Robust Optimization: Radiation Therapy & Support Vector Machines

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Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.

Convex Optimization: Farkas' Lemma

Covers Farkas' lemma, exploring the relationship between linear programs and the conditions for its validity.

Convex Sets: Mathematical Optimization

Introduces convex optimization, covering convex sets, solution concepts, and efficient numerical methods in mathematical optimization.

Conjugate Duality: Envelope Representations and Subgradients

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

Convex Sets: MGT-418 Lecture

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

Convex Optimization: Gradient Descent

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

Conjugate Duality: Understanding Convex Optimization

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

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.

Projected Gradient Descent

Explores convex constrained optimization through Projected Gradient Descent, focusing on tangent space and iterative minimization.