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Attained signed duality for fractional triangle packings

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Erdos81.fractional_triangle_signed_duality

by Zexuan Liu · Sep 10, 2026 · Mathlib 0df444a (Lean v4.33.1)

chordal-graphscombinatoricsfractional-packinglinear-programmingtriangle-packing

Every finite simple graph GGG, including an edgeless graph, admits a feasible fractional triangle packing www and a feasible signed triangle dual yyy such that

m(G)−2∥w∥1=DG(y).m(G)-2\lVert w\rVert_1=D_G(y).m(G)−2∥w∥1​=DG​(y).

Here m(G)m(G)m(G) is the number of unordered edges, the packing is nonnegative with total load at most one through each edge, and DG(y)D_G(y)DG​(y) is the total signed edge weight. Dual feasibility requires an upper bound of one on each edge weight and on each triangle's total edge weight; negative edge weights are allowed.

The equality is the attained primal–dual certificate for the exact fractional partition problem with edge and triangle pieces. It provides the graph-specific interface between finite LP duality and chordal signed-weight estimates. No chordality or minimum-order hypothesis is required.

Formalization Note The platform already contains the proved general theorem SmaleNinth.lp_strong_duality. This target concerns its finite graph-incidence specialization, including the conversion to the existing packing representation.

Preamble
import Definitions.Def_Erdos81_triangle_packings
import Definitions.Def_Erdos81_signed_triangle_dual
Formal statement
theorem Erdos81.fractional_triangle_signed_duality {n : ℕ}
    (G : SimpleGraph (Fin n)) :
    ∃ w y : Finset (Fin n) → ℝ,
      Erdos81.IsFractionalTrianglePacking G w ∧
      Erdos81.IsSignedTriangleDual G y ∧
      (Erdos81.edgeCount G : ℝ) -
        2 * Erdos81.fractionalTrianglePackingWeight w = Erdos81.signedEdgeWeight G y := by
  sorry
Source
C22 candidate, Section 3, equations (3)–(4) and the finite attainment argument, https://github.com/vibemathing/problem-erdos-81-chordal-clique-partition/blob/09c2b6f3e277eb20fc34d0add65ee8d027c5bb37/research/artifacts/candidates/erdos81-a01-c22-c20-audit.md; finite LP input: Boyd and Vandenberghe, Convex Optimization, Section 5.2.1, p. 224, (5.17)–(5.20), and Section 5.2.3, pp. 226–227, https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf . The candidate's graph-specific specialization is an open formalization target, not an already kernel-verified result.

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