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Lecture
Convex Sets: Theory and Applications
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Related lectures (28)
Convex Optimization: Convex Functions
Covers the concept of convex functions and their applications in optimization problems.
Optimal Transport: Rockafellar Theorem
Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.
Optimization Problems: Path Finding and Portfolio Allocation
Covers optimization problems in path finding and portfolio allocation.
Geodesic Convexity: Theory and Applications
Explores geodesic convexity in metric spaces and its applications, discussing properties and the stability of inequalities.
Linear Constraints: Polyhedron
Explains linear constraints and the concept of a polyhedron in optimization problems.
Convex Optimization: Gradient Descent
Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.
Optimal Transport: Theory and Applications
Explores Lagrange multipliers, minimax theorems, and convex subsets in optimal transport theory.
Convex Optimization: Elementary Results
Explores elementary results in convex optimization, including affine, convex, and conic hulls, proper cones, and convex functions.
KKT and Convex Optimization
Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.
Convexity: Functions and Global Minima
Explores convex functions, global minima, and their relationship with differentiability.
Cones of convex sets
Explores optimization on convex sets, including KKT points and tangent cones.
Optimal Transport: Cyclically Monotone Sets
Covers cyclically monotone sets in optimal transport theory and their properties.
Convex Functions
Covers the definition of convex functions and their properties in optimization.
Convex Optimization: Sets and Functions
Introduces convex optimization through sets and functions, covering intersections, examples, operations, gradient, Hessian, and real-world applications.
Unconstrained Optimization Theory
Explores unconstrained optimization theory, covering global and local minima, convexity, and gradient concepts.
Optimization with Constraints: KKT Conditions
Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.
Convex Optimization: Introduction and Sets
Covers the fundamentals of convex optimization, including mathematical problems, minimizers, and solution concepts, with an emphasis on efficient methods and practical applications.
Introduction to Convexity
Introduces the key concepts of convexity and its applications in different fields.
Solving Linear Programs: SIMPLEX Method
Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.
Convex Sets: Mathematical Optimization
Introduces convex optimization, covering convex sets, solution concepts, and efficient numerical methods in mathematical optimization.
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