The Geometry of Linear Optimization

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Page 2 of 3

KKT for convex problems and Slater's CQ

Covers the KKT conditions and Slater's condition in convex optimization problems.

Linear Programming Techniques in Reinforcement Learning

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Optimization Problems: Standard Form

Explores optimization problems in standard form, convex optimization, and optimality criteria.

Convex Optimization

Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.

Faster Gradient Descent: Projected Optimization Techniques

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

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Optimization Techniques: Gradient Descent and Convex Functions

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

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

Semi-Definite Programming

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

Duality: Duality in Linear Optimization

Covers the concept of linear optimization and the duality relationship between primal and dual problems.