Set cover problem

Related lectures (12)

Explores the integrality gap concept in set cover and multiplicative weights algorithms.

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

Explores the set covering problem through the example of completing a sticker collection at a minimum cost.

Covers the concept of graph sketching with a focus on connected components.

Covers the fundamentals of integer optimization, including integer programming, dynamic programming, and approximation algorithms.

Covers definitions and concepts in discrete optimization, including binary linear problems and combinatorial optimization.

Explores convex relaxation and negative type theorems in convex programs.

Explores Two-Stage Stochastic Programs, problem reformulation, Benders decomposition, extreme points, and sensitivity analysis.

Explores solving discrete optimization problems by relaxing integrality constraints.

Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.

Introduces Cartesian product and induction for proofs using integers and sets.

Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.

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