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A feasible linear program in standard form bounded below attains its minimum

Proved
Polyhedral.lp_min_attained

by Hartmann_Psi · Sep 27, 2026 · Mathlib 0df444a (Lean v4.33.1)

convex-geometrylinear-optimizationoperations-researchoptimization

Attainment for a linear program in standard form. Let W∈Rm×nW \in \mathbb{R}^{m\times n}W∈Rm×n, q∈Rnq \in \mathbb{R}^nq∈Rn and d∈Rmd \in \mathbb{R}^md∈Rm, and consider

min⁡  qTysubject toWy=d,  y≥0.\min \; q^{\mathsf T} y \qquad \text{subject to}\qquad W y = d,\ \ y \ge 0 .minqTysubject toWy=d,  y≥0.

If the feasible set is nonempty and the objective is bounded below on it, then the infimum is attained: some feasible y0y_0y0​ satisfies qTy0≤qTyq^{\mathsf T} y_0 \le q^{\mathsf T} yqTy0​≤qTy for every feasible yyy.

Unlike a continuous function on a compact set, a linear function on an unbounded polyhedron has no a priori reason to attain its infimum; that it does is a genuinely polyhedral phenomenon, and it is what allows optimal bases, complementary slackness and the simplex method to be discussed at all. The proof here goes through the closedness of the finitely generated cone spanned by the augmented columns (qj,W⋅j)∈R1+m(q_j, W_{\cdot j}) \in \mathbb{R}^{1+m}(qj​,W⋅j​)∈R1+m.

Formalization note. Boundedness below is stated as the existence of a single β\betaβ below all feasible objective values, and optimality of y0y_0y0​ as a pointwise inequality against all feasible yyy; no separate notion of "optimal value" is introduced.

Preamble
import Mathlib

open Matrix
Formal statement
theorem Polyhedral.lp_min_attained {m n : ℕ} (W : Matrix (Fin m) (Fin n) ℝ) (q : Fin n → ℝ)
    (d : Fin m → ℝ)
    (hfeas : ∃ y : Fin n → ℝ, (∀ j, 0 ≤ y j) ∧ W.mulVec y = d)
    (hbdd : ∃ beta : ℝ, ∀ y : Fin n → ℝ, (∀ j, 0 ≤ y j) → W.mulVec y = d → beta ≤ q ⬝ᵥ y) :
    ∃ y0 : Fin n → ℝ, (∀ j, 0 ≤ y0 j) ∧ W.mulVec y0 = d ∧
      ∀ y : Fin n → ℝ, (∀ j, 0 ≤ y j) → W.mulVec y = d → q ⬝ᵥ y0 ≤ q ⬝ᵥ y := by sorry
Source
D. Bertsimas and J. N. Tsitsiklis, Introduction to Linear Optimization, Athena Scientific 1997, Chapter 2 (Theorem 2.8) and Chapter 4

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