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(iii″) — for K₂ = Q, condition (iii) says the columns of Y and the first-column differences lie in K

Proved
LovaszSchrijver.IntegerHull.mem_M_Q_iff_columns

by mikedeng1 · Sep 27, 2026 · Mathlib 0df444a (Lean v4.33.1)

convex-conelift-and-projectmatrix-conep2o-batch-p100bp2o-gran-per-chapterp2o-plan-paperp2o-v1

Let K⊆QK \subseteq QK⊆Q be a closed convex cone in Rn+1\mathbb R^{n+1}Rn+1, and let YYY be an (n+1)×(n+1)(n+1)\times(n+1)(n+1)×(n+1) real matrix that is symmetric and satisfies yii=y0iy_{ii} = y_{0i}yii​=y0i​ for 1≤i≤n1 \le i \le n1≤i≤n (conditions (i) and (ii)). Write YejY e_jYej​ for the jjjth column of YYY (0≤j≤n0 \le j \le n0≤j≤n). Then Y∈M(K,Q)Y \in M(K, Q)Y∈M(K,Q) if and only if

Yej∈K  (0≤j≤n)andYe0−Yei∈K  (1≤i≤n).Y e_j \in K \ \ (0 \le j \le n) \qquad\text{and}\qquad Y e_0 - Y e_i \in K \ \ (1 \le i \le n).Yej​∈K  (0≤j≤n)andYe0​−Yei​∈K  (1≤i≤n).

In the paper's words: "In this case, K2∗=Q∗K_2^* = Q^*K2∗​=Q∗ is generated by the vectors eie_iei​ and fif_ifi​ [where fi=e0−eif_i = e_0 - e_ifi​=e0​−ei​], and hence (iii′) has the following convenient form: (iii″) Every column of YYY is in KKK; the difference of the first column and any other column is in KKK."

This column description of M(K)M(K)M(K) is the working form of condition (iii) used in the proofs of Lemmas 1.3 and 1.5.

Formalization Note Coordinates are indexed by Option ι, with none the 0th coordinate; the "first column" is column none. The paper derives (iii″) from the rewriting (iii′) YK2∗⊆K1YK_2^* \subseteq K_1YK2∗​⊆K1​, which uses K∗∗=KK^{**} = KK∗∗=K; the paper tacitly takes KKK closed (polyhedral in all its applications), and the hypothesis IsClosed K is added for this reason.

Preamble
import Mathlib
import Definitions.Def_LovaszSchrijver_IntegerHull_Basic
import Definitions.Def_LovaszSchrijver_IntegerHull_MatrixCone
open Matrix Pointwise
Formal statement
namespace LovaszSchrijver.IntegerHull

theorem mem_M_Q_iff_columns {ι : Type} [Fintype ι] [DecidableEq ι]
    (K : Set (Option ι → ℝ)) (hK : IsConvexCone K) (hKc : IsClosed K) (hKQ : K ⊆ Q)
    (Y : Matrix (Option ι) (Option ι) ℝ) (hY_symm : Y.IsSymm)
    (hY_diag : ∀ i : ι, Y (some i) (some i) = Y none (some i)) :
    Y ∈ M K Q ↔
      (∀ j : Option ι, Y *ᵥ Pi.single j 1 ∈ K) ∧
        ∀ i : ι, Y *ᵥ Pi.single none 1 - Y *ᵥ Pi.single (some i) 1 ∈ K := by sorry

end LovaszSchrijver.IntegerHull
Source
Lovász and Schrijver, Cones of matrices and set-functions and 0–1 optimization, SIAM J. Optim. 1(2) (1991), p. 170, Section 1.a, (iii″)
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What the Lean code literally says, in plain math · claude-opus-5-5

Let ι\iotaι be a finite type with decidable equality, and let Vι=R{0}⊔ιV_\iota = \mathbb{R}^{\{0\}\sqcup\iota}Vι​=R{0}⊔ι with coordinates x0x_0x0​ and (xi)i∈ι(x_i)_{i\in\iota}(xi​)i∈ι​. Let Q⊆VιQ \subseteq V_\iotaQ⊆Vι​ be the set of all finite nonnegative combinations of vectors whose coordinates are all in {0,1}\{0,1\}{0,1} and whose 000-th coordinate is 111.

Let K⊆VιK \subseteq V_\iotaK⊆Vι​ satisfy:

  • KKK is nonempty and closed under addition and under multiplication by every real c≥0c \ge 0c≥0;
  • KKK is topologically closed;
  • K⊆QK \subseteq QK⊆Q.

Let YYY be a real matrix indexed by {0}⊔ι\{0\}\sqcup\iota{0}⊔ι such that:

  • YYY is symmetric;
  • Yii=Y0iY_{ii} = Y_{0i}Yii​=Y0i​ for every i∈ιi \in \iotai∈ι.

For j∈{0}⊔ιj \in \{0\}\sqcup\iotaj∈{0}⊔ι, let YejY e_jYej​ denote the jjj-th column of YYY.

The statement asserts:

Y∈M(K,Q)  ⟺  (Yej∈K for every j∈{0}⊔ι) and (Ye0−Yei∈K for every i∈ι).Y \in M(K,Q) \iff \Big(Y e_j \in K \text{ for every } j \in \{0\}\sqcup\iota\Big) \ \text{and}\ \Big(Y e_0 - Y e_i \in K \text{ for every } i \in \iota\Big).Y∈M(K,Q)⟺(Yej​∈K for every j∈{0}⊔ι) and (Ye0​−Yei​∈K for every i∈ι).

Here M(K,Q)M(K,Q)M(K,Q) is the set of symmetric matrices YYY with Yii=Y0iY_{ii} = Y_{0i}Yii​=Y0i​ for all i∈ιi \in \iotai∈ι and with

u⊤Yv≥0for all u∈K∗, v∈Q∗.u^\top Y v \ge 0 \quad \text{for all } u \in K^*,\ v \in Q^*.u⊤Yv≥0for all u∈K∗, v∈Q∗.

In this expression, K∗={u:u⊤x≥0 ∀x∈K}K^* = \{u : u^\top x \ge 0\ \forall x \in K\}K∗={u:u⊤x≥0 ∀x∈K}, and Q∗Q^*Q∗ is defined likewise. Because symmetry and the diagonal condition are assumed as hypotheses, the left-hand side holds exactly when the inequality condition holds.

Degenerate cases:

  • The hypotheses are satisfiable. For example, K={0}K = \{0\}K={0} and Y=0Y = 0Y=0 meet all of them.
  • K={0}K = \{0\}K={0}. Then K∗=VιK^* = V_\iotaK∗=Vι​, and the right-hand side requires every column of YYY to be 000.
  • Empty ι\iotaι. Then Vι=RV_\iota = \mathbb{R}Vι​=R, Q=[0,∞)Q = [0,\infty)Q=[0,∞), and KKK is {0}\{0\}{0} or [0,∞)[0,\infty)[0,∞). YYY is a 1×11\times11×1 matrix (y)(y)(y) on which the hypotheses impose nothing. The right-hand side reduces to y∈Ky \in Ky∈K, since the second conjunct is vacuous.
Human review
  • Endorsed by Shuze Chen · Sep 27, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Sep 27, 2026

    Confirmed by the mission captain (proposal self-audit).

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