Lectures related to Objective function, Gradient and descent

Covers optimization methods, convergence guarantees, trade-offs, and variance reduction techniques in numerical optimization.

Discusses advanced optimization techniques, focusing on faster and projected gradient descent methods in machine learning.

Explores energy systems modeling, optimization, and cost analysis for efficient operations.

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Explores primal-dual optimization algorithms for convex-concave minimax problems, discussing convergence properties and applications.

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

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.