Duality: Duality in Linear Optimization

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Explores strong duality, complementary slackness, economic interpretation, and stochastic problem scenarios in linear programming.

Optimization Duality: Theory and Algorithms

Explores optimization duality, weak and strong duality, practical optimization algorithms, and challenges in nonconvex-concave problems.

Primal-dual Optimization: Fundamentals

Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.

Network Flows and LP Formulations

Explains network flows, LP formulations, simplex method, duality, and practical applications.

Quantile Regression: Linear Optimization

Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.

Linear Optimization: Directional Derivatives

Explores directional derivatives in linear optimization and their impact on objective functions.

Lagrangian Duality: Convex Optimization

Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.

Optimization: Mathematical Principles and Algorithms

Covers mathematical principles and algorithms of optimization, using real-world examples and Python implementation.

Optimization Programs: Piecewise Linear Cost Functions

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

Approximation Algorithms

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.