Lectures related to Optimization methods

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

Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.

Newton Method: Convergence and Quadratic Care

Covers the Newton method and its convergence properties near the optimal point.

Faster Gradient Descent: Projected Optimization Techniques

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Optimization with Constraints: KKT Conditions

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Gradient Descent: Optimization and Constraints

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

Introduction to Optimization and Operations Research

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

Semi-Definite Programming

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