Linked from

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

• Gaussian eliminationRelated: Its algorithms repeatedly solve linear systems involving constraints and candidate solution directions.

• Economic planningRelated: It formalizes how planners can allocate limited resources among competing goals.

• Linear equationRelated: Its equality constraints are linear equations defining feasible solutions.

• Convex functionRelated: Its development helped make convexity practically central to twentieth-century optimization.

• PolyhedronRelated: Its feasible regions are polyhedra, and bounded optima occur at extreme points.

• InequalityRelated: Its feasible region is defined by simultaneous inequalities.

• Assignment problemRelated: Binary assignment decisions have an integral linear-programming formulation.

• Extreme pointRelated: When an optimum exists on a polytope, some extreme point attains it.

• Carathéodory's theoremRelated: Extreme-point solutions and basic feasible solutions reflect dimension-bounded convex representations.

• Flow conservationRelated: Flow conservation equations are linear constraints in network optimization.

• Karush–Kuhn–Tucker conditionsRelated: KKT conditions specialize to the optimality relations behind linear-programming duality.

• Convex polygonRelated: In two variables, feasible regions are intersections of half-planes and may be convex polygons.

• Linear functionalRelated: Its objective is a linear functional evaluated on feasible vectors.

• Minimax theoremRelated: A finite zero-sum game can be solved through a pair of dual linear programs.

• Ford–Fulkerson algorithmRelated: A flow network’s maximum-flow problem can also be expressed as a linear program.

• Interior-point methodRelated: Interior-point methods became a major alternative to the simplex method for linear programs.

• Convex bodyRelated: Polyhedral convex bodies encode feasible regions and their extreme-point solutions.

• George DantzigRelated: Dantzig’s algorithm made this framework broadly usable, though this page links to it under mechanism.

• Helly's theoremRelated: Helly-type arguments yield bounds on infeasible constraint subsystems.

• Farkas' lemmaRelated: The lemma became a central tool for proving results about linear optimization.

• Radon's theoremRelated: Linear constraints can encode convex combinations and test whether two hulls intersect.

• Hungarian algorithmRelated: The assignment problem has a linear-programming formulation that underlies optimality arguments.

• Kepler conjectureRelated: Several finite estimates in the proof are certified through linear-programming bounds.

• Elementary algebraRelated: It applies linear equations and inequalities to constrained optimization.

• Ellipsoid methodRelated: Khachiyan's polynomial-time result concerned linear programming, even though the method is broader.

• Managerial economicsRelated: It can allocate limited labor, materials, or capacity across competing uses.

• Theory of computationRelated: Its polynomial-time solvability contrasts with the hardness of many discrete optimization problems.

• Balinski's theoremRelated: Polytope graphs encode adjacency among feasible solutions in linear programming.

• Margaret H. WrightRelated: Linear programming is a foundational optimization problem related to her broader algorithmic work.