Lyapunov Stability: Convex Optimization

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

Convex Optimization: Sets and Functions

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

Convex Optimization: Elementary Results

Explores elementary results in convex optimization, including affine, convex, and conic hulls, proper cones, and convex functions.

Convex Optimization: Theory and Applications

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

Linear Algebra Review: Convex Optimization

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

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 Optimization: Conjugate Duality

Explores envelope representations, subgradients, conjugate functions, duality gap, and strong duality in convex optimization.

Optimal Transport: Rockafellar Theorem

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

Convex Optimization: Self-dual Cones

Explores self-dual cones in convex optimization and their applications in various optimization problems.

KKT Conditions: Convex Optimization

Explores the KKT conditions in convex optimization, including dual cones, SDP duality, and convex hulls.