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Related lectures (33)
Convex Optimization: Dual Cones
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Explores dual cones, generalized inequalities, SDP duality, and KKT conditions in convex optimization.
KKT Conditions: Convex Optimization
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Explores the KKT conditions in convex optimization, including dual cones, SDP duality, and convex hulls.
Convexity: Functions and Global Minima
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Explores convex functions, global minima, and their relationship with differentiability.
Optimal Transport: Gradient Flows in Rd
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Explores optimal transport and gradient flows in Rd, emphasizing convergence and the role of Lipschitz and Picard-Lindelöf theorems.
Conjugate Duality: Understanding Convex Optimization
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Explores conjugate duality in convex optimization, covering weak and supporting hyperplanes, subgradients, duality gap, and strong duality conditions.
Convex Optimization: Theory and Applications
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Explores convex optimization theory, covering local and global minima, convex functions, and applications in various fields.
Convex Optimization: Epigraphs
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Explores epigraphs, convexity of bivariate functions, and log-sum-exp functions in convex optimization.
KKT for convex problems and Slater's CQ
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Covers the KKT conditions and Slater's condition in convex optimization problems.
Conjugate Duality: Envelope Representations and Subgradients
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Explores envelope representations, subgradients, and the duality gap in convex optimization.
Geodesically Convex Optimization
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Covers geodesically convex optimization on Riemannian manifolds, exploring convexity properties and minimization relationships.
Convex Optimization
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Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.
Conjugate Functions: Dual Programs
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Explores conjugate functions, dual programs, rescaling, convex programs, and optimization domains.
Convex Optimization: Exercises
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Covers exercises on convex optimization, focusing on formulating and solving optimization problems using YALMIP and solvers like GUROBI and MOSEK.
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