Convex Optimization: Introduction and Sets

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Related lectures (30)

Convex Optimization: Dual Cones

Explores dual cones, generalized inequalities, SDP duality, and KKT conditions in convex optimization.

Convex Optimization: Theory and Applications

Explores the theory and applications of convex optimization, covering topics such as log-determinant function, affine transformations, and relative entropy.

Convex Polyhedra and Linear Programs

Explores convex polyhedra, linear programs, and their optimization importance.

Optimal Transport: Rockafellar Theorem

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Convex Optimization: Epigraphs

Explores epigraphs, convexity of bivariate functions, and log-sum-exp functions in convex optimization.

Faster Gradient Descent: Projected Optimization Techniques

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

Convex Optimization: Generalized Inequalities

Explores problems with generalized inequalities in convex optimization and the equivalence between SOCP and SDP.

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.

Convex Sets: Theory and Applications

Explores convex sets, their properties, and applications in optimization.

Robust Optimization: Polynomial Optimization

Explores polynomial optimization, including writing polynomials as matrix products and solving linear equations for nonnegativity.

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