Lectures related to Gradient Descent: Optimization and Constraints

Optimization with Constraints: Theory and Applications

Covers the theory and applications of optimization with constraints, including key concepts and numerical methods.

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.

Semi-Definite Programming

Covers semi-definite programming and optimization over positive semidefinite cones.

Quasi-newton optimization

Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.

Optimization Techniques: Convexity in Machine Learning

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

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

ALM with Inequalities: Next Steps in Optimization

Explores the Augmented Lagrangian Method with equality and inequality constraints in optimization, emphasizing the importance of slack variables.