Lectures related to Quadratic Penalty Methods: Sound Problems

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Optimization Problems: Greedy Algorithms

Explores optimization problems and greedy algorithms to find the best solutions efficiently.

Cones of convex sets

Explores optimization on convex sets, including KKT points and tangent cones.

Set Cover: Integrality Gap

Explores the integrality gap concept in set cover and multiplicative weights algorithms.

Optimization in Engineering

Explores optimization methods in engineering, covering decision variables, constraints, and various solving techniques.

Linear Optimization: Finding Initial BFS

Explains the process of finding an initial Basic Feasible Solution for linear optimization problems using the Simplex Algorithm.

Faster and Projected Gradient Descent: Optimization Techniques

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

Optimization Techniques: Convexity and Algorithms in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.