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Theorem 7.5 — iterating the Lovász–Schrijver operator ppp times reaches the integer hull, Np(K)=conv(K0)N^p(K) = \mathrm{conv}(K_0)Np(K)=conv(K0​)

Proved
Disjunctive.HigherDim.lovasz_schrijver_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 7.5 of Balas's Disjunctive Programming (cited to Lovász and Schrijver [99]). Let K={x:A~x≥b~}K = \{x : \tilde A x \ge \tilde b\}K={x:A~x≥b~} be the LP relaxation of a mixed 0-1 program with p=∣N′∣p = |N'|p=∣N′∣ 0-1 variables, K0=K∩{xj∈{0,1}, j∈N′}K_0 = K \cap \{x_j \in \{0,1\},\ j \in N'\}K0​=K∩{xj​∈{0,1}, j∈N′}, and N1(K)=N(K)N^1(K) = N(K)N1(K)=N(K), Nt(K)=N(Nt−1(K))N^t(K) = N(N^{t-1}(K))Nt(K)=N(Nt−1(K)). Then

Np(K)=conv(K0).N^p(K) = \mathrm{conv}(K_0).Np(K)=conv(K0​).

The iterates are given by linear systems (At,bt)(A_t, b_t)(At​,bt​), t=0,…,pt = 0,\dots,pt=0,…,p, with {x:A0x≥b0}=K\{x : A_0 x \ge b_0\} = K{x:A0​x≥b0​}=K and {x:At+1x≥bt+1}=N({x:Atx≥bt})\{x : A_{t+1}x \ge b_{t+1}\} = N(\{x : A_t x \ge b_t\}){x:At+1​x≥bt+1​}=N({x:At​x≥bt​}), every system containing the bound rows x≥0x \ge 0x≥0, xj≤1x_j \le 1xj​≤1 (j∈N′j \in N'j∈N′).

Formalization Note. The retired version (Open) allowed systems without the bound rows, the gap that falsified Theorems 7.1, 7.4, 7.6 and 7.7 of the same chapter; with no rows, N(K)=RnN(K) = \mathbb R^nN(K)=Rn and the claim fails already for p=1p = 1p=1. 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. Each Nt(K)⊆KN^t(K) \subseteq KNt(K)⊆K lies in the box, so a system for it containing the bound rows always exists (append them as redundant rows); with them the linearized system defining NNN coincides with Lovász and Schrijver's representation-free cone definition.

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 7.5 (Balas, *Disjunctive Programming*, Springer 2018, §7.2, p. 93, [99]): for
`K = {x : Ãx ≥ b̃}` the LP relaxation of a mixed 0-1 program with `p = |N'|` 0-1 variables,
`N^p(K) = conv(K₀)`, where `N¹(K) := N(K)` and `N^t(K) := N(N^{t-1}(K))`. The iterates are given
by systems `(A t, b t)`, `t = 0,…,p`, with `Poly (A 0) (b 0) = K` and
`Poly (A (t+1)) (b (t+1)) = N(Poly (A t) (b t))`. Every system contains the bound rows `x ≥ 0`,
`x_j ≤ 1` (`j ∈ N'`) (`HasBoundRows`), as `Ãx ≥ b̃` does in the book; each `N^t(K) ⊆ K` lies in
the box, so such a representation always exists (append the redundant bound rows), and it makes
the linearized system of `N` equal to Lovász and Schrijver's representation-free cone
formulation.
Corrected: the retired version allowed systems without the bound rows. -/
theorem lovasz_schrijver_reaches_hull_v2 {n p : ℕ} {mA : Fin (p + 1) → ℕ}
    (A : (t : Fin (p + 1)) → Matrix (Fin (mA t)) (Fin n) ℝ)
    (b : (t : Fin (p + 1)) → Fin (mA t) → ℝ) (Nprime : Finset (Fin n)) (hp : Nprime.card = p)
    (hK : ∀ t, HasBoundRows (A t) (b t) Nprime)
    (hStep : ∀ t : Fin p, Poly (A t.succ) (b t.succ) = NOp (A t.castSucc) (b t.castSucc) Nprime) :
    Poly (A (Fin.last p)) (b (Fin.last p)) = convexHull ℝ (K0Set (A 0) (b 0) Nprime) := by sorry

end Disjunctive.HigherDim
Source
E. Balas, Disjunctive Programming, Springer 2018, DOI 10.1007/978-3-030-00148-3, §7.2, p. 93, Theorem 7.5

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