Lecture
KKT for convex problems and Slater's CQ
Related lectures (76)
Convex Optimization Problems: Standard Form
Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.
Convex Optimization: Self-dual Cones
Explores self-dual cones in convex optimization and their applications in various optimization problems.
Subgradients and Convex Functions
Explores subgradients in convex functions, emphasizing non-differentiable yet convex scenarios and properties of subdifferentials.
KKT Conditions: Convex Optimization
Explores the KKT conditions in convex optimization, including dual cones, SDP duality, and convex hulls.
Convex Optimization: Exercises
Covers exercises related to convex optimization, including SOCP dual, KKT conditions, and equality constrained least squares.
Primal-dual Optimization II
Explores primal-dual optimization methods, focusing on Lagrangian approaches and various methods like penalty, augmented Lagrangian, and splitting techniques.
Convex Optimization: Farkas' Lemma
Covers Farkas' lemma, exploring the relationship between linear programs and the conditions for its validity.
The Geometry of Linear Optimization
Delves into linear optimization formulation, capacity expansion, investment under taxation, and revenue management in various industries.
Optimization Problems: Standard Form
Explores optimization problems in standard form, convex optimization, and optimality criteria.
Convex Optimization Problems
Covers Convex Optimization Problems, LP formulations, and practical implementations using CVXPY and GUROBI.