Linked from

The 14 pages that link to Linear programming duality, each with the reason it gives.

• Assignment problemRelated: Dual variables provide lower bounds that can certify an assignment's minimum cost.

• Minimax theoremRelated: Duality later gave an optimization-based route to the game’s equal guaranteed values.

• Transportation problemRelated: Dual values explain the marginal value of supply and demand constraints.

• Shadow priceRelated: An optimal dual solution supplies shadow prices for the primal constraints.

• Linear programming relaxationRelated: Dual solutions can certify bounds supplied by a relaxation.

• Tjalling KoopmansRelated: Dual variables give scarce resources implicit values in allocation models.

• George DantzigRelated: Duality supplies optimality certificates and interprets constraints through associated prices.

• Max-flow min-cut theoremRelated: The max-flow min-cut equality is a discrete instance of primal-dual equality.

• Existence theoremRelated: Existence and attainment results help establish when optimal solutions are realized.

• Farkas' lemmaRelated: Dual feasible solutions often act as the lemma's infeasibility certificates.

• Hungarian algorithmRelated: Dual variables explain why adjusted costs certify that a matching is optimal.

• Kőnig's theoremRelated: The theorem is a combinatorial instance of equal optimal values in a primal-dual pair.

• Bondareva–Shapley theoremRelated: Duality connects balanced weights to the feasibility of core allocations.

• Hyperplane separation theoremRelated: Separation yields dual certificates for bounds and infeasibility in linear programs.