Linear Programming: Extreme Points

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Linear Programming Techniques in Reinforcement Learning

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Approximation Algorithms

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

Linear Programming: Solving LPs

Covers the process of solving Linear Programs (LPs) using the simplex method.

Linear Programming Duality

Explores the concept of duality in linear programming and its practical implications in optimization.

Linear Programming: Two-phase Simplex Algorithm

Covers the application of the two-phase Simplex algorithm to solve linear programming problems.

Branch & Bound: Optimization

Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.

Linear Programming Duality

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

Local Sensitivity Analysis: Feasibility and Optimality

Explores local sensitivity analysis in linear programming, examining how changes impact optimality and feasibility.

Game Theory: Minimax Theorem

Explores zero-sum games and the minimax theorem in Game Theory, emphasizing optimal strategies.

Solving Linear Programs: SIMPLEX Method

Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.