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Lecture
Convex Optimization: Generalized Inequalities
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Related lectures (37)
Conjugate Duality: Envelope Representations and Subgradients
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Explores envelope representations, subgradients, and the duality gap in convex optimization.
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.
Legendre Transform
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Explores the Legendre transform, duality in convex analysis, and optimization problems.
Distributionally Robust Portfolio Optimization
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Explores distributionally robust portfolio optimization and compares different estimation approaches and methods for portfolio evaluation.
Convex Optimization: Elementary Results
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Explores elementary results in convex optimization, including affine, convex, and conic hulls, proper cones, and convex functions.
Convex Optimization
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Covers an overview of convex optimization, affine sets, polyhedra, ellipsoids, and convex functions.
Optimization Problems: Standard Form
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Explores optimization problems in standard form, convex optimization, and optimality criteria.
Linear Algebra Review: Convex Optimization
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Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.
Support Vector Regression: Recap and Convex Optimization
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Covers the recap of Support Vector Regression with a focus on convex optimization and its equivalence to Gaussian Process Regression.
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.
Convex Optimization: Duality and KKT Conditions
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Explores convex optimization duality, KKT conditions, and financial arbitrage detection.
Lyapunov Stability: Convex Optimization
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Covers Lyapunov stability in convex optimization and its implementation through trajectory plots.
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.
Optimal Transport: Theory and Applications
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Covers the theory and applications of optimal transport, focusing on infimal convolution and Kantorovich potentials.
Solving Linear Programs: SIMPLEX Method
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Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.
General Conics and Homogeneous Coordinates
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Covers general conics, homogeneous coordinates, conic recognition, and matrix decomposition.
Introduction to Shortest Path
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Introduces the concept of shortest path, discussing weighted paths, Hamiltonian paths, and path optimization algorithms.
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