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The 14 pages that link to Feasible region, each with the reason it gives.

• Linear programmingRelated: Linear constraints carve out the region in which every permissible solution lies.

• Objective functionRelated: Only decisions in this region are candidates for optimizing the objective.

• Combinatorial optimizationRelated: Discrete constraints determine which combinations can compete for optimality.

• Constrained optimizationRelated: The optimum must lie in this set, often on its boundary.

• Linear programming dualityRelated: Primal and dual feasibility define the solutions compared by duality.

• Karush–Kuhn–Tucker conditionsRelated: Primal feasibility requires a KKT candidate to lie in this set.

• Mathematical optimizationRelated: Optimization searches this set rather than every possible decision.

• Interior-point methodRelated: The method’s defining approach takes place inside this set rather than along its boundary.

• Optimization problemRelated: Only points in this region can qualify as solutions.

• Shadow priceRelated: Relaxing a constraint can enlarge this set and improve the best attainable objective.

• Linear programming relaxationRelated: A relaxation enlarges this set while preserving the original feasible points.

• Nonlinear programmingRelated: Nonlinear constraints can make this set curved, disconnected, or empty.

• Numerical optimizationRelated: Constraints define which points an optimization algorithm is permitted to consider.

• Farkas' lemmaRelated: The lemma distinguishes a nonempty feasible region from a certifiable empty one.