Formulation, Problem Transformations

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Related lectures (32)

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Optimization: Mathematical Principles and Algorithms

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

Duality: Duality in Linear Optimization

Covers the concept of linear optimization and the duality relationship between primal and dual problems.

Simplex Algorithm: Initial Tableau & General Case

Covers the simplex algorithm, from initial tableau to general case.

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.

Optimization with Inequalities

Explores optimization with inequality constraints, emphasizing finding extreme values and stationary points.

Nonlinear Optimization Methods

Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.

Simplex Algorithm

Covers the Simplex algorithm for function minimization with linear constraints.

Cutset Formulation: MST Problem

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

Formulation, Problem Definition

Covers the formulation and problem definition in optimization, focusing on defining the objective function, constraints, and feasible set.

Two-phase Simplex Algorithm: Introduction and Duality

Introduces the two-phase simplex algorithm and explores duality in linear programming.