Lectures related to Descent methods and line search: Inexact line search

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Covers the optimality of convergence rates in accelerated and stochastic gradient descent methods for non-convex optimization problems.

Explores optimization trade-offs, variance reduction, statistical dimension, and convergence analysis in optimization algorithms.

Discusses gradient descent for optimization with equality constraints and iterative convergence criteria.

Explores choosing a step size in optimization on manifolds, including backtracking line-search and the Armijo method.