Linked from

The 61 pages that link to Linear programming, each with the reason it gives.

• Convex setBroader topic: Its feasible regions are polyhedra, a central class of convex sets.

• Operations researchBroader topic: It models resource-allocation decisions with explicit objectives and limits.

• Convex optimizationBroader topic: It is the foundational special case of convex optimization.

• Constrained optimizationBroader topic: It is the tractable, widely used case where both the objective and restrictions are linear.

• Convex geometryBroader topic: Its feasible regions are polyhedra, a central class of convex sets.

• ConvexityBroader topic: Its feasible region is a convex polyhedron, making it a foundational convex optimization problem.

• Mathematical optimizationBroader topic: It models resource allocation when effects and limits are linear.

• Convex analysisBroader topic: It is a foundational convex problem with geometric and dual interpretations.

• Optimization problemBroader topic: It is a tractable, widely used subclass with a distinctive geometric solution structure.

• Polynomial timeBroader topic: Interior-point methods solve linear programs in polynomial time.

• Linear programming relaxationBroader topic: The relaxation is solved as an ordinary linear program.

• Maximum and minimumBroader topic: Its finite optima, when they exist, occur at vertices of the feasible region.

• Numerical optimizationBroader topic: It is a structured special case with efficient algorithms and strong global guarantees.

• Leonid KantorovichBroader topic: Kantorovich developed the method to solve production and resource-allocation problems.

• Management scienceBroader topic: It models resource-allocation choices such as production planning and staffing.