С другой стороны, из-за вычисления градиента функции потерь в локальной позиции модель может колебаться вокруг локальных минимумов, что в свою очередь увеличивает время схождения.

Хотя во многих статьях обозначается как , а как , на данный момент нет единого стандарта и в разных статьях можно встретить для них разные обозначения, но для унификации во всех примерах будут использоваться следующие обозначения: — это первый момент или ещё по-другому экспоненциально взвешенное скользящее среднее градиентов, а — второй момент или экспоненциально взвешенное скользящее среднее квадратов градиентов, однако в случае AdaGrad это просто квадрат градиентов.

Nesterov momentum

Для решения вышеописанной проблемы применяется небольшая оптимизация, суть которой заключается в вычислении градиентов не в локальной позиции, а чуть дальше в направлении момента. Другими словами, функция потерь будет снижаться не в текущей позиции, а в которой якобы должна оказаться в будущем, следуя направлению импульса.

Алгоритм обновления параметров моментной оптимизации Нестерова

$v_t \leftarrow \beta v_{t-1} - \alpha d(w_{t-1} + \beta v_{t-1} )$

$w_t \leftarrow w_{t-1} + v_t$

Схема Momentum vs Nesterov momentum

Такой подход позволяет сойтись гораздо быстрее и чуть ближе к оптимуму, однако он всё ещё может быть чувствительным в отношении одних направлений пространства параметров и нечувствительным в других из-за трудностей подбора оптимальной скорости обучения. Ниже будут представлены оптимизации градиентного спуска с адаптивной скоростью обучения.

AdaGrad

Adaptive Gradient является одной из первых оптимизаций градиентного спуска с адаптивным шагом, в которой снижается градиент вдоль самых крутых направлений. Проще говоря, на каждом шаге происходит обновление параметров модели с учётом значений как предыдущих градиентов, так и их квадратов.

Алгоритм обновления параметров AdaGrad

$s_t \leftarrow s_{t-1} + dw_{t-1}^2$

$w_t \leftarrow w_{t-1} - \frac{\alpha}{\sqrt{s_t + \epsilon}} dw_{t-1}$

Где $\epsilon = 1e-8$ — сглаживающий параметр для избегания деления на ноль.

Такой простой подход позволяет алгоритму быстро начать обучение, а затем замедлить его, когда градиент становится меньше или меняет знак. Однако на практике AdaGrad хорошо справляется с выпуклыми случаями, а вот при обучении нейронных сетей нередко останавливается слишком рано из-за быстрого снижения скорости обучения, так и не достигнув глобального оптимума.

RMSProp

RMSProp (Root Mean Square Propagation) — модификация AdaGrad, адаптированная для лучшей работы в невыпуклом случае. Основная идея заключается в изменении способа агрегирования градиента на экспоненциально взвешенное скользящее среднее. Другими словами, вместо накопления всех квадратов градиента с начала обучения, накапливаются квадраты градиента только из самых последних итераций.

Алгоритм обновления параметров RMSProp

$s_t \leftarrow \beta s_{t-1} + (1 - \beta) dw_{t-1}^2$

$w_t \leftarrow w_{t-1} - \frac{\alpha}{\sqrt s_t + \epsilon} dw_{t-1}$

В целом, RMSProp является крайне эффективной оптимизацией, но не самой лучшей. Например, могут возникнуть трудности обучения в многомерных пространствах из-за проблем с масштабированием градиентов в разных направлениях.

Adam и его модификации

Adam (Adaptive Moment Estimation) объединяет в себе концепции Momentum и RMSProp, практически являясь серебряной пулей в задачах стохастической оптимизации. Как можно заметить, расчёт моментов в Adam очень схож с Momentum и RMSProp за исключением того, что к ним применяется поправка на смещение. В целом, благодаря таким улучшениям Adam сходится быстрее и лучше предшественников, а также более устойчив к подбору гиперпараметров, что делает его более стабильным решением в большинстве случаев.

Алгоритм обновления параметров Adam

$v_t \leftarrow \beta_1 v_{t-1} + (1 - \beta_1) dw_{t-1}$

$s_t \leftarrow \beta_2 s_{t-1} + (1 - \beta_2) dw_{t-1}^2$

$w_t \leftarrow w_{t-1} - \alpha \frac{v_t}{\sqrt s_t + \epsilon}$

Поправка на смещение:

$v_t \leftarrow \frac{v_t}{1 - \beta_1^t}$

$s_t \leftarrow \frac{s_t}{1 - \beta_2^t}$

Где $\beta_1 = 0.9, \ \ \beta_2 = 0.99$ используются по умолчанию.

С другой стороны, данный алгоритм более сложен в анализе и может быть чувствителен к шуму, что может привести к проблемам со сходимостью, а также он требует больших объемов памяти, поскольку хранит не только параметры модели, но а также информацию о градиентах и накопленном импульсе. Помимо этого, у Adam и всех предыдущих алгоритмов есть ещё один неприятный недостаток: если продолжить обучение модели после прерывания в некоторой точке, придётся восстанавливать не только веса модели из данной точки, но а также и всю информацию о накопленных параметрах.

Однако время не стоит на месте и были разработаны модификации Adam, призванные устранить в той или иной степени недостатки, описанные выше. К наиболее популярным и интересным модификациям можно отнести следующие:

AdaMax использует экспоненциально взвешенную норму бесконечности для обновления вектора второго момента, то есть скорость обучения изменяется, основываясь максимальной величине градиента в истории:

$s_t \leftarrow max(\beta_2 s_{t-1} |dw_{t-1}|)$

Nadam (Nesterov-accelerated Adaptive Moment Estimation) использует другую поправку на смещение для вектора первого момента:

$\hat v_t \leftarrow \frac{(1 - \beta_1^t) dw_{t-1}}{1 - \Pi_{i=1}^{t} \beta_1^i} + \frac{\beta_1^{t+1} v_t}{1 - \Pi_{i=1}^{t+1} \beta_1^i}$

AdamW добавляет L2-регуляризацию к функции потерь и при обновлении весов:

$$w_t \leftarrow w_{t-1} - \frac{\alpha v_t}{\sqrt s_t + \epsilon} + \lambda w_{t-1}$

Yogi обновляет вектор второго момента с учётом разности второго момента и квадрата градиента:

$s_t \leftarrow s_{t-1} - (1 - \beta_2) sign(s_{t-1} - dw_{t-1}^2) dw_{t-1}^2$

Adan (Adaptive Nesterov Momentum) использует модифицированный New Nesterov Momentum (NME позволяет избежать дополнительных затрат на вычисление градиента в точке экстраполяции) для оценки первого и второго моментов, что позволяет значительно ускорить сходимость и найти приближённую точку первого порядка с заданной точностью ϵ. Схема работы алгоритма выглядит следующим образом:

Обучение и визуализация оптимизаторов

Для наглядности рассмотрим небольшой пример работы различных оптимизаторов на данных Boston Housing. Сначала мы создадим простую нейросеть в Pytorch и обучим её на всех тренировочных данных, на которых после же оценим снижение потерь оптимизаторов на каждой итерации. Также для любителей Keras мы обучим простую нейросеть на мини-пакетах, но уже оценим снижение потерь на тестовом наборе.

Загрузка и подготовка датасета

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

df_path = "/content/drive/MyDrive/BostonHousing.csv"
boston = pd.read_csv(df_path)
print(boston)


        crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \
0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   
1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   
2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   
3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   
4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   
..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   
501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   
502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   
503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   
504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   
505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   

     ptratio       b  lstat  medv  
0       15.3  396.90   4.98  24.0  
1       17.8  396.90   9.14  21.6  
2       17.8  392.83   4.03  34.7  
3       18.7  394.63   2.94  33.4  
4       18.7  396.90   5.33  36.2  
..       ...     ...    ...   ...  
501     21.0  391.99   9.67  22.4  
502     21.0  396.90   9.08  20.6  
503     21.0  396.90   5.64  23.9  
504     21.0  393.45   6.48  22.0  
505     21.0  396.90   7.88  11.9  

[506 rows x 14 columns]

print(boston.isna().sum())


crim       0
zn         0
indus      0
chas       0
nox        0
rm         5
age        0
dis        0
rad        0
tax        0
ptratio    0
b          0
lstat      0
medv       0
dtype: int64

boston.dropna(inplace=True)

X, y = boston.iloc[:, :-1].values, boston.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

X_train_s = StandardScaler().fit_transform(X_train)
X_test_s = StandardScaler().fit_transform(X_test)

Параметры нейросети

nn_params = {'in_features': X.shape[1], 'h1': 16, 'h2': 8, 'out': 1}

Сравнение оптимизаторов Pytorch на тренировочных данных

from torch import nn, optim, FloatTensor, relu, manual_seed


class TorchNN(nn.Module):
    def __init__(self, in_features, h1, h2, out):
        super(TorchNN, self).__init__()
        self.fc1 = nn.Linear(in_features, h1)
        self.fc2 = nn.Linear(h1, h2)
        self.fc3 = nn.Linear(h2, out)

    def forward(self, x):
        x = relu(self.fc1(x))
        x = relu(self.fc2(x))
        x = self.fc3(x)

        return x


def torch_losses(data, model, optim_cls, optim_params, num_epochs=100):
    losses = []
    X_train, y_train = data
    criterion = nn.MSELoss()
    optimizer = optim_cls(model.parameters(), **optim_params)

    for epoch in range(num_epochs):
        y_pred = model.forward(X_train)
        loss = criterion(y_pred, y_train)
        losses.append(loss.item())

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    return losses


manual_seed(0)
X_train_s_tensor = FloatTensor(X_train_s)
y_train_tensor = FloatTensor(y_train).view(-1, 1)

torch_optimizers = [
    (optim.SGD, 'SGD', {'lr': 0.001}),
    (optim.SGD, 'Momentum', {'lr': 0.001, 'momentum': 0.9}),
    (optim.SGD, 'Nesterov Momentum', {'lr': 0.001, 'momentum': 0.9, 'nesterov': True}),
    (optim.Adagrad, 'Adagrad', {'lr': 0.01}),
    (optim.RMSprop, 'RMSprop', {'lr': 0.01}),
    (optim.Adam, 'Adam', {'lr': 0.01}),
    (optim.Adamax, 'Adamax', {'lr': 0.01}),
    (optim.AdamW, 'AdamW', {'lr': 0.01}),
    (optim.NAdam, 'NAdam', {'lr': 0.01})
    ]

# Visualization
plt.figure(figsize=(14, 6))

for optim_cls, optim_name, optim_params in torch_optimizers:
    nn_model = TorchNN(**nn_params)
    t_losses = torch_losses((X_train_s_tensor, y_train_tensor), nn_model, optim_cls, optim_params)
    plt.plot(range(len(t_losses)), t_losses, label=f'{optim_name}')

plt.title('Pytorch optimizers comparison on train data')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

Сравнение оптимизаторов Keras на тестовых данных

from keras import optimizers
from keras.layers import Dense
from keras.models import Sequential
from keras.utils import set_random_seed


def create_keras_nn(optimizer, in_features, h1, h2, out):
    model = Sequential()
    model.add(Dense(input_shape=(in_features,), units=h1, activation='relu'))
    model.add(Dense(input_shape=(h1,), units=h2, activation='relu'))
    model.add(Dense(units=out))
    model.compile(optimizer=optimizer, loss='mse', metrics=['mse'])

    return model


def keras_losses(train_data, test_data, optimizers, nn_params, num_epochs=10):
    losses = []
    X_train, y_train = train_data
    X_test, y_test = test_data

    for name, optimizer in optimizers:
        model = create_keras_nn(optimizer, **nn_params)
        model_history = model.fit(X_train, y_train, batch_size=32, epochs=num_epochs,
                                  verbose=0, validation_data=(X_test, y_test))
        losses.append((name, model_history.history['val_loss']))

    return losses


set_random_seed(0)

keras_optimizers = [
    ('SGD', optimizers.SGD(learning_rate=0.001)),
    ('Momentum', optimizers.SGD(learning_rate=0.001, momentum=0.9)),
    ('Nesterov Momentum', optimizers.SGD(learning_rate=0.001, momentum=0.9, nesterov=True)),
    ('Adagrad', optimizers.Adagrad(learning_rate=0.01)),
    ('RMSprop', optimizers.RMSprop(learning_rate=0.01)),
    ('Adam', optimizers.Adam(learning_rate=0.01)),
    ('Adamax', optimizers.Adamax(learning_rate=0.01)),
    ('AdamW', optimizers.AdamW(learning_rate=0.01)),
    ('Nadam', optimizers.Nadam(learning_rate=0.01))
    ]


plt.figure(figsize=(14, 6))

k_losses = keras_losses((X_train_s, y_train), (X_test_s, y_test), keras_optimizers, nn_params)

for name, k_loss in k_losses:
    plt.plot(k_loss, label=name + ' test Loss')

plt.title('Keras optimizers comparison on test data')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

Промежуточные выводы

Поскольку SDG, Momentum и Nesterov momentum являются менее стабильными алгоритмами и больше всего зависят от начальных значений learning rate, для них была задана меньшая скорость обучения, чем для остальных методов. На первом графике видно, что даже в таком случае эти алгоритмы немного подвержены колебаниям, хотя и быстро сошлись. Второй же график хорошо демонстрирует как обучение на мини-пакетах значительно ускоряет сходимость алгоритмов. В случае с AdaGrad возникла классическая проблема резкого снижения скорости обучения, которую в данном случае можно легко решить, установив изначально более высокое значение. А вот RMSprop хорошо сработал в обоих случаях и даже лучше Adam-подобных оптимизаторов.

Отсюда следует интересный вывод, что адаптивные методы могут сходиться быстрее предшественников далеко не в каждой ситуации, но зато они гораздо стабильнее минимизируют функцию потерь. Конкретно в данном случае это означает, что если мы установим одинаковые произвольные значения learning rate для всех оптимизаторов, то неадаптивные методы зачастую будут снижаться с очень большими рывками и могут не сойтись вообще, в то время как адаптивные методы будут более плавно сходиться, хоть и медленно.

Стоит добавить, что данный пример лишь в очередной раз доказывает, что нет универсального оптимизатора, который являлся бы лучшим выбором для всех задач машинного обучения, поскольку многое зависит от самых разных факторов, включая природу данных, сложность модели и цели обучения. Однако это не отменяет тот факт, что в случае со сложными ландшафтами потерь адаптивные методы практически всегда будут превосходить простые стохастические оптимизаторы не только в плане стабильности, но и конечно же в скорости. Поэтому в большинстве ситуаций RMSprop и особенно семейство оптимизаторов Adam можно рассматривать в качестве хорошего стартового решения.

Нарушение работы адаптивных методов со скользящим средним

Поскольку работа всех диагональных адаптивных методов в той или иной степени основана на использовании покоординатного learning rate, то в случае, когда последовательность s_t перестаёт монотонно не убывать, алгоритм не может нормально сойтись к локальным минимумам. Как правило, такая ситуация означает приближение к критическим точкам и происходит при уменьшении $dw_{t-1}^2$ в сравнении с предыдущей накопленной историей с точностью до некоторой константы.

Данную проблему можно устранить путём наделения таких алгоритмов долговременной памятью о прошлых градиентах для исключения отрицательных регуляризаторов подобно тому, как это реализовано в AMSGrad. Проще говоря, выполняется условие $s_t >= s_{t-1}$ , что возможно благодаря простому трюку:

$\hat s_0 = 0, \ \hat s_t = max(\hat s_{t-1}, s_t) \ and \ \hat S_t = \ diag(\hat s_t)$

Однако стоит иметь в виду, что в таком случае требуются дополнительные расходы памяти, что может быть особенно проблематично для моделей с разреженными параметрами. Решить данную проблему можно за счёт использования непостоянных $\beta_1^t$ и $\beta_2^t$ , что было предложено в алгоритме AdamNC:

$v_t \leftarrow \beta_1^t v_{t-1} + (1 - \beta_1^t) dw_{t-1} \\ s_t \leftarrow \beta_2^t s_{t-1} + (1 - \beta_2^t) dw_{t-1}^2$

Помимо того, что такой подход не требует изменения структуры оригинального Adam, он также позволяет достичь хороших показателей сходимости.

Однако в работе Tran Thi Phuong и Le Trieu Phong было показано, что проблема в доказательстве сходимости AMSGrad заключается в обработке гиперпараметров, рассматривая их как равные, в то время как это не совсем верно. Так авторами был представлен контрпример в контексте простой задачи выпуклой оптимизации, который показывает эту проблему. Алгоритм должен сходиться согласно следующей лемме:

$R(T) \leq \sum_{i=1}^{d} \sum_{t=1}^{T} \frac{\sqrt{\hat{s}_{t,i}}}{2\alpha_t(1 - \beta_1^t)} ((w_{t,i} - w^*_{i})^2 - (w_{t+1,i} - w^*_{i})^2) + \\ + \sum_{i=1}^{d} \sum_{t=1}^{T} \frac{\alpha_t}{1 - \beta_1} \frac{v^2_{t,i}}{\sqrt{\hat{s}_{t,i}}} + \sum_{i=1}^{d} \sum_{t=2}^{T} \frac{\beta_1^t \sqrt{\hat{s}_{t-1,i}}}{2\alpha_{t-1}(1 - \beta_1)} (w_{t,i} - w^*_{i})^2$

Однако проблема заключается в равенстве, которое на самом деле может быть как положительным, так и отрицательным:

$(w_{t,i} - w^*_{i})^2 - (w_{t+1,i} - w^*_{i})^2$

Поэтому авторы представили новое доказательство сходимости для AMSGrad, а также алгоритм AdamX на его основе, который учитывает различия в гиперпараметрах и позволяет избежать проблемы выше. Решение выглядит следующим образом:

$\hat s_1 = s_1, \ \hat s_t = max \left(\frac{(1 - \beta_1^t)^2}{(1 - \beta_1^{t-1})^2} \hat s_{t-1}, s_t \right) \ \ if \ \ t \geq 2, \ and \ \hat S_t = diag(\hat s_t)$

Но стоит иметь в виду, что как и в случае с AMSGrad, AdamX также требует дополнительных расходов памяти.

Стратегии изменения скорости обучения

Как можно было заметить, скорость обучения является одним из ключевых параметров в градиентном спуске, которая может оказывать значительное влияние на сходимость и конечную ошибку модели. Помимо вышеописанных стратегий сокращения скорости обучения, ещё одними из самых популярных являются следующие:

1) $\alpha = \frac{1}{1 + decay \_ rate * t} \alpha_0 \ - \$ плавное затухание;
2) $\alpha = k^t \alpha_0 \ - \$ экспоненциальное затухание;
3) $\alpha = \frac{k}{\sqrt{t}} \alpha_0$ , где $k \ -$ константа, а $t \ -$ число итераций;
4) дискретное сокращение, когда после заданного количества итераций шаг уменьшается с определённым размером.

Не смотря на то, что на сегодняшний день в алгоритмах на основе градиентного спуска используется автоматическое снижение скорости обучения как в библиотеке sckit-learn, так и в библиотеках глубокого обучения, подбор learning rate всё ещё нужно выполнять аккуратно: алгоритм может преждевременно выйти на плато либо вовсе разойтись. Пример приведён на графике ниже.

Другим подходом в изменении скорости обучения является её резкое увеличение, чтобы "вытолкнуть" модель из возможных локальных минимумов, в которых она может застрять в процессе обучения. Такой подход называется Warm Restart и включает в себя циклическое изменение скорости обучения: начиная с высокого значения, скорость постепенно снижается по определённому правилу, а затем после достижения минимального значения она снова повышается. Это особенно полезно, когда модель сходится к неоптимальному решению из-за плохой инициализации или когда пространство параметров содержит множество локальных минимумов.

Одним из популярных методов, использующий такой подход, является CosineAnnealingWarmRestarts, в котором скорость обучения регулируется на основе "косинусного отжига" с перезапусками, возвращаясь к своему начальному значению:

$η_t = η_{min} + \frac{1}{2}(η_{max} - η_{min})(1 + \cos(\frac{T_{cur}}{T_{i}}\pi))$

Где:

$\eta_t$ — скорость обучения в момент времени ;
$\eta_{\text{max}}$ — начальная скорость обучения;
$\eta_{\text{min}}$ — минимальная скорость обучения;
$T_{\text{cur}}$ — количество эпох с момента последнего перезапуска;
а — количество эпох между двумя перезапусками.

С полным списком "планировщиков" скорости обучения можно ознакомиться здесь и здесь.

Работа с большими мини-пакетами

Рассмотрим ситуацию, когда нам необходимо обучить сложную нейронную сеть на больших данных с использованием нескольких графических процессоров (GPU). Одно из самых простых решений будет заключаться в вычислении стохастических градиентов для каждого батча отдельно на разных GPU с их последующим усреднением по всем GPU на каждом этапе обновления параметров модели.

По сути, такой подход равносилен обучению с одним огромным батчем и на первый взгляд может показаться оптимальным, но не всё так просто, поскольку это может привести к такому явлению как generalization gap, при котором возникает ухудшение обобщающей способности модели (иногда значительное). Почему так происходит? При использовании батча большого размера оптимизатор начинает лучше распознавать ландшафт функции потерь для определённой выборки, что увеличивает шансы скатиться в узкие локальные минимумы с низкой обобщающей способностью. Тогда даже при незначительном сдвиге ландшафта (при так называемом distributional shift, когда происходит переход от тренировочной выборки к тестовой) значение функции потерь может резко увеличиться. Проще говоря, большие батчи могут приводить к переобучению.

С другой стороны широкие локальные минимумы более устойчивы к изменениям в ландшафте функции потерь и способны обеспечить лучшую обобщающую способность, а для того чтобы в них попадать можно использовать оптимизаторы с более точным динамическим подбором learning rate, про которые далее пойдёт речь. Однако перед этим стоит добавить, что использование маленьких батчей в оптимизации также является не самой хорошей идеей, поскольку это может привести к более шумным оценкам градиента и в конечном счёте к недообучению модели.

LARS

Первым на очереди будет Layer-wise Adaptive Rate Scaling (LARS), использующий momentum в качестве базового алгоритма. Его основная идея заключается в подборе скорости обучения не для каждого нейрона или всей нейросети, а для каждого слоя отдельно. Проще говоря, обновление весов происходит с учётом локального learning rate, рассчитанного как отношение нормы весов к сумме нормы весов и градиентов. Работа алгоритма показана ниже:

Не смотря на то, что такой метод позволяет использовать батчи большого размера для обучения без потери качества, он плохо справляется со сложными моделями, где каждый слой имеет важное значение для общей производительности, например, с моделями внимания, такими как BERT. Это связано с тем, что если в одном из слоёв сети наблюдается нестабильное снижение градиентов, то к этому слою будет применяться меньшая скорость обучения, а поскольку LARS стремится синхронизировать обновления по всем слоям, то скорости обучения для более "стабильных" слоёв также будут снижены для предотвращения расхождения в обучении. Следовательно, более "стабильные" слои будут обновляться не так быстро, как могли бы, что в итоге значительно замедлит обучение всей модели и окажет негативное влияние на конечный результат.

LAMB

В ответ на это был разработан более продвинутый оптимизатор, известный как Layer-wise Adaptive Moments Based optimizer (LAMB), который можно рассматривать как применение LARS к оптимизатору Adam. Основное различие заключается в использовании двойной стратегии нормализации:

1) Нормализация каждого измерения (или параметра модели) относительно квадратного корня из второго момента, используемого в Adam. Это означает, что каждый параметр модели обновляется с учётом его собственной истории изменений, что помогает более точно настраивать веса модели.
2) Применение послойной нормализации. Другими словами, обновления параметров модели происходят с учётом масштаба каждого слоя в нейронной сети. Это помогает предотвратить слишком большие изменения в весах слоёв и избежать нестабильности в процессе обучения.

Алгоритм работает следующим образом:

Такой подход позволил значительно улучшить процесс обучения модели BERT даже на батчах большого размера без потери точности. Так команде Google Brain удалось сократить время обучения с 3 дней до всего лишь 76 минут, увеличив размер пакета до предела памяти модуля TPUv3. Более того, как и другие оптимизаторы, LAMB имеет ряд модификаций, способных не только ускорить обучение (как в NVLAMB благодаря предварительной нормализации градиентов), но и сделать его ещё более стабильным в плане сходимости (как в N-LAMB и NN-LAMB).

Прокcимальные методы

Проксимальные методы — это класс алгоритмов, предназначенных для решения задач выпуклой оптимизации негладких функций. Основная идея таких методов заключается в замене исходной сложной задачи оптимизации на серию более простых подзадач, аппроксимируя новое решение к текущему. Другими словами, данные методы представляют собой обобщение проективных градиентных методов.

Метод проксимальной минимизации

Может быть полезен в тех случаях, когда трудно минимизировать функцию потерь , но легко (или хотя бы проще) минимизировать её регуляризованную версию, то есть плюс квадратичная величина. Данный алгоритм имеет множество разных интерпретаций в зависимости от целей использования. Наиболее популярными являются следующие:

1) Регуляризация Тихонова позволяет находиться $w_{k+1}$ недалеко от и ускоряет сходимость, особенно при использовании метода сопряжённого градиента. Это обусловлено тем, что наличие регуляризации обеспечивает сильную выпуклость задачи:

$w_{k+1} = \text{argmin} \left\{ f(w) + \frac{1}{2\alpha_k} \| w - w_k \|^2 \right\} = prox_{\alpha_k f}(w_k)$

2) Градиентный поток обеспечивает сходимость в точке минимума из любой начальной точки при $t \to \infty$ :

$\frac {d}{dt} w(t) = - \nabla f(w(t))$

Решив данное дифференциальное уравнение с помощью прямой схемы Эйлера, получим:

$w_{k+1} = w_k - \alpha \nabla f(w_k)$

Однако такое решение менее стабильно, чем с использованием обратной схемы Эйлера. Тогда задача приобретает вид:

$\frac{w_{k+1} - w_k}{\alpha} = - \nabla f(w_{k+1})$

Поскольку уравнение представлено в неявном виде, а его левая часть является по сути градиентом функции g(w^*) в точке $w_{k+1}$ , тогда для выпуклого случая:

$\nabla (f(w^*) + g(w^*))(w_{k+1}) = 0 \Rightarrow \\ \Rightarrow w_{k+1} = \text{argmin} \left\{ f(w^*) + \frac{1}{2\alpha_k} \| w^* - w_k \|^2 \right\} = prox_{\alpha_k f}(w_k)$

Также стоит отметить, что идея градиентного потока может быть обобщена на случаи, когда не дифференцируется с помощью субградиентного дифференциального включения:

$\frac {d}{dt} w(t) \in - \partial f(w(t))$

3) Итеративное уточнение основано на идее асимптотически исчезающей регуляризации Тихонова и используется для решения линейных уравнений вида с использованием факторизации Холецкого для $X + \frac{1}{\alpha} I$ .

Рассмотрим задачу минимизации квадратичной функции:

$f(w) = \frac{1}{2} w^T X w - y^T w$

где $X \in S_+^n$ (множество симметричных положительных полуопределённых x матриц). Такая задача эквивалентна решению системы линейных уравнений Xw = y . Когда несингулярно, то единственным решением является $w = X^{-1} y$ . Такую же проблему можно заметить в методе наименьших квадратов. Проксимальный оператор для при w_k можно выразить аналитически:

$prox_{\alpha_k f}(w_k) = \text{argmin} \left\{ \frac{1}{2} w^T X w - y^T w + \frac{1}{2} \| w - w_k \|^2 \right\} = \\ = \left(X + \frac{1}{\alpha} I \right)^{-1} \left(y + \frac{1}{\alpha} w_k \right)$

Переписав данное выражение, получим:

$w_{k+1} = w_k + \left(X + \frac{1}{\alpha} I \right)^{-1} (y - X w_k)$

Проксимальный градиентный метод (PGM)

Применяется для решения проблемы вида $min \rightarrow f(w) + g(w)$ , где является гладкой (дифференцируемой), а негладкой функцией, для которой существует быстрый проксимальный оператор. Тогда, выполнив градиентный шаг по и проксимальный по , получим итеративный процесс вида:

$w_{k+1} = prox_{\alpha_k g} (w_k - \alpha_k \nabla f(w_k))$

Ещё такой процесс называется forward-backward splitting, где forward относится к шагу градиента, а backward — к проксимальному шагу.

PGM также имеет различные интерпретации:

1) Majorization-minimization используется для решения проблемы вида:

$min \rightarrow q_{\alpha}(w, w_k) = \frac{1}{2} \| w - (w_k \ - \alpha_k \nabla f(w_k)) \|^2 + \alpha g(w)$

А процесс обновления $w_{k+1}$ выглядит следующим образом:

$w_{k+1} = \text{argmin} \ q_{\alpha}(w, w_k)$

2) Fixed point iteration основано на идее, что существует фиксированная точка , которая является решением $min \rightarrow f(w) + g(w)$ лишь в том случае, когда она является точкой оператора forward-backward:

$(I + \alpha \partial g)^{-1} (I - \alpha \nabla f)$

Тогда решение приобретает вид:

$w^* = (I + \alpha \partial g)^{-1} (I - \alpha \nabla f) (w^*) = prox_{\alpha g} (w^* - \alpha_k \nabla f(w^*))$

3) Forward-backward интегрирование градиентного потока представляет собой метод численного интегрирования дифференциального уравнения градиентного потока, в котором используется прямой Шаг Эйлера для дифференцируемой части и обратный шаг Эйлера для возможно недифференцируемой части . Если исходить предположения, что является также дифференцируемой, то систему градиентного потока можно представить в виде:

$\frac {d}{dt} w(t) = - \nabla f(w(t)) - \nabla g(w(t))$

Тогда получим процесс обновления, называемый forward-backward splitting:

$w_{k+1} = (I + \alpha \nabla g)^{-1} (I - \alpha \nabla f) w_k$

Также стоит добавить, что существует вариант ускоренного проксимального градиентного метода, который включает в себя этап экстраполяции:

$z_{k+1} = w_k + p_k (w_k - w_{k-1}) \\ w_{k+1} = prox_{\alpha_k g} (z_{k+1} - \alpha_k \nabla f(z_{k+1}))$

где p_k — параметр экстраполяции, а одна из самых простых схем его расчёта выглядит следующим образом:

$p_k = \frac {k}{k + 3}$

ISTA (Iterative Shrinkage(Soft)-Thresholding Algorithm)

Данный метод можно рассматривать как обновление проксимального градиента, применяемое к L1-регуляризованной задаче наименьших квадратов:

$\|Xw - y\|^2 + \lambda \|w\| \rightarrow min$

Тогда прокс-оператор для l1-нормы будет выглядеть следующим образом:

$prox_{\lambda \alpha} (w) = argmin \left\{\frac{1}{2 \alpha} \|w - w^* \|^2 + \lambda \|w^* \| \right\} = \\ = argmin \left\{\sum_{i=1}^d \left[ \frac{1}{2 \alpha} (w_i - w_i^*) + \lambda |w_i^*| \right] \right\} = T_{\lambda \alpha} (w)$

где $T_{\lambda \alpha} (w)$ — оператор с плавным установлением порога, который для одномерной задачи является решением со следующим правилом:

$T_{\lambda \alpha} (w_i) = \begin{cases} w_i - \lambda \alpha, \ \ w_i \geq \lambda \alpha \\ 0, \ \ \ \ \ \ \ \ \ \ \ \ \ |w_i| \leq \lambda \alpha \\ w_i + \lambda \alpha, \ \ w_i \leq - \lambda \alpha \\ \end{cases}$

Отсюда получим градиентный шаг обновления:

$w_{k+1} = T_{\lambda \alpha_k} (w_k - \alpha_k X^T (Xw_k- y))$

Как можно заметить, данный метод позволяет получить разреженную модель, поскольку часть координат будет зануляться в случае $|w_i| \leq \alpha$ , что особенно полезно в задачах компьютерного зрения и обработки сигналов. Однако ISTA в чистом виде имеет низкую скорости сходимости, поэтому на практике используются его различные модификации. Например, FISTA (от слова Fast), основанное на ускорении Нестерова, где используется дополнительный шаг экстраполяции:

$t_{k+1} = \frac{1 + \sqrt{1 + 4t_k^2}}{2} \\ z_{k+1} = w_k + \frac{t_k - 1}{t_{k+1}} (w_k - w_{k-1}) \\ w_{k+1} = prox_{\lambda \alpha} (z_{k+1}) = T_{\lambda \alpha_k}(z_{k+1} - \alpha_k \nabla f(z_{k+1}))$

Такой подход позволил достичь скорости сходимости O(1/k^2) вместо O(1/k) как в ISTA. К слову, на сегодняшний день FISTA также имеет ряд модификаций, делающих его работу более стабильной и быстрой в ряде случаев.

Методы второго порядка

Методы оптимизации второго порядка — это алгоритмы, которые используют информацию о вторых производных (гессиане) функции потерь для более быстрого и точного нахождения точек минимума с очень неровным и сложным ландшафтом потерь.

Вторая производная характеризует скорость изменения градиента функции потерь за счёт измерения её кривизны, то есть позволяет оценить ожидаемое улучшение от шага градиентного спуска. На примере квадратичной функции это будет означать следующее:

1) Если кривизна отрицательная (отрицательное значение второй производной), то функция потерь снижается быстрее, чем ожидалось на основе прогноза градиента. Это может привести к более быстрому достижению минимума, но также увеличивает риск его перепрыгнуть при использовании больших шагов.
2) Если кривизна нулевая (нулевое значение второй производной), то градиент точно указывает на изменение функции потерь. В этом случае, градиентный спуск будет эффективно минимизировать функцию потерь, поскольку нет ни ускорения, ни замедления в изменении её значений.
3) Если кривизна положительная (положительное значение второй производной), то функция потерь снижается медленнее, чем ожидалось на основе прогноза градиента. Это говорит о том, что при использовании слишком больших шагов можно не только замедлить процесс сходимости, но и увеличить значение функции потерь, отдаляясь от оптимального решения.

Метод Ньютона

Использует разложение в ряд Тейлора второго порядка для приближения функции f(w) в окрестностях точки :

$f(w + t) \approx f(w) + \nabla f(w)t + \frac{1}{2} \nabla ^ 2 (w) t^2$

Приравняв к нулю $\nabla f(w + t)$ , получим оптимальное решение, то есть направление спуска:

$0 = \frac{d f(w + t)}{dt} = \nabla f(w) + \nabla ^ 2 f(w) \Rightarrow \\ \Rightarrow t = - \frac {\nabla f(w)}{\nabla ^ 2 f(w)} = - H^{-1}(w) \nabla f(w)$

Тогда получим итеративный процесс обновления параметров следующего вида:

$w_{k+1} = w_k + t_k = w_k - \alpha_k H^{-1} (w_k) \nabla f (w_k)$

Если шаг $\alpha_k = 1$ , то это классический метод Ньютона, а при другом размере шага $\alpha_k \in (0, 1)$ получим дэмпированный (damped) метод Ньютона.

Такой подход обеспечивает квадратичную скорость сходимости в окрестностях оптимума за счёт более правильного оценивания кривизны линий уровня функции потерь, что делает его также устойчивым к плохо обусловленным задачам (задачам, в которых небольшое изменение в начальных данных может привести к значительным ошибкам в решении).

Однако метод Ньютона применим только в случае положительно определённой матрицы , иначе говоря, если не все собственные значения гессиана положительны вблизи седловой точки, то обновление параметров может произойти в неверном направлении. Одно из самых простых решений данной проблемы заключается в регуляризации гессиана через прибавление константы $\lambda$ :

$w_{k+1} = w_k + t = w_k - \alpha_k [H^{-1} (w_k) + \lambda I] \nabla f (w_k)$

Помимо этого, у метода Ньютона есть ещё 2 серьёзных недостатка, которые делают его неприменимым к нейронным сетям с большим количеством параметров:

1) Чувствительность к выбору начальной точки. Квадратичная скорость сходимости обеспечивается лишь в близи оптимума, поэтому если начальная точка расположена далеко от него, то алгоритм будет сходиться медленней и может не сойтись вообще. Отсюда следует необходимость в подборе шага $\alpha_k$ как и в случае с градиентным спуском. Частично данную проблему можно решить также с применением различных видов регуляризацией, например, такой как кубической.
2) Высокая требовательность к вычислительным ресурсам. Поскольку число элементов гессиана равно квадрату числа параметров , затраты памяти для их хранения составят , а вычислительная сложность алгоритма будет .

Далее будут рассмотрены альтернативы, использующие некоторые преимущества метода Ньютона, но при этом не требующие таких больших вычислительных затрат.

Метод сопряжённых градиентов

В отличие от метода наискорейшего спуска, в данном методе используется спуск в сопряжённых направлениях. Это означает, что в отличие от метода наискорейшего спуска, где направление спуска всегда совпадает с антиградиентом текущей точки и может привести к зигзагообразным траекториям, в данном случае направления спуска выбираются таким образом, чтобы они были сопряжёнными (ортогональными) относительно гессиана функции потерь, то есть текущее направление выбирается с учётом предыдущего значения:

$d_k = \nabla f(w) + \beta_k d_{t-1}$

Направления d_k и $d_{k-1}$ являются сопряжёнными, если выполняется условие $d_k^T H d_{k-1}=0$ , а коэффициент $\beta_k$ определяет какую часть направления $d_{k-1}$ следует прибавить к текущему направлению поиска.

Найти сопряжённые направления можно, просто получив собственные вектора для $\beta_k$ , однако это вычислительно неэффективно, поэтому для расчёта $\beta_k$ обычно используются следующие методы:

1) Метод Флетчера-Ривса:

$\beta_k = \frac{\nabla f(w_k)^T \nabla f(w_k)}{\nabla f(w_{k-1})^T \nabla f(w_{k-1})}$

2) Метод Полака-Рибьера:

$\beta_k = \frac{(\nabla f(w_k) - \nabla f(w_{k-1}))^T \nabla f(w_k)}{\nabla f(w_{k-1})^T \nabla f(w_{k-1})}$

Отсюда следует, что для квадратичной поверхности сопряжённость направлений гарантирует поиск минимума не более чем за поисков в -мерном пространстве. Если же функция потерь выходит за рамки простой квадратичной формы и становится более сложной, то не всегда возможно обеспечить сохранение минимума вдоль ранее определённых сопряжённых направлений. Поэтому в нелинейном случае сопряжённых градиентов периодически выполняется процедура рестарта, когда алгоритм заново начинает поиск вдоль направления неизменённого градиента, что в итоге позволяет лучше адаптироваться к изменениям в ландшафте функции потерь и более эффективно находить точки минимума.

BFGS (Broyden–Fletcher–Goldfarb–Shanno)

Является одним из самых известных квазиньютоновских методов и основан на аппроксимации обратного гессиана матрицей , которая итеративно уточняется в ходе обновлений низкого ранга. Обозначив приближение гессиана симметричной положительно определённой матрицей B_k размера $d \ \text{x} \ d$ , получим функцию потерь аналогичную в методе Ньютона:

$m(w_{k+1}) \approx f(w_{k+1}) + \nabla f(w_{k+1})t + \frac{1}{2} \nabla ^ 2 (w_{k+1}) t^2$

Основываясь на информации из последнего шага, $B_{k+1}$ должен удовлетворять условию, при котором градиент $m(w_{k+1})$ соответствует градиенту на двух последних итерациях w_k и $w_{k+1}$ :

$\nabla m(w_k - w_{k+1}) = \nabla m(- \alpha_k d_k) = \nabla f(w_{k+1}) - B_{k+1} \alpha_k d_k = \nabla f(w_k)$

Отсюда получаем уравнение секущей, которое ещё известно как квазиньютоновское условие:

$B_{k+1} \alpha_k d_k = \nabla f(w_{k+1}) - \nabla f(w_k)$

Перепишем данное уравнение в более удобной форме, сделав несколько упрощений:

$B_{k+1} s_k = y_k \\ s_k = (w_{k+1} - w_k) = \alpha_k d_k \\ y_k = \nabla f(w_{k+1}) - \nabla f(w_k)$

Согласно данному уравнению, матрица $B_{k+1}$ может преобразовать s_k в y_k только в случае, если они будут удовлетворять условию кривизны:

Когда выполняется данное условие, уравнение секущей всегда имеет решение, а для однозначного определения вводится дополнительное условие, что среди всех симметричных матриц, удовлетворяющих уравнению секущей, $B_{k+1}$ будет расположено наиболее близко к текущей матрице B_k . Другими словами, мы получим задачу вида:

$min \rightarrow \|B - B_k \|$

Которую можно решить достаточно просто с использованием взвешенной нормы Фробениуса:

$\|A \|_W = \|W^{1/2} A W^{1/2} \|_F$

Где $\|\cdot \|_F$ определяется через $\|C \|^2_F = \sum_{i=1}^n \sum_{j=1}^n c^2_{ij}$ , а матрица весов может быть выбрана в качестве любой при выполнении условия W y_k = s_k . Определим как обратный усреднённый гессиан $\overline G_k^{-1}$ , где:

$\overline G_k = \int_{0}^{1} \nabla^2 f(w_k + \alpha_k d_k) d \lambda$

С помощью теоремы Тейлора можно показать, что:

$y_k = \overline G_k \alpha_k d_k = \overline G_k s_k$

При таком выборе матрицы взвешенная норма Фробениуса является безразмерной, что является желательным свойством, поскольку позволяет избежать зависимости от единиц измерения при решении задачи минимизации. Тогда получим решение, которое называется методом DFP (Davidon-Fletcher-Powell):

$B_{k+1} = (I - \frac{y_k s_k^T}{y_k^T s_k}) B_K (I - \frac{s_k y_k^T}{y_k^T s_k}) + \frac{y_k y_k^T}{y_k^T s_k}$

Поскольку в квазиньютоновских методах нас больше интересует обратный гессиан, перепишем данную формулу в более удобном виде. Для этого определим $M=B_k^{-1}$ и воспользуемся обобщением формулы Шермана–Моррисона–Вудбери:

$B^{-1} = A^{-1} - A^{-1} U (I + V^T A^{-1} U) V^T A^{-1}$

В таком случае решение DFP приобретает следующий вид:

$M_{k+1} = M_k - \frac{M_k y_k y_k^T M_k}{y_k^T M_k y_k} + \frac{s_k s_k^T}{y_k^T s_k}$

В этом и заключается фундаментальная идея квазиньютоновского обновления: вместо того, чтобы пересчитывать приближённые гессианы (или обратные гессианы) с нуля на каждой итерации, мы применяем простую модификацию, которая объединяет самую последнюю наблюдаемую информацию о функции потерь с информацией из текущего приближения гессиана.

Однако формулу обновления из алгоритма DFP можно сделать ещё более эффективной, проделав аналогичные процедуры с M_k вместо B_k . В итоге получим обновление BFGS:

$M_{k+1} = (I - \frac{s_k y_k^T}{y_k^T s_k}) M_k (I - \frac{y_k s_k^T}{y_k^T s_k}) + \frac{s_k s_k^T}{y_k^T s_k}$

Мы также можем вывести версию алгоритма BFGS, которая работает с аппроксимацией гессиана B_k , а не M_k , если снова воспользуемся обобщением формулы Шермана–Моррисона–Вудбери:

$B_{k+1} = B_k - \frac{B_k s_k s_k^T B_k}{s_k^T B_k s_k} + \frac{y_k y_k^T}{y_k^T s_k}$

Тогда направление спуска будет выглядеть следующим образом:

$d_k = - M_k \nabla f(w_k)$

А параметры будут обновляться по следующему правилу:

$w_{k+1} = w_k + \alpha_k d_k$

Подобно методу сопряжённых градиентов, в алгоритме BFGS создаётся серия линейных поисков на основе информации о кривизне функции, однако важное отличие заключается в том, что в данном случае достижение точки, максимально приближённой к истинному минимуму вдоль заданного направления, не так критично. Это делает данный алгоритм более эффективным, поскольку не требуется тратить дополнительное время на уточнение результатов каждого линейного поиска, однако для хранения матрицы требуется O(d^2) памяти из-за чего данный алгоритм также малопригоден для моделей с большим количеством параметров.

SR1 (Symmetric Rank-1)

Представляет собой более простое обновление ранга 1, которое сохраняет симметрию матрицы и удовлетворяет уравнению секущей, но при этом исчезают гарантии, что обновлённая матрица B_k (или M_k ) будет положительно определённой. Тем не менее, данный метод может быть полезен, когда стандартные предположения BFGS не выполняются или когда необходимо использовать методы определения доверительной области.

Симметричное обновление ранга 1, которое также известно как формула Бройдена, имеет общий вид:

$B_{k+1} = B_k + \sigma v v^T$

Где $\sigma \in \{-1, 1 \}$ , а также выбирается вместе с $B_{k+1}$ таким образом, чтобы удовлетворять уравнению секущей. Подставляя обновление выше в данное уравнение, получим:

$y_k = B_k s_k + [\sigma v v^T] s_k$

Поскольку член в скобках является скалярным, должно быть кратно y_k - B_k s_k для некоторого скалярного значения $\delta$ :

$y_k - B_k s_k = \sigma \delta^2 [s_k^T (y_k - B_k s_k)] (y_k - B_k s_k)$

Данное уравнение выполняется только в том случае, когда:

$\sigma = sign [s_k^T (y_k - B_k s_k)] \\ \delta = \pm |s_k^T (y_k - B_k s_k)|^{-1/2}$

Следовательно, формула обновления для B_k приобретает следующий вид:

$B_{k+1} = B_k + \frac{(y_k - B_k s_k) (y_k - B_k s_k)^T}{(y_k - B_k s_k)^T s_k}$

Применив обобщение формулы Шермана–Моррисона–Вудбери, получим соответствующее обновление для обратной аппроксимации гессиана M_k :

$M_{k+1} = M_k + \frac{(s_k - M_k y_k) (s_k - M_k y_k)^T}{(s_k - M_k y_k)^T y_k}$

Не смотря на менее стабильную работу в сравнении с DFP и BFGS, преимущество SR1 заключается в создании неопределённых аппроксимаций гессиана, что позволяет получить больше информации о локальной кривизне функции потерь и лучше исследовать пространство для поиска направлений, которые могут привести к лучшему решению.

L-BFGS (Limited-memory BFGS)

Данная модификация использует только последние (обычно от 3 до 20) пар векторов $\{s_i, y_i \}$ и начальное приближение M_0 для аппроксимации M_k , что позволяет иметь линейные затраты O(md) не только на память, но и на итерацию. Чтобы лучше разобраться как это происходит, сначала перепишем формулы обновления BFGS в упрощённом виде:

$w_{k+1} = w_k + \alpha_k M_k \nabla f (w_k) \\ M_{k+1} = V_k^T M_k V_k + \rho_k s_k s_k^T \\ V_k = I - \rho_k y_k s_k^T, \ \rho_k = \frac{1}{y_k^T s_k}$

Тогда приближение M_k можно представить в следующем виде:

$\begin{align} M_k &= (V_{k-1}^{T} ... V_{k-m}^{T}) M_0 (V_{k-m} ... V_{k-1}) + \\ &+ \rho_{k-m} (V_{k-1}^T ... V_{k-m+1}^{T}) s_{k-m} s_{k-m}^T (V_{k-m+1} ... V_{k-1}) + \\ &+ \ ... \\ &+ \rho_{k-1} s_{k-1} s_{k-1}^T \end{align}$

Из этого выражения можно вывести рекурсивный алгоритм для эффективного вычисления произведения $M_k \nabla f(w_k)$ , который называется L-BFGS two-loop recursion:

$\begin{align*} & q \leftarrow \nabla f(w_k) \\ & \textbf{for } i \ = \ k - 1, \ k - 2, ..., \ k - m \\ & \hspace{1.3cm} \alpha_i \leftarrow \rho_i s_i^T q \\ & \hspace{1.3cm} q \leftarrow q - \alpha_i y_i \\ \\ & r \leftarrow M_0 q \\ & \textbf{for } i \ = \ k - m, \ k - m + 1, ..., \ k - 1 \\ & \hspace{1.3cm} \beta \leftarrow \rho_i y_i^T r \\ & \hspace{1.3cm} r \leftarrow r + s_i (\alpha_i - \beta) \\ \\ & M_k \nabla f(w_k) = r \end{align*}$

Преимущество данной рекурсии заключается в том, что умножение на исходную матрицу M_0 изолировано от остальных вычислений, что позволяет свободно выбирать эту матрицу и изменять её между итерациями. Методом выбора M_0 , показавшим себя хорошо на практике, является установка $M_0 = \gamma_k I$ , где:

$\gamma_k = \frac{s_{k-1}^T y_{k-1}}{y_{k-1}^T y_{k-1}}$

В данном случае $\gamma_k$ позволяет произвести оценку размера истинного гессиана по последнему направлению поиска. Это гарантирует хорошую масштабируемость направления поиска d_k , в результате чего на большинстве итераций $\alpha_k = 1$ . Теперь можно сделать формальное определение алгоритма L-BFGS следующим образом:

$\begin{align*} & \text{Choose starting point } w_0 \\ & m > 0, \ k \leftarrow 0 \\ & \textbf{repeat until convergence} \\ & \hspace{1.3cm} M_0 = \gamma_k I \\ & \hspace{1.3cm} d_k = - M_k \nabla f(w_k) \hspace{0.4cm} \text{from algorithm above} \\ \\ & \hspace{1.3cm} w_{k+1} = w_k + \alpha_k d_k, \hspace{0.3cm} \text{where} \ \alpha_k \ \text{satisfies the Wolfe's conditions} \\ & \hspace{1.3cm} r \leftarrow r + s_i (\alpha_i - \beta) \\ & \hspace{1.3cm} \textbf{if } k > m: \\ & \hspace{2.6cm} \text{Discard the vector pair } \{s_{k-m}, y_{k-m} \} \ \text{from storage} \\ & \hspace{1.3cm} s_k \leftarrow w_{k+1} - w_k \\ & \hspace{1.3cm} y_k = \nabla f(w_{k+1}) - \nabla f(w_k) \\ & \hspace{1.3cm} k \leftarrow k + 1 \end{align*}$

Shampoo

Вышеописанные методы всё ещё уступают в скорости и потреблении памяти обычным градиентным методам, особенно при обучении больших глубоких нейросетей. Отчасти это связано с тем, что они требуют точного, а не стохастического вычисления градиентов. В идеале нам хотелось бы иметь такой алгоритм, который совмещает в себе точность алгоритмов второго порядка и скорость алгоритмов первого порядка. В попытке приблизиться к этой цели появился алгоритм Shampoo, который применяется для стохастической оптимизации не только в матричных, но и в тензорных пространствах. Рассмотрим его основную идею.

Начнём с того, что данный алгоритм относится к так называемым предобусловленным методам (в контексте машинного обучения предобуславливание можно рассматривать как процесс изменения условий задачи таким образом, чтобы алгоритмы могли работать с ней более точно и эффективно). Вместо использования обратного гессиана явным образом в Shampoo используется произведение градиентов для формирования предобусловленной матрицы, которая выполняет роль, схожую с гессианом, храня информацию о кривизне поверхности функции потерь. По сути, данный алгоритм представляет собой многомерный аналог AdaGrad, а для матричного случая его принцип работы выглядит следующим образом:

На каждой итерации из матриц L_t размера x и R_t размера x , содержащих информацию о втором моменте из накопленных градиентов, формируются две предобусловленные матрицы, которые потом умножаются на градиентную матрицу слева направо соответственно. Каждая из предобусловленных матриц работает только с одним измерением градиента, сокращаясь по остальным, что позволяет неплохо ускорить процесс обучения. Объём пространства, который Shampoo использует в данном случае, составляет O(m^2 + n^2) вместо O(m^2 n^2) в сравнении с полноматричными методами. Более того, поскольку предобуславливание включает в себя обращение матрицы (и чаще всего спектральное разложение), объём вычислений для построения левого и правого предобуславливателей также сокращается с O(m^3 n^3) до O(m^3 + n^3) .

Однако shampoo всё ещё требует больших вычислительных затрат, что особенно выделяется при работе с полносвязными и эмбэддинг слоями. Основная сложность возникает при вычислении p-х корней матриц и $R_t^{-1/4}$ , которые были реализованы с использованием спектрального разложения (SVD), что может занимать много времени. Поэтому для ускоренной работы Shampoo было разработано несколько улучшений:

1) Вычисление предобуславливателей раз в несколько сотен шагов. Поскольку в большинстве случаев структура функции потерь не изменяется существенно с каждым шагом, то и особого эффекта на снижение точности произойти не должно.
2) Использование эффективных итерационных методов для вычисления p-х корней вместо SVD, например, применение модификаций на основе метода Шура-Ньютона.
3) Использование одного из предобуславливателей $L_t^{-1/2p}$ или $R_t^{-1/2q}$ , где .
4) Применение параллельного обучения на GPU.

Стоит отметить, что данные улучшения без проблем можно использовать для Shampoo в его общей форме, который применим к тензорам произвольной размерности. В фреймворках глубокого обучения реализована именно тензорная форма данного алгоритма, а его принцип работы выглядит следующим образом:

Проследив историю развития методов второго порядка, становится очевидно, что с появлением таких алгоритмов как Shampoo, их применение становится всё более привлекательным с практической точки зрения. На мой скромный взгляд, в будущем их популярность будет расти и они будут играть значимую роль не только в глубоком обучении, но и в других областях инженерии и науки.

Дополнительные источники

Статьи:

«Adam: A Method for Stochastic Optimization», Diederik P. Kingma, Jimmy Ba;
«Decoupled Weight Decay Regularization», Ilya Loshchilov, Frank Hutter;
«Incorporating Nesterov Momentum into Adam», Timothy Dozat;
«Adaptive Methods for Nonconvex Optimization», Manzil Zaheer, Sashank J. Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar;
«Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models», Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan;
«On the Convergence of Adam and Beyond», Sashank J. Reddi, Satyen Kale, Sanjiv Kumar;
«On the Convergence Proof of AMSGrad and a New Version», Tran Thi Phuong, Le Trieu Phong;
«SGDR: Stochastic Gradient Descent with Warm Restarts», Ilya Loshchilov, Frank Hutter;
«Optimization for deep learning: theory and algorithms», Ruoyu Sun;
«Large Batch Training of Convolutional Networks», Yang You, Igor Gitman, Boris Ginsburg;
«Large Batch Optimization for Deep Learning: Training BERT in 76 minutes», Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh;
«Proximal Algorithms», Neal Parikh, Stephen Boyd;
«A Fast Iterative Shrinkage-Thresholding Algorithm», for Linear Inverse Problems, Amir Beck, and Marc Teboulle;
«Improving Fast Iterative Shrinkage-Thresholding Algorithm: Faster, Smarter and Greedier», Jingwei Liang, Tao Luo, Carola-Bibiane Schönlieb;
«Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice», Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli;
«Numerical Optimization», Jorge Nocedal, Stephen J. Wright;
«A regularized limited-memory BFGS method for unconstrained minimization problems», Shinji SUGIMOTO and Nobuo YAMASHITA;
«Shampoo: Preconditioned Stochastic Tensor Optimization», Vineet Gupta, Tomer Koren, Yoram Singer;
«Scalable Second Order Optimization for Deep Learning», Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer;
«A Schur-Newton method for the matrix p'th root and its inverse», C.-H. Guo and N. J. Higham.

Видео:

Обзор и реализация популярных ML-алгоритмов 🡆

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10 мая 2024 г.

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After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3
23.01.25 14:20 nellymargaret

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23.01.25 14:20 nellymargaret

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26.01.25 03:54 [email protected]

Losing access to my crypto wallet account was one of the most stressful experiences ever. After spending countless hours building up my portfolio, I suddenly found myself locked out of my account without access. To make matters worse, the email address I had linked to my wallet was no longer active. When I tried reaching out, I received an error message stating that the domain was no longer in use, leaving me in complete confusion and panic. It was as though everything I had worked so hard for was gone, and I had no idea how to get it back. The hardest part wasn’t just the loss of access it was the feeling of helplessness. Crypto transactions are often irreversible, and since my wallet held significant investments, the thought that my hard-earned money could be lost forever was incredibly disheartening. I spent hours scouring forums and searching for ways to recover my funds, but most of the advice seemed either too vague or too complicated to be of any real help. With no support from the wallet provider and my email account out of reach, I was left feeling like I had no way to fix the situation.That’s when I found out about Trust Geeks Hack Expert . I was hesitant at first, but after reading about their expertise in recovering lost crypto wallets, I decided to give them a try. I reached out to their team, and from the very beginning, they were professional, understanding, and empathetic to my situation. They quickly assured me that there was a way to recover my wallet, and they got to work immediately.Thanks to Trust Geeks Hack Expert , my wallet and funds were recovered, and I couldn’t be more grateful. The process wasn’t easy, but their team guided me through each step with precision and care. The sense of relief I felt when I regained access to my crypto wallet and saw my funds safely back in place was indescribable. If you find yourself in a similar situation, I highly recommend reaching out to Trust Geeks Hack Expert. contact Them through EMAIL: [email protected] + WEBSITE. HTTPS://TRUSTGEEKSHACKEXPERT.COM + TELE GRAM: TRUSTGEEKSHACKEXPERT
28.01.25 21:48 [email protected]

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02.02.25 20:53 Michael9090

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05.02.25 00:04 Jannetjeersten

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06.02.25 19:42 Marta Golomb

My name is Marta, and I’m sharing my experience in the hope that it might help others avoid a similar scam. A few weeks ago, I received an email that appeared to be from the "Department of Health and Human Services (DHS)." It claimed I was eligible for a $72,000 grant debit card, which seemed like an incredible opportunity. At first, I was skeptical, but the email looked so professional and convincing that I thought it might be real. The email instructed me to click on a link to claim the grant, and unfortunately, I followed through. I filled out some personal details, and then, unexpectedly, I was told I needed to pay a "processing fee" to finalize the grant. I was hesitant, but the urgency of the message pushed me to make the payment, believing it was a necessary step to receive the funds. Once the payment was made, things quickly went downhill. The website became unreachable, and I couldn’t get in touch with anyone from the supposed DHS. It soon became clear that I had been scammed. The email, which seemed so legitimate, had been a clever trick to steal my money.Devastated and unsure of what to do, I began searching for ways to recover my lost funds. That’s when I found Tech Cyber Force Recovery, a team of experts who specialize in tracing stolen money and assisting victims of online fraud. They were incredibly reassuring and quickly got to work on my case. After several days of investigation, they managed to track down the scammers and recover my funds. I can’t express how grateful I am for their help. Without Tech Cyber Force Recovery, I don’t know what I would have done. This experience has taught me a valuable lesson: online scams are more common than I realized, and the scammers behind them are incredibly skilled. They prey on people’s trust, making it easy to fall for their tricks. HOW CAN I RECOVER MY LOST BTC,USDT =Telegram= +1 561-726-36-97 =WhatsApp= +1 561-726-36-97
08.02.25 05:45 [email protected]

I'm incredibly grateful that I did enough research to recover my stolen cryptocurrency. When I first fell victim to a scam, I felt hopeless and lost, unsure if I'd ever see my funds again. A few months ago, I was approached by someone on Telegram who claimed to have a lucrative investment opportunity in cryptocurrencies. They promised huge returns and played on my emotions, making it seem like a can't-miss chance. I was so eager to make my money grow that I didn't fully vet the situation, and unfortunately, I ended up falling for the scam. They guided me to invest a significant amount of money, and soon after, I realized I had been duped. The scammers blocked me, and my funds were gone. I felt devastated. All of my savings had been wiped out in what seemed like an instant, and the feeling of being taken advantage of was crushing. I spent days researching how to recover my stolen cryptocurrency but found the process to be overwhelming and complicated. I was starting to lose hope when I came across Trust Geeks Hack Expert. At first, I was skeptical about reaching out to a cryptocurrency recovery company, but after reading testimonials and researching their reputation, I decided to give them a try. I contacted Trust Geeks Hack Expert Website: www://trustgeekshackexpert.com/, and I was immediately reassured by their professionalism and expertise. They took the time to listen to my situation, and they were honest about what could and could not be done. What stood out to me was their deep understanding of cryptocurrency fraud and the recovery process. They were able to track down the scammers and initiate the recovery of my stolen funds, step by step. Thanks to Trust Geeks Hack Expert, I was able to get back a significant portion of the cryptocurrency I had lost. Their team was responsive, transparent, and diligent in their efforts. I was kept informed throughout the entire process, and they made sure I felt supported every step of the way. I truly can't thank them enough for their dedication and for restoring my faith in the possibility of recovery after such a devastating loss. I will definitely recommend Trust Geeks Hack Expert to anyone who has fallen victim to a cryptocurrency scam. TeleGram: Trustgeekshackexpert & what's A p p +1 7 1 9 4 9 2 2 6 9 3
08.02.25 05:46 [email protected]

I'm incredibly grateful that I did enough research to recover my stolen cryptocurrency. When I first fell victim to a scam, I felt hopeless and lost, unsure if I'd ever see my funds again. A few months ago, I was approached by someone on Telegram who claimed to have a lucrative investment opportunity in cryptocurrencies. They promised huge returns and played on my emotions, making it seem like a can't-miss chance. I was so eager to make my money grow that I didn't fully vet the situation, and unfortunately, I ended up falling for the scam. They guided me to invest a significant amount of money, and soon after, I realized I had been duped. The scammers blocked me, and my funds were gone. I felt devastated. All of my savings had been wiped out in what seemed like an instant, and the feeling of being taken advantage of was crushing. I spent days researching how to recover my stolen cryptocurrency but found the process to be overwhelming and complicated. I was starting to lose hope when I came across Trust Geeks Hack Expert. At first, I was skeptical about reaching out to a cryptocurrency recovery company, but after reading testimonials and researching their reputation, I decided to give them a try. I contacted Trust Geeks Hack Expert Website: www://trustgeekshackexpert.com/, and I was immediately reassured by their professionalism and expertise. They took the time to listen to my situation, and they were honest about what could and could not be done. What stood out to me was their deep understanding of cryptocurrency fraud and the recovery process. They were able to track down the scammers and initiate the recovery of my stolen funds, step by step. Thanks to Trust Geeks Hack Expert, I was able to get back a significant portion of the cryptocurrency I had lost. Their team was responsive, transparent, and diligent in their efforts. I was kept informed throughout the entire process, and they made sure I felt supported every step of the way. I truly can't thank them enough for their dedication and for restoring my faith in the possibility of recovery after such a devastating loss. I will definitely recommend Trust Geeks Hack Expert to anyone who has fallen victim to a cryptocurrency scam. TeleGram: Trustgeekshackexpert & what's A p p +1 7 1 9 4 9 2 2 6 9 3
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11.02.25 04:24 heyemiliohutchinson

I invested substantially in Bitcoin, believing it would secure my future. For a while, things seemed to be going well. The market fluctuated, but I was confident my investment would pay off. But catastrophe struck without warning. I lost access to my Bitcoin holdings as a result of several technical issues and inadequate security measures. Every coin in my wallet suddenly disappeared, leaving me with an overpowering sense of grief. The emotional impact of this loss was far greater than I had imagined. I spiraled into despair, feeling as though my dreams of financial independence were crushed. I was on the verge of giving up when I came across Assets_Recovery_Crusader. Being willing to give them a chance, I had nothing left to lose. They listened to my narrative and took the time to comprehend the particulars of my circumstance, rather than treating me like a case number. They worked diligently, using their advanced recovery techniques and deep understanding of blockchain technology to track down my lost Bitcoin. Assets_Recovery_Crusader rebuilt my trust in the bitcoin space. The financial impact had a significant emotional toll, but I was able to get past it thanks to Assets_Recovery_Crusader’s proficiency and persistence. For proper talks, reach out to them via TELEGRAM : Assets_Recovery_Crusader EMAIL: [email protected]
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13.02.25 14:45 aoifewalsh130

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13.02.25 16:50 andytom798

I lost $210,000 worth of Bitcoin to a group of fake blockchain impostors on Red note, a Chinese app. They contacted me, pretending to be official blockchain support, and I was misled into believing they were legitimate. At the time, I had been saving up in Bitcoin, hoping to take advantage of the rising market. The scammers were convincing, and I made the mistake of trusting them with access to my blockchain wallet. To my shock and disbelief, they stole a total of $10,000 worth of Bitcoin from my wallet. It was devastating, as this amount represented all of my hard-earned savings. I was in utter disbelief, feeling foolish for falling for their deceptive tactics. I felt lost, as though everything I had worked towards was taken from me in an instant. Thankfully, my uncle suggested I reach out to an expert in cryptocurrency recovery. After doing some research online, I came across CYBERPOINT RECOVERY COMPANY. I was hesitant at first, but their positive reviews gave me some hope. I decided to contact them directly and explained my situation, including the amount I had lost and how the scammers had gained access to my account. To my relief, the team at CYBERPOINT RECOVERY responded quickly and assured me they could help. They launched a detailed recovery program, using advanced tools and techniques to trace the stolen Bitcoin. Within a matter of days, they successfully recovered my full $210,000 worth of Bitcoin, and they even identified the individuals behind the scam. Their expertise and professionalism made a huge difference, and I was incredibly grateful for their support. If you find yourself in a similar situation, I highly recommend reaching out to Cyber Constable Intelligence. They helped me recover my funds when I thought all hope was lost. Whether you’ve lost money to scammers or any other form of online fraud, they have the knowledge and resources to help you get your funds back. Don’t give up there are experts who can help you reclaim what you’ve lost. I’m sharing my story to hopefully guide others who are going through something similar. Here's Their Info Below ([email protected]) or W.H.A.T.S.A.P.P:+1.7.6.0.9.2.3.7.4.0.7
13.02.25 16:52 birenderkumar20101

I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected])
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14.02.25 02:50 Vladimir876

I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected]) or W.H.A.T.S.A.P.P:+1.7.6.0.9.2.3.7.4.0.7
14.02.25 02:56 christophadelbert3

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14.02.25 15:33 prelogmilivoj

I never imagined I would find myself in a situation where I was scammed out of such a significant amount of money, but it happened. I became a victim of a fake online donation project that cost me over $30,000. It all started innocently enough when I was searching for assistance after a devastating fire incident in California. While looking for support, I came across an advertisement that seemed to offer donations for fire victims. The ad appeared legitimate, and I reached out to the project manager to inquire about how to receive the donations. The manager was very convincing and insisted that in order to qualify for the donations, I needed to pay $30,000 upfront. In return, I was promised $1 million in donations. It sounded a bit too good to be true, but in my desperate situation, I made the mistake of believing it. The thought of receiving a substantial amount of help to rebuild after the fire clouded my judgment, and I went ahead and sent the money. However, after transferring the funds, the promised donations never arrived, and the manager disappeared. That’s when I realized I had been scammed. Feeling lost, helpless, and completely betrayed, I tried everything I could to contact the scammer, but all my efforts were in vain. Desperation led me to search for help online, hoping to find a way to recover my money and potentially track down the scammer. That’s when I stumbled upon several testimonies from others who had fallen victim to similar scams and had been helped by a company called Tech Cyber Force Recovery. I reached out to them immediately, providing all the details of the scam and the information I had gathered. To my immense relief, the experts at Tech Cyber Force Recovery acted swiftly. Within just 27 hours, they were able to locate the scammer and initiate the recovery process. Not only did they help me recover the $30,000 I had lost, but the most satisfying part was that the scammer was apprehended by local authorities in their region. Thanks to Tech Cyber Force Recovery, I was able to get my money back and hold the scammer accountable for their actions. I am incredibly grateful for their professionalism, expertise, and dedication to helping victims like me. If you have fallen victim to a scam or fraudulent activity, I highly recommend contacting Tech Cyber Force Recovery. They provide swift and efficient recovery assistance, and I can confidently say they made all the difference in my situation. ☎☎ 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ ☎☎ 📩 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ 📩
14.02.25 22:12 eunice49954

Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.
15.02.25 02:51 Michelle Lynn

Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com
15.02.25 02:51 Michelle Lynn

Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com, WhatsApp Info: 1 (252) 378-7611
16.02.25 01:01 Peter

I fell victim to a crypto scam and lost a significant amount of money. What are the most effective strategies to recover my funds? I've heard about legal actions, contacting authorities, and hiring recovery experts, but I'm not sure where to start. Can you provide some guidance on the best ways to recover money lost in a crypto scam? Well if this is you, [email protected] gat you covered get in touch and thank me later
16.02.25 20:06 eunice49954

Agent Lopez specializes in recovering stolen cryptocurrencies, especially Bitcoin/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.
18.02.25 19:35 donovancristina

Now, I’m that person sharing my success story on LinkedIn, telling others about the amazing team at TECH CYBER FORCE RECOVERY who literally saved my financial life. I’ve also become that guy who proudly shares advice like “Always back up your wallet, and if you don’t have TECH CYBER FORCE RECOVERY on speed dial.” So, a big thank you to TECH CYBER FORCE RECOVERY if I ever get a chance to meet the team, I might just offer to buy them a drink. They’ve earned it. FOR CRYPTO HIRING WEBSITE WWW://techcyberforcerecovery.com WHATSAPP : ⏩ wa.me/15617263697
18.02.25 22:13 keithphillip671

WhatsApp +44,7,4,9,3,5,1,3,3,8,5 Telegram @Franciscohack The day my son uncovered the truth—that the man I entrusted my hopes of wealth and companionship with through a cryptocurrency platform was a cunning scammer was the day my world crumbled. The staggering realization that I had been swindled out of 150,000.00 Euro worth of Bitcoin left me in a state of profound despair. As a 73-year-old grappling with loneliness, I had sought solace in what I believed to be a genuine connection, only to find deceit and betrayal. Countless sleepless nights were spent in tears, mourning not only the financial devastation but also the crushing blow to my trust. Attempts to verify the authenticity of our interactions were met with hostility, further deepening my sense of isolation. Through the loss it was my son who became my beacon of resilience. He took upon himself the arduous task of tracing the scam and seeking justice on my behalf. Through meticulous effort and determination, he unearthed {F R A N C I S C O H A C K}, renowned for their expertise in recovering funds lost to cryptocurrency scams. Entrusting them with screenshots and evidence of the fraudulent transactions, my son initiated the journey to reclaim what had been callously taken from me. {F R A N C I S C O H A C K} approached our plight with empathy and unwavering professionalism, immediately instilling a sense of confidence in their abilities. Despite my initial skepticism, their transparent communication and methodical approach reassured us throughout the recovery process. Regular updates on their progress and insights into their strategies provided much-needed reassurance and kept our hopes alive amid the uncertainty. Their commitment to transparency and client welfare was evident in every interaction, fostering a sense of partnership rather than mere service. Miraculously, in what felt like an eternity but was actually an impressively brief period, {F R A N C I S C O H A C K} delivered the astonishing news—I had recovered the entire 150,000.00 Euro worth of stolen Bitcoin. The flood of relief and disbelief was overwhelming, marking not just the restitution of financial losses but the restoration of my faith in justice. {F R A N C I S C O H A C K} proficiency in navigating the intricate landscape of blockchain technology and online fraud was nothing short of extraordinary. Their dedication to securing justice and restoring client confidence set them apart as more than just experts—they were steadfast allies in a fight against digital deceit. What resonated deeply with me {F R A N C I S C O H A C K} integrity and compassion. Despite the monumental recovery, they maintained transparency regarding their fees and ensured fairness in all dealings. Their proactive guidance on cybersecurity measures further underscored their commitment to safeguarding clients from future threats. It was clear that their mission extended beyond recovery—it encompassed education, prevention, and genuine advocacy for those ensnared by cyber fraud. ([email protected]) fills me with profound gratitude. They not only rescued my financial security but also provided invaluable emotional support during a time of profound vulnerability. To anyone navigating the aftermath of cryptocurrency fraud, I wholeheartedly endorse {F R A N C I S C O H A C K}. They epitomize integrity, expertise, and unwavering dedication to their clients' well-being. My experience with {F R A N C I S C O H A C K} transcended mere recovery—it was a transformative journey of resilience, restoration, and renewed hope in the face of adversity.
21.02.25 07:42 daniel231101

I never thought I would fall victim to a crypto scam until I was convinced of a crypto investment scam that saw me lose all my entire assets worth $487,000 to a crypto investment manager who convinced me I could earn more from my investment. I thought it was all gone for good but I kept looking for ways to get back my stolen crypto assets and finally came across Ethical Hack Recovery, a crypto recovery/spying company that has been very successful in the recovery of crypto for many other victims of crypto scams and people who lost access to their crypto. I’m truly grateful for their help as I was able to recover my stolen crypto assets and get my life back together. I highly recommend their services EMAIL ETHICALHACKERS009 AT @GMAIL DOT COM whatsapp +14106350697
21.02.25 21:38 eunice49954

Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.
22.02.25 18:01 benluna0991

Mark Zuckerberg. That’s the name I was introduced to when I first encountered the cryptocurrency mining platform, WHATS Invest. A person claiming to be Zuckerberg himself reached out to me, saying that he was personally backing the platform to help investors like me earn passive income. At first, I was skeptical—after all, how often do you get a direct connection to one of the world’s most famous tech entrepreneurs? But this individual seemed convincing and assured me that many people were already seeing substantial returns on their investments. He promised me a great opportunity to secure my financial future, so I decided to take the plunge and invest $10,000 into WHATS Invest. They told me that I could expect to see significant returns in just a few months, with payouts of at least $1,500 or more each month. I was excited, believing this would be my way out of financial struggles. However, as time passed, things didn’t go according to plan. Months went by, and I received very little communication. When I finally did receive a payout, it was nowhere near the $1,500 I was promised. Instead, I received just $200, barely 13% of what I had expected. Frustrated, I contacted the support team, but the responses were vague and unhelpful. No clear answers or solutions were offered, and my trust in the platform quickly started to erode. It became painfully clear that I wasn’t going to get anywhere with WHATS Invest, and I began to worry that my $10,000 might be lost for good. That's when I discovered Certified Recovery Services. Desperate to recover my funds, I decided to reach out to them for help. In just 24 hours, they worked tirelessly to recover the majority of my funds, successfully retrieving $8,500 85% of my initial investment. I couldn’t believe how quickly and efficiently they worked to get my money back. I’m extremely grateful for Certified Recovery Servicer's fast and professional service. Without them, I would have been left with a significant loss, and I would have had no idea how to move forward. If you find yourself in a similar situation with WHATS Invest or any other platform that isn’t delivering as promised, I highly recommend reaching out to Certified Recovery Services They were a lifesaver for me, helping me recover nearly all of my funds. It's reassuring to know that trustworthy services like this exist to help people when things go wrong. They also specialize in recovering money lost to online scams, so if you’ve fallen victim to such a scam, don’t hesitate to contact Certified Recovery Services they can help! Here's Their Info Below: WhatsApp: +1(740)258‑1417 mail: [email protected], [email protected] Website info; https://certifiedrecoveryservices.com
23.02.25 22:00 eunice49954

Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.
24.02.25 06:36 ANDREW DAVIS

RECOVER YOUR SCAMMED FUNDS AND CRYPTOCURRENCY VIA SPOTLIGHT RECOVERY Professional hackers at Spotlight Recovery provide services for compromised devices, accounts, and websites as well as for recovering stolen bitcoin and money from scams. They finish their work safely and quickly. Their order has been fulfilled since day one, and the victim will never be conscious of the outside entrance. Very few even attempt to give critical information, look into network security, or discreetly discuss personal issues. The Spotlight Recovery Crew helped me recover $264,000 that was stolen from my corporate bitcoin wallet, and I appreciate them giving me further details on the unidentified people. In the event that you have been defrauded of your hard-earned cash or bitcoins, contact SPOTLIGHT RECOVERY CREW at Contact: [email protected]
24.02.25 07:19 maggie4567

Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.
24.02.25 07:19 maggie4567

Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.
24.02.25 07:19 maggie4567

Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.
27.02.25 12:46 monikaguttmacher

Weddings are supposed to be magical, but the months leading up to mine were anything but. Already, wedding planning was a high-stress, sleep-deprived whirlwind: endless details to manage, from venue deposits and guest lists to dress fittings and vendor contracts. But nothing-and I mean, nothing-compared to the panic that washed over me when I realized that somehow, I had lost access to my Bitcoin wallet-with $600,000 inside. It happened in the worst possible way. In between juggling my to-do lists and trying to keep my sanity intact, I lost my seed phrase. I went through my apartment like a tornado, flipping through notebooks, checking every email, every file-nothing. I sat there in stunned silence, heart pounding, trying to process the fact that my entire savings, my security, and my financial future might have just vanished. In utter despair, I vented to my bridesmaid's group chat for some sympathetic words from the girls. Instead, one casually threw out a name that would change everything in a second: "Have you ever heard of Tech Cyber Force Recovery? They recovered Bitcoin for my cousin. You should call them." I had never heard of them before, but at that moment, I would have tried anything. I immediately looked them up, scoured reviews, and found story after story of people just like me—people who thought they had lost everything, only for Tech Cyber Force Recovery to pull off the impossible. That was all the convincing I needed. From the very first call, I knew I was in good hands. Their team was calm, professional, and incredibly knowledgeable. They explained the recovery process in a way that made sense, even through my stress-fogged brain. Every step of the way, they kept me informed, reassured me, and made me feel like this nightmare actually had a solution. And then, just a few days later, I got the message: "We have recovered your Bitcoin." (EMAIL. support @ tech cyber force recovery . com) OR WHATSAPP (+1 56 17 26 36 97) I could hardly believe my eyes: Six. Hundred. Thousand. Dollars. In my hands again. I let out my longest breath ever and almost cried, relieved. It felt like I woke up from a bad dream, but it was real, and Tech Cyber Force Recovery had done it. Because of them, I walked down the aisle not just as a bride, but as someone who had dodged financial catastrophe. Instead of spending my honeymoon stressing over lost funds, I got to actually enjoy it—knowing that my wallet, and my future, were secure. Would I refer to them? In a heartbeat. If you ever find yourself in that situation, please don't freak out, just call Tech Cyber Force Recovery. They really are the real deal.
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07.03.25 15:18 Ariduk

BEST LINK FOR RECOVERY SCAM ON TRADING INVESTMENT AND OTHERS TROUBLESHOOT? Welcome To General Hacking Techniques Service known as DHACKERS. Year 2025 we are active and best in what we do, as we give Solution to every problem concerning Web3 INTERNET activities, we guide you right to a positive fund Recovery e.t.c. Question From Most of Our Client, HOW POSSIBLE AND TIME WILL IT TAKE TO RECOVERY LOST OR SCAM FUND? Our Answer. Yes is 89.9% possible and how long it takes your Fund to be recovered depend on you. The fact is there are lot of fake binary investment companies out their, same a lot fake recovery companies and agents too. CAUTION 1). Make sure you ask one or two questions concerning the service and how they render there recovery services. 2) Do not give out your scammed details to any agent or hacker when you are not yet ready to recover back your fund. 3) do not make any payment when you are not sure of the service if you are to make one. (4) We discovered that most of this fake hacker or agent do give us scam details, playing the victim, because they can afford the service charge. 5) As long you have your scam details with you, you can recover your fund back anytime any-day with the right channel. Contact us from our Front Desk. [email protected] For advice and services, our standby Guru email. [email protected] [email protected] List of Service. ▶️Binary Recovery ▶️Data Recovery ▶️University Result Upgraded ▶️Clear your Internet Blunder and controversy ➡️Increase your Credit Score ➡️Wiping of Criminal Records ➡️Social Media Hack ➡️Blank ATM Card ➡️Load and wipe ➡️Phone Hacking ➡️Private Key Reset etc. For quick response. Email [email protected] Border us with your jobs and allow us give you positive result with our hacking skills. All Right Reserved (c)2025
08.03.25 04:42 andreassenhedda

If you’ve fallen victim to online scammers who have deceived you into investing your hard-earned money through fraudulent Bitcoin schemes, know that you're not alone. Countless individuals have been misled by scammers using various tactics to swindle money through deceptive investment platforms or promises of high returns. These scams often come in the form of fake cryptocurrency investments, Ponzi schemes, or phishing scams designed to steal your Bitcoin and other assets. Unfortunately, many victims suffer not only financial loss but also the psychological toll of realizing they’ve been taken advantage of. In some cases, scammers go even further, threatening legal consequences or using intimidation to keep victims silent. However, all hope is not lost. There are ways to recover your funds and potentially even bring those responsible to justice. One promising resource that can assist in reclaiming lost funds is Lee Ultimate Hacker, a trusted platform designed to help victims of online fraud. This service specializes in helping individuals who have been scammed through cryptocurrency investments or similar schemes. The platform’s expertise can help you navigate the process of recovering your money, giving you a chance to fight back against those who’ve wronged you. To begin the recovery process, it’s important to gather any proof of payment or transaction history involving the scammers. This evidence is crucial in tracing the flow of funds and identifying the scam operation behind it. Once you have collected your documentation, you can reach out to Lee Ultimate Hacker via LEEULTIMATEHACKER @ AOL . COM or wh@tsapp +1 (715) 314 - 9248 for assistance. They offer professional services to investigate fraudulent schemes, track Bitcoin transactions, and work to reverse fraudulent transfers. The recovery process can be complex and requires expert knowledge of blockchain technology and financial investigations, but with the help of a dedicated team, you’ll have a better chance of seeing your funds returned. In addition to helping you recover your assets, Lee Ultimate Hacker also works towards identifying the scammers and reporting them to the appropriate authorities. They understand the urgency of halting these fraudsters before they can victimize others. With their assistance, you not only increase the chances of recovering your funds but also play a part in holding cybercriminals accountable. If you've been affected by Bitcoin scams or other fraudulent online activities, reaching out to a service like Lee Ultimate Hacker can provide hope and a clear path forward. Their team can guide you through the recovery process, offering expert support while you take steps to reclaim what you’ve lost. It’s time to take action and work toward reclaiming your hard-earned money.
08.03.25 18:20 Diegolola514gmail.com

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08.03.25 18:23 faridasumadi

WEBSITE W.W.W.techcyberforcerecovery.com WHATSAPP +1 561.726.36.97 EMAIL [email protected] I Thought I Was Too Smart to Be Scammed, Until I Was. I'm an attorney, so precision and caution are second nature to me. My life is one of airtight contracts and triple-checking every single detail. I'm the one people come to for counsel. But none of that counted for anything on the day I lost $750,000 in Bitcoin to a scam. It started with what seemed like a normal email, polished, professional, with the same logo as my cryptocurrency exchange's support team. I was between client meetings, juggling calls and drafting agreements, when it arrived. The email warned of "suspicious activity" on my account. My heart pounding, I reacted reflexively. I clicked on the link. I entered my login credentials. I verified my wallet address. The reality hit me like a blow to the chest. My balance was zero seconds later. The screen went dim as horror roiled in my stomach. The Bitcoin I had worked so hard to accumulate over the years, stored for my retirement and my children's future, was gone. I felt embarrassed. Lawyers are supposed to outwit criminals, not get preyed on by them. Mortified, I asked a client, a cybersecurity specialist, for advice, expecting criticism. But he just suggested TECH CYBER FORCE RECOVERY. He assured me that they dealt with delicate situations like mine. I was confident from the first call that I was in good hands. They treated me with empathy and discretion by their staff, no patronizing lectures. They understood the sensitive nature of my business and assured me of complete confidentiality. Their forensic experts dove into blockchain analysis with attention to detail that rivaled my own legal work. They tracked the stolen money through a complex network of offshore wallets and cryptocurrency tumblers tech jargon that appeared right out of a spy thriller. Once they had identified the thieves, they initiated a blockchain reversal process, a cutting-edge method I was not even aware was possible. Three weeks of suffering later, my Bitcoin was back. Every Satoshi counted for. I sat in front of my desk, looking at the refilled balance, tears withheld. TECH CYBER FORCE RECOVERY not only restored my assets, they provided legal-grade documentation that empowered me to bring charges against the scammers. Today, I share my story with colleagues as a warning. Even the best minds get it. But when they do, it is nice to know the Wizards have your back.
09.03.25 17:22 Wayne707

‎Scammers have ruined the forex trading market, they have deceived many people with their fake promises on high returns. I learnt my lesson the hard way. I only pity people who still consider investing with them. Please try and make your researches, you will definitely come across better and reliable forex trading agencies that would help you yield profit and not ripping off your money. Also, if your money has been ripped-off by these scammers, you can report to a regulated crypto investigative unit who make use of software to get money back. If you encounter with some issue make sure you contact ( [email protected] ) they're recovery expert and a very professional one at that. ‎
10.03.25 18:18 springwilli

RECLAIM MY LOSSES REVIEWS HIRE DUNAMIS CYBER SOLUTIONI have always taken a security-first approach with my Bitcoin. That's why I put my hardware wallet in a fireproof safe-because you never know. Turned out I should have been even more paranoid. A few months ago, a fire took hold in my house. I lost nearly everything: the electronics, furniture, irreplaceable memorabilia. But when I dug through the remains, there it was: my Ledger hardware wallet, somehow intact. I held it up like some sort of post-apocalyptic movie scene, thinking, "At least my Bitcoin survived." But then, fate was not quite done with me: when I powered it on, it showed no signs of life whatsoever. The heat had fried the internal chip, and all I had was a melted, lifeless brick. That's when I realized my entire $680,000 in Bitcoin was trapped inside. At first, I told myself: "There has to be a way." From forensic data recovery to DIY repair tricks, everything I could conceivably Google, I researched. I considered buying an identical Ledger device and swapping components: spoiler, not a good idea unless you are an electrical engineer. Nothing worked. Every expert I contacted had the same answer: "Your Bitcoin is gone." I refused to accept that. That's when I found DUNAMIS CYBER SOLUTION I was skeptical, to say the least. If the manufacturer couldn't help me, how would they? At that point, I had no other choice but to try them. The first call I made, I immediately knew I was dealing with pros: no absurd promises, no giving of hopes, just explanation of their entire process with clarity-from advanced forensic techniques to secure data reconstruction. I mailed them my charred wallet, still half expecting a miracle to be impossible. Four days later, an email arrived. The subject line? "We have good news." They had successfully extracted my seed phrase and restored every single Bitcoin. I couldn't believe it. I had gone from losing everything to recovering $680,000 worth of crypto in just days. If your hardware wallet is damaged, dead, or seems irreparable, please do not give up. Just call DUNAMIS CYBER SOLUTION. They really did pull off some sort of high-stakes rescue operation on my behalf, and believe me, it was the stuff of legend. [email protected] +13433030545
10.03.25 20:19 wendytaylor015

My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected]
10.03.25 20:19 wendytaylor015

My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected]
11.03.25 13:35 cristydavis101

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11.03.25 16:23 dannywilliams

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11.03.25 16:23 dannywilliams

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11.03.25 16:25 ricciordonez

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12.03.25 05:35 cholevasaca

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15.03.25 00:29 irenmroma

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15.03.25 14:34 spencerwerner

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17.03.25 12:20 browne

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17.03.25 13:14 vinnypraise

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17.03.25 15:23 Charlesagrimes

Running the small business was hard enough in itself without having to suffer such stress as loss of access to a Bitcoin wallet. I had put $532,000 into Bitcoin for my business in case of emergencies, but one day, the wallet was nowhere to be seen. I had trusted it to my friend while he managed my financials, little knowing he would prove less than trustworthy. I couldn't envision the gut-wrenching feeling coupled with a great feeling of folly for having placed so much trust in someone else's hands. I could not believe my eyes to see that all of my digital fortune was gone. Literally, every second of my busy day froze in disbelief as I tried to conceive the loss. I remembered the hours I had put into building my business, securing my investments, and planning for the future. Now, I was staring at an empty wallet that once held my safety net. This hit me hard-not only had I lost $532,000, but the betrayal hurt further because it came from someone I considered reliable. The emotional toll was huge, and this financial hit could cripple my business. In the midst of my despair, I knew I had to act fast. I went online, searching everywhere for a solution through which I could get back in control of my finances. That is where I came across Digital resolution services. I knew them for recovery related to lost crypto, but also somehow skeptical. At that point, it looked like I had nothing left to lose. I reached out, and with our very first conversation, I gained the impression that they did understand my situation. Their team was nothing short of extraordinary. They approached my case with professionalism and empathy, meticulously explaining the recovery process in clear, simple terms that alleviated some of my anxiety. They set realistic expectations while promising to do everything possible to retrieve my funds. Over the next several weeks, they worked tirelessly, employing advanced blockchain forensic techniques and sophisticated tracking tools that I’d never even heard of before. It kept me informed with constant updates and gave me hope in this desperate time. Finally, it came-the day Digital resolution services recovered my lost Bitcoin. Relief overwhelmed me, and it wasn't all about the money but control and self-trust in my financial future. This experience taught me the most valuable lesson: never lose control over your own financial security and never put blind trust in anyone. With Digital resolution services, I recovered not only my $532,000 but also learned how to protect my assets better in the future. Contact Digital Resolution Services: Email: digitalresolutionservices (@) myself. c o m WhatsApp: +1 (361) 260-8628 Stay protected Charles agrimes
18.03.25 23:49 lindaspringer311

My name is Linda Springer, and I am part of an ultra-high-net-worth family in the United Kingdom. Managing our wealth has always been a top priority, and I’ve always looked for opportunities to grow and diversify our investments. When I was introduced to an offshore banking investment program called Market Fund, it seemed like the perfect opportunity to enhance our portfolio. The program promised impressive returns, and with professional advisors and seemingly legitimate documentation, I felt confident that it was a secure investment. However, my trust in Market Fund quickly proved to be misplaced. Initially, everything seemed to go smoothly. Reports showed strong growth, and I was reassured by the continuous updates from the program’s advisors. But when I tried to withdraw my funds, everything took a sharp turn. The Market Fund website became unresponsive, and all my attempts to reach the advisors through emails and phone calls went unanswered. It was then that I realized I had fallen victim to a scam, and the £60,000 I had invested was gone. Feeling devastated and unsure of what to do next, I turned to a friend I had met at a club called The Vault. During one of our conversations, Alex, a fellow club-goer, mentioned how Rapid Digital Recovery had helped them recover funds from a similar fraudulent scheme. Intrigued by their success story, I decided to reach out to Rapid Digital Recovery....Whatsapp: +1 4 14 80 71 4 85.. hoping they could help me recover my money as well. From the moment I contacted them, the team at Rapid Digital Recovery took immediate action. They began investigating the scam and used advanced technology to trace the origins of Market Fund. Through their efforts, they uncovered a network of fake websites and shell companies that had been designed to deceive investors like myself. Thanks to their persistence and expertise, Rapid Digital Recovery successfully recovered the £60,000 I had lost.....Email: rapid digital recovery (@) execs. com.. Not only did they restore my financial security, but they also exposed the fraudulent operation behind Market Fund, preventing others from falling victim to the same scam. While the experience was incredibly stressful, it served as an important reminder of the need for due diligence and professional oversight when dealing with offshore investments. I am incredibly grateful to Rapid Digital Recovery for their support, expertise, and determination in recovering my funds. Telegram: h t t p s: // t. me /Rapiddigitalrecovery1
20.03.25 17:21 katherineingram

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20.03.25 17:21 katherineingram

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20.03.25 19:34 ysabelbristol

I am YSABEL, a medical student living with my grandmother. For the past few years, I’ve been diligently saving my grandmother’s money to ensure she has a comfortable and secure future. I invested over $40,000 into an online platform, which promised significant returns. After completing several steps, they told me that to withdraw the money, I needed to make another payment of $73,000. As a medical student, my time is already stretched thin, and managing my grandmother’s finances on top of my studies was overwhelming. That's when a costudent, who had gone through a similar experience, recommended MUYERN TRUST HACKER. Initially, I was skeptical. After all, how could anyone help me recover the money I had already lost to such a sophisticated scam? But after reaching out to them, I quickly realized I was in good hands. From the very first conversation, the team at MUYERN TRUST HACKER was professional, compassionate, and knowledgeable. They understood the gravity of my situation and took the time to thoroughly review all the details of my case. They explained the recovery process clearly, step by step, and assured me that they would work tirelessly to help recover the funds I had lost. Their expertise in dealing with online fraud was evident, and for the first time in weeks, I felt a sense of hope that I could regain control over this situation. The recovery process wasn’t immediate, and there were moments of uncertainty, but MUYERN TRUST HACKER remained dedicated and persistent. As someone who has been working hard to save and protect my grandmother’s money, it was devastating to feel like I might lose everything to a scam. But MUYERN TRUST HACKER gave me the support, expertise, and guidance I needed to recover what was lost. I’m deeply grateful for their help, and I now have a renewed sense of hope that I can protect my grandmother’s future. If you find yourself in a similar situation, feeling trapped and overwhelmed by a fraudulent company, I highly recommend MUYERN TRUST HACKER. (Whats A p p at + 1 (440) (335) (0205) Their team provides real solutions, and their professionalism and dedication were a lifeline for me during a very difficult time. Thanks to them, I feel confident that I can move forward and continue to protect my grandmother’s savings. Alternatively, contact their tele gram also if you need immediate attention at muyerntrusthackertech.
21.03.25 21:19 carmenbeechum643

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22.03.25 14:13 [email protected]

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23.03.25 12:24 maryjul75

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23.03.25 22:01 joaneldnde

The ground trembled like a nervous intern on espresso shots. One minute, I was monitoring my geothermal Bitcoin miners, humming in harmony with Iceland's most unpredictable volcano. Next? An eruption painted the sky gray with ash, raining destruction like an out-of-control blockchain fork. Power cables flickered out. Servers turned into abstract-art pieces. And my wallet with $460,000 worth of mining revenue fried faster than a motherboard in a tidal wave of lava. I was knee-deep in volcanic mud, clutching the charred wallet, wondering if the universe had a vendetta against renewable energy. For weeks, I’d played geothermal gambler, harnessing Earth’s anger to mine crypto. Now, Mother Nature had countered with a literal power move. My wallet’s backups? Corrupted by ash-clogged drives. My cold storage? Warmer than a freshly erupted fissure. Even the volcanologists on my team shrugged. “We predict lava, not ledger errors,” one said, handing me a business card signed at the edges. “Try these Cyber Constable Intelligence. They’ve fixed crypto in weird places.” Cyber Constable Intelligence phoned on the first ring. Cyber Constable Intelligence saved not just crypto. They demonstrated that even the fury of nature cannot surpass human tenacity. My operation now operates robustly, excavating coins with Earth's anger and a backup generator sufficient to run a small glacier. The volcano? Still grumbling. My wallet? Locked inside a fireproof safe, as irony bites sharper than an Icelandic winter. If your crypto somehow gets smothered beneath the pyroclastic ash of life, skip the freak-out. Call the Cybers. They'll dig through lava streams until your cash bubbles up to the surface. Just maybe set up your rigs a few miles closer to the crater next time. If you’re facing a similar problem I highly recommend contacting Cyber Constable Intelligence 📞 WhatsApp:+1 (2 5 2 ) 3 7 8 7 6 1 1 🌐 Website: www cyberconstableintelligence.com 📩 Telegram: https://t.me/cyberconstable
23.03.25 22:18 [email protected]

A few months ago, I stumbled upon a post about a cryptocurrency investment platform that I thought was a good idea for me to invest in crypto, I didn’t realize I was being catfished by the cryptocurrency investment manager who promised me huge returns on my investment. I lost my capital of $170,000 and interest without receiving any profits in return, I was devastated And I realized I had been scammed out of my cryptocurrency. I felt hopeless and didn’t know where to turn. i searched everywhere for a solution and That’s when my friend introduced me to a Crypto recovery platform Trust Geeks Hack Expert. At first, I was skeptical—after all, I had just been scammed—but Trust Geeks Hack Expert professionalism and transparency reassured me. They took the time to analyze my case, track my lost funds, and guide me through the recovery process. To my amazement, they successfully retrieved my stolen crypto! I can’t express how grateful I am to Trust Geeks Hack Expert team. Their expertise and dedication gave me a second chance. If you’ve been a victim of a crypto scam, don’t lose hope—Trust Geeks Hack Expert. is the real deal! for assistance, Email: [email protected] (TeleGram:: Trustgeekshackexpert) & what's A p p +1 7 1 9 4 9 2 2 6 9 3
24.03.25 10:03 Diegolola514gmail.com

"I am incredibly grateful to Sylvester Bryant for his exceptional expertise and assistance in recovering my USDC funds, which were lost to a phishing scam. Thanks to his diligent efforts, he successfully retrieved €480,000 worth of USDC that I thought was gone forever. If you or anyone you know has fallen victim to a similar situation, I highly recommend reaching out to Sylvester for professional assistance. You can contact him via email at Yt7cracker@gmail. com or through WhatsApp/text message at +1 512 577 7957. He is truly a reliable and skilled professional in recovering assets from online scams."
25.03.25 18:06 maryjul75

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27.03.25 01:52 debrapavon89

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