Nonlinear programming

Formal sciences
Theoretical computer science
Algorithms and data structures
Combinatorial optimization

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Related lectures (32)

Linear Programming: Solving LPs

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

Dynamic Programming: Steinitz Sequence

Explores dynamic programming with the Steinitz sequence to optimize solutions efficiently.

Simplex Algorithm: Basics

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Convex Optimization: Farkas' Lemma

Covers Farkas' lemma, exploring the relationship between linear programs and the conditions for its validity.

Convex Optimization: Theory and Applications

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

Optimization Methods: Theory Discussion

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

Local Sensitivity Analysis: Feasibility and Optimality

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

Linear Programming Basics

Covers the basics of linear programming and the simplex method, focusing on finding optimal solutions and handling degeneracy.

Hedging for LPs

Covers the Hedge algorithm for minimizing loss in linear programming problems.

Linear Programming: Optimization and Constraints

Introduces linear programming, emphasizing optimization with constraints and practical examples.

Linear Optimization Techniques: Problem Solving Approaches

Provides an overview of linear optimization techniques, focusing on problem-solving methods and the importance of constraints and objective functions.

Linear Programming: Convex Hull

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

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