Discrete Optimization: Relaxation

Related lectures (32)

Explores the simplex algorithm and how optimal solutions can be found on vertices of constraint polyhedra.

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

Explores optimization problems in standard form, convex optimization, and optimality criteria.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

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

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

Covers semi-definite programming and optimization over positive semidefinite cones.

Explores optimal decision making through discrete optimization, emphasizing binary variables and practical applications.

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

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

Covers deriving basic linear program representation, finding solutions, and exploring optimality.

Explores optimization methods in engineering, covering decision variables, constraints, and various solving techniques.

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