Gradient Descent: Early Stopping and Stochastic Gradient Descent

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Stochastic Gradient Descent: Optimization Techniques

Explores Stochastic Gradient Descent with Averaging, comparing it with Gradient Descent, and discusses challenges in non-convex optimization and sparse recovery techniques.

Strong Convexity and Convergence Rates

Explores strong convexity's role in faster convergence rates for optimization algorithms.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Stochastic Optimization: Algorithms and Methods

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.

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Adaptive Optimization Methods: Theory and Applications

Explores adaptive optimization methods that adapt locally and converge without knowing the smoothness constant.

Momentum methods and nonlinear CG

Explores gradient descent with memory, momentum methods, conjugate gradients, and nonlinear CG on manifolds.

Newton's Method: Optimization Techniques

Explores optimization techniques like gradient descent, line search, and Newton's method for efficient problem-solving.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.