Lectures related to Convex Optimization

Subgradients and Convex Functions

Explores subgradients in convex functions, emphasizing non-differentiable yet convex scenarios and properties of subdifferentials.

Optimization Techniques: Convexity in Machine Learning

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

Gradient Descent Methods: Theory and Computation

Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.

Mathematics of Data: Optimization Basics

Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Convex Optimization Log in to Mediaspace to watch this video

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

KKT and Convex Optimization Log in to Mediaspace to watch this video

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Convex Optimization: Gradient Flow Log in to Mediaspace to watch this video

Explores convex optimization, emphasizing the importance of minimizing functions within a convex set and the significance of continuous processes in studying convergence rates.

Convex Optimization: Theory and Applications Log in to Mediaspace to watch this video

Explores convex optimization theory, covering local and global minima, convex functions, and applications in various fields.

Optimization Techniques: Convexity and Algorithms in Machine Learning Log in to Mediaspace to watch this video

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

Convex Optimization: Gradient Descent Log in to Mediaspace to watch this video

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.

Linear Algebra Review: Convex Optimization Log in to Mediaspace to watch this video

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

KKT for convex problems and Slater's CQ Log in to Mediaspace to watch this video

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

Convex Optimization: Sets and Functions Log in to Mediaspace to watch this video

Introduces convex optimization through sets and functions, covering intersections, examples, operations, gradient, Hessian, and real-world applications.

Convexity: Functions and Global Minima Log in to Mediaspace to watch this video

Explores convex functions, global minima, and their relationship with differentiability.

Convex Optimization: Introduction and Sets Log in to Mediaspace to watch this video

Covers the fundamentals of convex optimization, including mathematical problems, minimizers, and solution concepts, with an emphasis on efficient methods and practical applications.

Conjugate Duality: Understanding Convex Optimization Log in to Mediaspace to watch this video

Explores conjugate duality in convex optimization, covering weak and supporting hyperplanes, subgradients, duality gap, and strong duality conditions.

Conjugate Duality: Envelope Representations and Subgradients Log in to Mediaspace to watch this video

Explores envelope representations, subgradients, and the duality gap in convex optimization.

Convex Sets: MGT-418 Lecture Log in to Mediaspace to watch this video

On Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.

Linear Programming Techniques in Reinforcement Learning Log in to Mediaspace to watch this video

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