Linked from

The 17 pages that link to Combinatorial optimization, each with the reason it gives.

• Convex optimizationCompared with: Its discrete feasible sets are usually nonconvex despite convex relaxations.

• Integer programmingNarrower topic: Many integer programs are algebraic formulations of combinatorial optimization problems.

• P versus NP problemNarrower topic: Many optimization problems encode NP-complete decision problems.

• Greedy algorithmNarrower topic: Greedy algorithms are one approach to optimizing discrete choices.

• Constrained optimizationCompared with: Its discrete feasible sets require methods unlike smooth multiplier-based analysis.

• Assignment problemNarrower topic: The assignment problem is a canonical discrete optimization problem.

• Approximation algorithmNarrower topic: Approximation algorithms target discrete optimization problems whose exact solutions may be computationally difficult.

• Optimization problemBroader topic: Discrete feasible choices resist the smooth methods used in continuous problems.

• Vehicle routing problemNarrower topic: Choosing route assignments and visit orders makes vehicle routing a combinatorial optimization problem.

• Max-flow min-cut theoremNarrower topic: The theorem is a canonical example of an exact structural certificate for an optimum.

• Numerical optimizationBroader topic: Discrete decisions often require different algorithms from continuous numerical methods.

• Blossom algorithmNarrower topic: The blossom algorithm became a foundational result in this field.

• Ellipsoid methodRelated: The method helped establish polynomial-time results for problems whose convex relaxations admit efficient separation oracles.

• Travelling salesman problemNarrower topic: A tour is a discrete choice of edges optimized for total travel cost.

• Algebraic combinatoricsCompared with: Its central goal is optimization, while algebraic combinatorics emphasizes structural and enumerative insight.

• Baranyai's theoremRelated: Finding useful decompositions connects the theorem to discrete optimization methods.

• Richard M. KarpNarrower topic: This field connects Karp’s algorithmic research with graph and network problems.