Counterexample · DS-GLOBAL-TIE-DEGENERACY

Global profiled optima can have degenerate ties

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Meaning

An exact witness exposes a tie class at a global optimum, limiting geometric conclusions.

Statement

An exact N=8, d=2, d_psi=1, K=3 sample has a 31-fold exactly tied global in-bin optimum (all feasible refinements of one reduced bipartition); every tied optimum has exactly coincident projected centroids, so zero first-order violations do not make an optimum compilable, and label-level optima are identified only up to Phi-neutral splits.

Where it stops

Refuted by

  • Counterexample fixtureCE-DS-DEGENERATE-GLOBAL-TIE-001

    An exact finite global profiled-Ds optimum is unique up to label permutation, keeps its efficient-projected centroids pairwise separated, and (when it satisfies its own first-order efficient-semimetric rule with zero violations) is reproducible from that rule.

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Local map of A finite global Ds optimum can be a degenerate exact tie class1 prerequisites to the left, 1 results it enables or questions it raises to the right, 1 boundary cases below.Variational form of the profiled objectiveVariational form of t…Margins dichotomy at global finite Ds optima (conditionally centered laws, scalar nuisance)Margins dichotomy at…A finite global Ds optimum can be a degenerate exact tie classA finite global Ds op…CE-DS-DEGENERATE-GLOBAL-TIE-001CE-DS-DEGENERATE-GLOB…
Prerequisites to the left, what it enables or raises to the right, boundary cases below. Open in the map.

The example

DS11. Variational form of the profiled objective and $\Phi$-neutral splits

Audit (28 Aug 2026, AUDITS/AUDIT-DS-POPULATION-BRIDGE-001.md): the boxed identity is classical — it is the extremal characterization of the generalized Schur complement (Krein 1947; Anderson's shorted operator 1971; Li–Mathias, SIAM Review 42(2), 2000, Thm 2.2, including the Loewner order, the pseudo-inverse, and the exact attainment set). The statistical reading V(B)=Var⁡(E[Sψ−BSλ∣Z])V(B)=\operatorname{Var}(E[S_\psi-BS_\lambda\mid Z]) additionally requires centered scores (E[S]=0E[S]=0). The binned transfer and consequences (a)–(d) are project-level.

For any partition with binned information blocks Iψψ,Iψλ,IλλI_{\psi\psi},I_{\psi\lambda},I_{\lambda\lambda} define the generalized profiled information Sψ+(I)=Iψψ−IψλIλλ+IλψS_\psi^+(I)=I_{\psi\psi}-I_{\psi\lambda}I_{\lambda\lambda}^+I_{\lambda\psi} (Moore–Penrose pseudo-inverse; the ordinary Schur complement when Iλλ≻0I_{\lambda\lambda}\succ0). Then

Sψ+(Iq)=min⁡BVar⁡ ⁣(E[Sψ−BSλ∣Z])=min⁡B∑bWb(μbψ−Bμbλ)(μbψ−Bμbλ)⊤,\boxed{ S_\psi^+(I_q) =\min_B\operatorname{Var}\!\bigl(E[S_\psi-BS_\lambda\mid Z]\bigr) =\min_B\sum_bW_b(\mu_{b\psi}-B\mu_{b\lambda})(\mu_{b\psi}-B\mu_{b\lambda})^\top, }

a Loewner minimum over dψ×dλd_\psi\times d_\lambda matrices BB, attained at every solution of BIλλ=IψλBI_{\lambda\lambda}=I_{\psi\lambda} (in particular Bq∗=IψλIλλ+B_q^*=I_{\psi\lambda}I_{\lambda\lambda}^+; solutions exist because range⁡(Iλψ)⊆range⁡(Iλλ)\operatorname{range}(I_{\lambda\psi})\subseteq\operatorname{range}(I_{\lambda\lambda}) for a PSD block matrix).

Proof. V(B)=Iψψ−BIλψ−IψλB⊤+BIλλB⊤V(B)=I_{\psi\psi}-BI_{\lambda\psi}-I_{\psi\lambda}B^\top +BI_{\lambda\lambda}B^\top; for any normal-equation solution B0B_0, completion of squares gives V(B)=V(B0)+(B−B0)Iλλ(B−B0)⊤⪰V(B0)V(B)=V(B_0)+(B-B_0)I_{\lambda\lambda}(B-B_0)^\top\succeq V(B_0), and V(B0)=Sψ+(I)V(B_0)=S_\psi^+(I). ∎

Consequences.

(a) Efficient-score domination, singular extension, and the exact equality condition (an earlier, since-closed question, fixed qq). Evaluating at B=Bfull∗B=B^*_{\rm full} re-derives DS7 in one line, extends it to singular binned nuisance blocks, and gives the exact gap

Var⁡(E[S^∣q])−Sψ+(Iq)=(Bfull∗−Bq∗) Iλλq (Bfull∗−Bq∗)⊤⪰0.\boxed{ \operatorname{Var}(E[\widehat S\mid q])-S_\psi^+(I_q) =(B^*_{\rm full}-B^*_q)\,I^q_{\lambda\lambda}\,(B^*_{\rm full}-B^*_q)^\top \succeq0 . }

Equality for a fixed qq holds iff (Bfull∗−Bq∗)Iλλq=0(B^*_{\rm full}-B^*_q)I^q_{\lambda\lambda}=0: the full-data nuisance projection is already optimal for the binned centroids. The finite-KK gap is therefore exactly the cost of estimating the nuisance projection from bins. Along any refining sequence of partitions generating the Borel σ\sigma-field, E[S∣ZK]→SE[S\mid Z_K]\to S in L2L^2 (Lévy upward martingale convergence), so IqK→IfullI_{q_K}\to I_{\rm full}; provided Iλλfull≻0I^{\rm full}_{\lambda\lambda}\succ0 (implicit in defining S^\widehat S), the binned nuisance blocks are eventually nonsingular, where B↦IψλIλλ−1B\mapsto I_{\psi\lambda}I_{\lambda\lambda}^{-1} is continuous, so BqK∗→Bfull∗B^*_{q_K}\to B^*_{\rm full} and the gap vanishes — the K→∞K\to\infty part of an earlier, since-closed question. (The pseudo-inverse map is discontinuous at rank drops — witness Iλλ(k)=diag(1,1/k)I_{\lambda\lambda}^{(k)}=\mathrm{diag}(1,1/k), Iψλ=[1,1]I_{\psi\lambda}=[1,1], Bk∗=[1,k]B^*_k=[1,k] — so the nonsingular-limit hypothesis is load-bearing; audit §8.) The behavior at global optima was an earlier, since-closed question and is now settled by DS15 for conditionally centered laws at dψ=dλ=1d_\psi=d_\lambda=1: the gap is the projection tax and vanishes a.s. along free global optima (DS15 conclusion 5); beyond that class it is OPEN-DS-MARGINS-NONCENTERED.

(b) Refinement monotonicity with exact equality characterization (dψ=1d_\psi=1). Splitting cell MM into (x,y)(x,y): for every slope cc, with eb(c)=μbψ−c μbλe_b(c)=\mu_{b\psi}-c\,\mu_{b\lambda},

V(c;split)=V(c;merged)+WxWyWM(ex(c)−ey(c))2.V(c;\text{split})=V(c;\text{merged}) +\frac{W_xW_y}{W_M}\bigl(e_x(c)-e_y(c)\bigr)^2 .

Hence Sψ+S_\psi^+ never decreases under refinement, and the split is profiled-information-neutral iff some cc simultaneously minimizes V(⋅;merged)V(\cdot;\text{merged}) and equalizes ex(c)=ey(c)e_x(c)=e_y(c). (Audit: the identity is the exact between-group variance decomposition; the "iff" holds by evaluating at a minimizer of the split problem, which exists by the normal-equation argument. The same statement and proof hold for every dψ≥1d_\psi\ge1 with the rank-one PSD gap WxWyWM(ex(B)−ey(B))(ex(B)−ey(B))⊤\frac{W_xW_y}{W_M}(e_x(B)-e_y(B))(e_x(B)-e_y(B))^\top and Loewner sandwiching — the form DS14's merged variant consumes; audit §8.)

(c) Wasted cells and exact global ties. If the merged configuration is entirely nuisance-degenerate (all cells have μbλ=0\mu_{b\lambda}=0, so V(⋅;merged)V(\cdot;\text{merged}) is constant and every slope minimizes it), every split whose sub-cells have distinct nuisance means is exactly neutral, while a split with equal (hence zero) nuisance means and distinct POI means strictly increases Sψ+S_\psi^+ but keeps the nuisance block singular — it leaves the in-bin formulation (DS9). (Audit hardening: the neutrality argument needs the minimizer set of V(⋅;merged)V(\cdot;\text{merged}) to meet the equalizing slopes; full nuisance degeneracy supplies that, a partially degenerate configuration need not.) Exact witnesses:

  • COUNTEREXAMPLES/CE-DS-DEGENERATE-GLOBAL-TIE-001.json: a centered equal-weight N=8,d=2,dψ=1,K=3N=8,d=2,d_\psi=1,K=3 sample whose exact global in-bin optimum 1083/40961083/4096 is attained by 31 distinct labelings — exactly the feasible refinements of one reduced bipartition — every one of which has two exactly coincident projected centroids; the unique nuisance-mean-equal refinement is infeasible (singular nuisance block) with generalized value 1191/4096>1083/40961191/4096>1083/4096.
  • COUNTEREXAMPLES/CE-DS-POP-WASTED-CELLS-001.json: the population/quadrature wasted-cell construction of DS12.

(d) Identifiability up to neutral splits. The profiled objective is invariant under Φ\Phi-neutral splits, so a finite global optimum is in general identified only up to the reduced configuration of projected centroids {(Wb,eb(Bq∗))}\{(W_b,e_b(B^*_q))\}; deployable content lives at the reduced level.

From Profiled Ds: from a sample to the population · chapter contents

Evidence: COUNTEREXAMPLES/CE-DS-DEGENERATE-GLOBAL-TIE-001.json. Measured, never theorem authority.