Linked from

The 22 pages that link to Integer programming, each with the reason it gives.

• Constraint satisfaction problemRelated: It can encode many CSPs, but uses a narrower linear modeling language.

• PolyhedronRelated: Polyhedral relaxations bound and help solve problems with discrete variables.

• Combinatorial optimizationRelated: It expresses discrete choices as variables, constraints, and an objective function.

• Chromatic numberRelated: A binary-variable formulation can seek the fewest colors while enforcing different colors on adjacent vertices.

• Geometry of numbersRelated: Geometric methods for integer points inform bounds and algorithms for discrete optimization.

• NP-hardnessRelated: General integer programming is NP-hard, linking complexity theory to mathematical optimization.

• Kidney exchangeRelated: Kidney exchange programs use it to choose feasible cycles and chains that maximize transplants.

• Vehicle routing problemRelated: Binary decisions can represent whether a vehicle travels between two locations.

• Floor and ceiling functionsRelated: Floor and ceiling inequalities can express integer bounds and strengthen optimization formulations.

• Travelling salesman problemRelated: Binary edge variables encode selected roads, while constraints enforce a valid tour.

• Pick's theoremRelated: Lattice-point counts connect polygon geometry with feasible integer solutions.

• Optimal facility locationRelated: Binary site-selection variables encode whether a candidate facility is opened.

• Shapley–Folkman lemmaRelated: Convexification can relax discrete choices, while the lemma limits how many summands need remain nonconvex.