Linear and Integer Programming

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Related lectures (32)

Convex Optimization: Theory and Applications

Explores convex optimization theory, covering local and global minima, convex functions, and applications in various fields.

Convex Optimization: Epigraphs

Explores epigraphs, convexity of bivariate functions, and log-sum-exp functions in convex optimization.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Convex Optimization: Gradient Descent

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.

Convex Optimization Problems: Standard Form

Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.

KKT Conditions: Convex Optimization

Explores the KKT conditions in convex optimization, including dual cones, SDP duality, and convex hulls.

Lagrangian Duality: Theory and Applications

Explores Lagrangian duality in convex optimization, discussing strong duality, dual solutions, and practical applications in second-order cone programs.

Optimization Problems: Standard Form

Explores optimization problems in standard form, convex optimization, and optimality criteria.

Cutset Formulation: MST Problem

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

Cones of convex sets

Explores optimization on convex sets, including KKT points and tangent cones.

Linear constraints, Feasible directions

Explores feasible directions in optimization algorithms and how to determine them.

Optimization Programs: Piecewise Linear Cost Functions

Covers the formulation of optimization programs for minimizing piecewise linear cost functions.

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