Monotonicity conjecture for the optimal grouped MSE constant

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Let Δk\Delta_k denote the optimal asymptotic MSE constant within the generalized grouping (GG) family for groups of size kk. Monotonicity conjecture.

Δk≤Δk−1for all k≥2,\Delta_k \leq \Delta_{k-1}\quad\text{for all }k\geq 2,

i.e., the optimal asymptotic MSE constant within the GG family is non-increasing in kk. The conjecture is strongly supported by the numerical evidence and examples described in the paper, but a general proof remains open.

References

Primary source

Neri Merhav, “Grouped Reverse Importance Sampling for the Partition Function”, arXiv:2606.26748 (2026).

Progress summary

Refreshed
Claimed progress

The conjecture has only numerical support, while an unverified posted argument claims the optimization may be ill-posed and that a narrower fixed-shape version can fail.

Neri Merhav’s 2026 paper formulates the conjecture that the best asymptotic mean-squared-error constant in the generalized Gaussian family satisfies Δk≤Δk−1\Delta_k \leq \Delta_{k-1} for every k≥2k \geq 2. It explicitly leaves a general proof open.

Known results

  • The paper proves that product-form group weights cannot improve ordinary reverse importance sampling (Merhav, 2026).
  • It proves that, for non-overlapping groups, it suffices to optimize weights depending on total group energy (Merhav, 2026).
  • Numerical examples show the constant decreasing from about 0.08070.0807 at k=1k=1 to 0.01440.0144 at k=10k=10, with diminishing returns (Merhav, 2026).
  • Power-law energies yield a Gamma representation suggested as a possible route to a proof, but no proof is given (Merhav, 2026).

Posted attempt

An unverified argument claims that, under an unrestricted interpretation, the infimum is 00 for every kk but is generally unattained; it also claims a three-state example reverses monotonicity when the Gaussian shape is fixed at α=2\alpha=2. These are discussion claims and have not been independently verified.

Current status (as of August 2026): The conjecture remains unproved; the new posted argument is unverified and does not establish a verified counterexample to the stated conjecture.

Sources

Solutions 1

ProofThis solution needs a summarySee full solutionHide full solution

The unrestricted optimization has zero infimum and generally no minimum; the meaningful fixed-Gaussian monotonicity statement is false.

Use the definitions in Neri Merhav, Grouped Reverse Importance Sampling for the Partition Function, arXiv:2606.26748, Remark 2, equations (22), (24), and (30), and Conjecture 1. Let β>0\beta>0, let U≥0U\geq0, and let νk\nu_k be the group-energy counting measure or density of states. Assume

Ck=∫0∞e−βu dνk(u)=Z(β)k∈(0,∞).C_k=\int_0^\infty e^{-\beta u}\,d\nu_k(u) =Z(\beta)^k\in(0,\infty).

The generalized-Gaussian family and its objective are

mα,s(u)=exp⁡ ⁣(−uα2s),α>1,s>0,Qk(m)=Ck∫m(u)2eβu dνk(u)(∫m(u) dνk(u))2,Vk(m)=Qk(m)−1k.\begin{aligned} m_{\alpha,s}(u) &=\exp\!\left(-\frac{u^\alpha}{2s}\right), &&\alpha>1,\quad s>0,\\ Q_k(m) &=\frac{ C_k\displaystyle\int m(u)^2e^{\beta u}\,d\nu_k(u) }{ \left(\displaystyle\int m(u)\,d\nu_k(u)\right)^2 },\\ V_k(m)&=\frac{Q_k(m)-1}{k}. \end{aligned}

1. Universal collapse of the unrestricted optimum

Cauchy–Schwarz gives Qk(m)≥1Q_k(m)\geq1, with equality precisely when m(u)=ce−βum(u)=c e^{-\beta u} almost everywhere. Fix s=(2β)−1s=(2\beta)^{-1} and let α↓1\alpha\downarrow1. Set

I1(α)=∫e−βuα dνk(u),I2(α)=∫e−2βuα+βu dνk(u).I_1(\alpha)=\int e^{-\beta u^\alpha}\,d\nu_k(u), \qquad I_2(\alpha)=\int e^{-2\beta u^\alpha+\beta u}\,d\nu_k(u).

Since u−uα≤1u-u^\alpha\leq1 for u≥0u\geq0 and α>1\alpha>1,

e−βuα≤eβe−βu,e−2βuα+βu≤e2βe−βu.e^{-\beta u^\alpha}\leq e^\beta e^{-\beta u}, \qquad e^{-2\beta u^\alpha+\beta u} \leq e^{2\beta}e^{-\beta u}.

Dominated convergence consequently yields

I1(α),I2(α)⟶Ck,Qk(mα,(2β)−1)=CkI2(α)I1(α)2⟶1.I_1(\alpha),I_2(\alpha)\longrightarrow C_k, \qquad Q_k(m_{\alpha,(2\beta)^{-1}}) =\frac{C_kI_2(\alpha)}{I_1(\alpha)^2} \longrightarrow1.

Therefore, for every group size,

inf⁡α>1, s>0Vk(mα,s)=0(k≥1).\boxed{ \inf_{\alpha>1,\,s>0}V_k(m_{\alpha,s})=0 \qquad(k\geq1). }

Moreover, if νk\nu_k is supported on at least three distinct energies, equality cannot occur: it would require

uα2s−βu=constant\frac{u^\alpha}{2s}-\beta u=\text{constant}

on the support, although the left-hand side is strictly convex and can take any fixed value at no more than two points. Hence the minimum in the source's equation (24) does not exist. This includes its own example U(x)=∣x∣U(x)=|x| on R\mathbb R.

2. An explicit three-state boundary certificate

Take the source-admissible discrete system

X={0,1,2},U(j)=j,β=log⁡2,Z(β)=74.\mathcal X=\{0,1,2\}, \qquad U(j)=j, \qquad\beta=\log2, \qquad Z(\beta)=\frac74.

For n≥1n\geq1, choose

αn=log⁡2 ⁣(2+log⁡2 ⁣(1+1n))>1,s=12log⁡2.\alpha_n =\log_2\!\left(2+\log_2\!\left(1+\frac1n\right)\right)>1, \qquad s=\frac1{2\log2}.

Then the one-sample weights are

(mn(0),mn(1),mn(2))=(1,12,n4(n+1)),\left(m_n(0),m_n(1),m_n(2)\right) =\left(1,\frac12,\frac{n}{4(n+1)}\right),

and exact rational arithmetic gives

V1(mn)=6(7n+6)2>0,V1(mn)⟶0.V_1(m_n)=\frac{6}{(7n+6)^2}>0, \qquad V_1(m_n)\longrightarrow0.

Equality would require weights proportional to (1,1/2,1/4)(1,1/2,1/4). The values at energies 00 and 11 force (2s)−1=log⁡2(2s)^{-1}=\log2, while the value at energy 22 then forces α=1\alpha=1, which is excluded. Thus the infimum is zero but the minimum is absent.

3. Strict reversal for the fixed Gaussian family

The source's numerical Table 1 fixes α=2\alpha=2 and optimizes only ss. For this distinct substantive interpretation, write

Δk(2)=inf⁡s>0Vk(m2,s),t=e−1/(2s)∈(0,1).\Delta_k^{(2)}=\inf_{s>0}V_k(m_{2,s}), \qquad t=e^{-1/(2s)}\in(0,1).

For the same three-state system,

M1(t)=1+t+t4,N1(t)=1+2t2+4t8,V1(t)=74N1(t)M1(t)2−1.\begin{aligned} M_1(t)&=1+t+t^4,\\ N_1(t)&=1+2t^2+4t^8,\\ V_1(t)&=\frac74\frac{N_1(t)}{M_1(t)^2}-1. \end{aligned}

At t=7/10t=7/10,

Δ1(2)≤10454806376398801<7250.\Delta_1^{(2)} \leq\frac{10454806}{376398801} <\frac7{250}.

For two samples, the energy multiplicities are (1,2,3,2,1)(1,2,3,2,1), so

M2(t)=1+2t+3t4+2t9+t16,N2(t)=1+4t2+12t8+16t18+16t32,V2(t)=12(4916N2(t)M2(t)2−1).\begin{aligned} M_2(t)&=1+2t+3t^4+2t^9+t^{16},\\ N_2(t)&=1+4t^2+12t^8+16t^{18}+16t^{32},\\ V_2(t)&=\frac12\left( \frac{49}{16}\frac{N_2(t)}{M_2(t)^2}-1 \right). \end{aligned}

In fact,

V2(t)−7250=F(t)4000M2(t)2,V_2(t)-\frac7{250} =\frac{F(t)}{4000M_2(t)^2},

where

F(t)=95888t32−8448t25−12672t20+89552t18−8448t17−4224t16−25344t13−16896t10−8448t9+54492t8−25344t5−12672t4+16052t2−8448t+4013.\begin{aligned} F(t) ={}&95888t^{32}-8448t^{25}-12672t^{20}+89552t^{18} -8448t^{17}-4224t^{16}\\ &-25344t^{13}-16896t^{10}-8448t^9+54492t^8 -25344t^5-12672t^4\\ &+16052t^2-8448t+4013. \end{aligned}

To certify positivity globally, write F(t)=∑i=032aitiF(t)=\sum_{i=0}^{32}a_it^i. On each interval [j/8,(j+1)/8][j/8,(j+1)/8], use the Bernstein expansion

F ⁣(j+y8)=∑ℓ=032bj,ℓ(32ℓ)yℓ(1−y)32−ℓ,0≤y≤1,F\!\left(\frac{j+y}{8}\right) =\sum_{\ell=0}^{32} b_{j,\ell}\binom{32}{\ell}y^\ell(1-y)^{32-\ell}, \qquad 0\leq y\leq1,

whose coefficients are explicitly

bj,ℓ=∑r=0ℓ(ℓr)(32r)∑i=r32ai(ir)ji−r8i.b_{j,\ell} =\sum_{r=0}^{\ell} \frac{\binom{\ell}{r}}{\binom{32}{r}} \sum_{i=r}^{32}a_i\binom{i}{r}\frac{j^{i-r}}{8^i}.

Exact rational evaluation gives

j01234567min⁡ℓbj,ℓ>3203>2830>2682>2394>1601>312>12>2639.\begin{array}{c|rrrrrrrr} j&0&1&2&3&4&5&6&7\\ \hline \min_{\ell}b_{j,\ell} &>3203&>2830&>2682&>2394&>1601&>312&>12&>2639. \end{array}

Since the Bernstein basis is nonnegative and sums to 11, it follows that F(t)>12F(t)>12 throughout [0,1][0,1]. Also M2(t)≤9M_2(t)\leq9. Therefore

Δ2(2)≥7250+127000>7250>Δ1(2).\boxed{ \Delta_2^{(2)} \geq\frac7{250}+\frac1{27000} >\frac7{250} >\Delta_1^{(2)}. }

Thus the unrestricted infimum formulation is identically zero, the literal minimum formulation is generally undefined, and the practically motivated fixed-Gaussian monotonicity formulation is false.