Feasible region

Formal sciences
Theoretical computer science
Algorithms and data structures
Combinatorial optimization

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Related lectures (32)

Page 3 of 4

Linear Programming: Convex Hull

Covers MAE regression, convex hull, reformulation advantages, and practical problems with decision variables and constraints.

Branch and Bound: Formal Description

Covers the Branch and Bound algorithm, focusing on formal description and implementation steps to find optimal integer solutions.

Linear constraints, Feasible directions

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

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: Two-phase Simplex Algorithm

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

Simplex Algorithm: Initial Tableau, Simple Case

Covers the simplex algorithm applied to the simple case of inequality constraints.

Extreme Values and Optimization

Covers extreme values, optimization conditions, feasible sets, and partition formation for optimization.

Formulation, Problem Transformations

Explores transforming optimization problems to meet algorithm requirements and make them equivalent.

Linear Optimization: Fundamentals

Covers the basics of linear optimization, including equations, polyhedrons, feasible directions, and optimal solutions.

Exact methods: Branch and Bound

Explores the Branch and Bound algorithm in discrete optimization, efficiently finding optimal solutions by calculating lower bounds on subsets.