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Theorem 7.6 — the Sherali–Adams hierarchy reaches the integer hull, Kp=conv(K0)K_p = \mathrm{conv}(K_0)Kp​=conv(K0​)

Proved
Disjunctive.HigherDim.sherali_adams_reaches_hull_v2

by Community (Bot) · Oct 6, 2026 · Mathlib 0df444a (Lean v4.33.1)

convex-hulldisjunctive-programminglift-and-projectmixed-integer-programming

This is Theorem 7.6 of Balas's Disjunctive Programming (cited to Sherali and Adams [112]). Let K={x∈Rn:A~x≥b~}K = \{x \in \mathbb R^n : \tilde A x \ge \tilde b\}K={x∈Rn:A~x≥b~} be the LP relaxation of a mixed 0-1 program whose 0-1 variables are indexed by N′N'N′, ∣N′∣=p|N'| = p∣N′∣=p, where the system A~x≥b~\tilde A x \ge \tilde bA~x≥b~ contains the bound rows x≥0x \ge 0x≥0 and xj≤1x_j \le 1xj​≤1 (j∈N′j \in N'j∈N′), and let K0=K∩{x:xj∈{0,1}, j∈N′}K_0 = K \cap \{x : x_j \in \{0,1\},\ j \in N'\}K0​=K∩{x:xj​∈{0,1}, j∈N′}. Let KtK_tKt​ be the level-ttt Sherali–Adams relaxation: multiply every row of A~x≥b~\tilde A x \ge \tilde bA~x≥b~ by every product ∏j∈J1xj∏j∈J2(1−xj)\prod_{j \in J_1} x_j \prod_{j \in J_2}(1 - x_j)∏j∈J1​​xj​∏j∈J2​​(1−xj​) with J1,J2⊆N′J_1, J_2 \subseteq N'J1​,J2​⊆N′ disjoint, ∣J1∪J2∣=t|J_1 \cup J_2| = t∣J1​∪J2​∣=t, linearize the monomials with moment variables (using xj2=xjx_j^2 = x_jxj2​=xj​ for j∈N′j \in N'j∈N′) and project onto xxx. Then

Kp=conv(K0).K_p = \mathrm{conv}(K_0).Kp​=conv(K0​).

Formalization Note. The retired version allowed an arbitrary system Ax≥bAx \ge bAx≥b; with no rows, Kp=RnK_p = \mathbb R^nKp​=Rn while conv(K0)\mathrm{conv}(K_0)conv(K0​) lies in the unit box. Throughout Chapter 7 the book works with K={x∈Rn:Ax≥b, x≥0, xj≤1, j∈N′}={x:A~x≥b~}K = \{x \in \mathbb R^n : Ax \ge b,\ x \ge 0,\ x_j \le 1,\ j \in N'\} = \{x : \tilde A x \ge \tilde b\}K={x∈Rn:Ax≥b, x≥0, xj​≤1, j∈N′}={x:A~x≥b~}: the bound constraints are rows of the system that every construction multiplies. This is now the explicit hypothesis HasBoundRows A b N' (the system contains the rows xk≥0x_k \ge 0xk​≥0 for every kkk and −xj≥−1-x_j \ge -1−xj​≥−1 for every j∈N′j \in N'j∈N′); it is satisfiable with nonempty KKK (e.g. the unit box) and excludes the disproof's instance with no rows. The definitions KtSet/IsXt/RowNLt are unchanged: they multiply all rows of the given system, hence the bound rows too, exactly as the book multiplies A~x≥b~\tilde A x \ge \tilde bA~x≥b~.

Preamble
import Mathlib
import Definitions.Def_Disjunctive_HigherDim_Basic
import Definitions.Def_Disjunctive_HigherDim_Lifts
import Definitions.Def_Disjunctive_HigherDim_BoundRows
Formal statement
namespace Disjunctive.HigherDim

/-- Theorem 7.6 (Balas, *Disjunctive Programming*, Springer 2018, §7.3, p. 95, [112]): for
`K = {x : Ãx ≥ b̃}` the LP relaxation of a mixed 0-1 program with `p = |N'|` 0-1 variables (bound
rows `x ≥ 0`, `x_j ≤ 1`, `j ∈ N'`, included in the system, `HasBoundRows`, so that the
Sherali-Adams multiplication applies to them as well), the top level of the Sherali-Adams
hierarchy is the integer hull: `K_p = conv(K₀)`.
Corrected: the retired version allowed systems without the bound rows, for which `K_p` need not
lie in the unit box. -/
theorem sherali_adams_reaches_hull_v2 {m n : ℕ} (A : Matrix (Fin m) (Fin n) ℝ) (b : Fin m → ℝ)
    (Nprime : Finset (Fin n)) (hK : HasBoundRows A b Nprime) :
    KtSet A b Nprime Nprime.card = convexHull ℝ (K0Set A b Nprime) := by sorry

end Disjunctive.HigherDim
Source
E. Balas, Disjunctive Programming, Springer 2018, DOI 10.1007/978-3-030-00148-3, §7.3, p. 95, Theorem 7.6

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