Duality (optimization)

Related lectures (31)

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Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.

Explores convexifying nonconvex problems through kernel tricks, sensitivity interpretation, and nonlinear dimensionality reduction.

Explores dual translations in linear programming, emphasizing primal and dual formulations and the significance of invertible submatrices.

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Covers the proof of strong duality in optimization problems and provides examples of Rayleigh quotient optimization.

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

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

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Explores minimax optimization theory, including weak and strong duality, saddle points, and practical algorithm performance.

Explores duality in network optimization problems with examples and step-by-step explanations.