The negative partial-sum conjecture for normalization of aggregated Markov-chain distributions

Let an aggregation of a Markov chain have approximated transient distribution p~k\tilde{p}_k and transient distribution pkp_k, where k∈Nk \in \mathbb{N}, and let nn be the number of coordinates. The inequality referred to in the source as

compares the normalized approximation with the unnormalized approximation. **Negative \partial-\sum conjecture.** If there are pairwise different indices $i_1, \dots, i_{n-1}$ such that

\sum_{j=1}^{n-1}\tilde{p}_k(i_j) \leq -1,

thenthen

holds true.

This is one of three explicitly described as unproven postulations in the paper. Its precise mathematical status beyond the paper is not established by the supplied material.

References

Primary source

Patrick Sonnentag, “Finding the Smallest Possible Exact Aggregation of a Markov Chain”, arXiv:2507.11157 (2025).

Progress summary

Refreshed
Claimed solved

A reader-provided complete proof claims the conjecture follows from a sharper criterion, but nobody has independently verified it.

The conjecture asserts that a sufficiently negative partial sum over all but one coordinate guarantees the paper’s normalization error inequality. Patrick Sonnentag’s 2025 paper lists it among three explicitly unproved conjectures.

Posted attempt

An unverified reader-provided attempt claims a necessary-and-sufficient subset criterion for the underlying normalization inequality and uses it to prove the indexed conjecture, along with the other two conjectures in the paper. It claims a complete result, including optimality of the positive-coordinate threshold 9/89/8, but the argument has not been independently checked.

Current status (as of August 2026): The conjecture has a complete proof claim but no verified proof; its mathematical status remains unsettled.

Sources

Solutions 1

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The indexed conjecture holds, as do both other unproved normalization conjectures in the same source. In fact there is a sharp necessary-and-sufficient criterion, and the source's positive-coordinate constant 9/89/8 is optimal.

Let x∈Rd∖{0}x\in\mathbb R^d\setminus\{0\}, a=∥x∥1a=\|x\|_1, and let pp be an arbitrary probability vector. We ask when

∥xa−p∥1≤∥x−p∥1\left\|\frac{x}{a}-p\right\|_1 \le \|x-p\|_1

holds for every such pp.

For a>1a>1, the exact criterion is

There is no S⊆{i:xi>0} satisfying a2<∑i∈Sxi<a(3−a)2.\boxed{ \text{There is no }S\subseteq\{i:x_i>0\} \text{ satisfying } \frac a2 < \sum_{i\in S}x_i < \frac{a(3-a)}2. }

To prove this, put ℓi=xi/a\ell_i=x_i/a and ci=(a−1)ℓic_i=(a-1)\ell_i for xi>0x_i>0. Direct coordinatewise evaluation gives

∥x−p∥1−∥xa−p∥1=a−1−2∑i:xi>0min⁡((pi−ℓi)+,ci).\|x-p\|_1- \left\|\frac{x}{a}-p\right\|_1 = a-1 - 2\sum_{i:x_i>0} \min\bigl((p_i-\ell_i)_+,c_i\bigr).

For S⊆{i:xi>0}S\subseteq\{i:x_i>0\}, let LS=∑i∈SℓiL_S=\sum_{i\in S}\ell_i. Optimizing over the probability simplex gives exactly

sup⁡p∑i:xi>0min⁡((pi−ℓi)+,ci)=max⁡S:LS≤1min⁡{1−LS,(a−1)LS}.\sup_p \sum_{i:x_i>0} \min\bigl((p_i-\ell_i)_+,c_i\bigr) = \max_{S:L_S\le1} \min\{1-L_S,(a-1)L_S\}.

Indeed, for each active set the summand is bounded by both available probability mass and total capacity; conversely, first allocate its baseline masses ℓi\ell_i, then distribute the remaining mass up to these capacities. Therefore the normalization fails for some pp precisely when

12<LS<3−a2,\frac12<L_S<\frac{3-a}{2},

equivalently when the boxed forbidden subset exists. In particular, a≥2a\ge2 always implies improvement.

Now set

N−=∑xi<0(−xi),P+=∑xi>0xi,a=P++N−.N_-=\sum_{x_i<0}(-x_i), \qquad P_+=\sum_{x_i>0}x_i, \qquad a=P_++N_-.

If N−≥1N_-\ge1, the cases a=1a=1 and a≥2a\ge2 are immediate. For 1<a<21<a<2, every positive-coordinate subset satisfies

∑i∈Sxi≤P+=a−N−≤a−1<a2.\sum_{i\in S}x_i\le P_+=a-N_-\le a-1<\frac a2.

Thus normalization improves the error universally whenever total negative mass is at least 11. In particular:

  • A coordinate xi≤−1x_i\le-1 proves Conjecture 4.1.3.
  • A sum of d−1d-1 distinct coordinates at most −1-1 implies N−≥1N_-\ge1, proving the indexed Conjecture 4.1.5.

For the remaining source conjecture, suppose some xj=v≥9/8x_j=v\ge9/8. Again only 1<a<21<a<2 needs consideration. Any subset containing jj has sum at least

v≥98≥a(3−a)2,v\ge\frac98\ge\frac{a(3-a)}2,

since the last quadratic has maximum 9/89/8. Any positive-coordinate subset omitting jj has sum at most

a−v<a2.a-v<\frac a2.

Hence no forbidden subset exists, proving Conjecture 4.1.4.

The constant is sharp: for every 1<v<9/81<v<9/8, take

x=(v−3/2,v),p=(0,1).x=(v-3/2,v),\qquad p=(0,1).

Then a=3/2a=3/2, but

∥x−p∥1=12,∥xa−p∥1=2−4v3>12.\|x-p\|_1=\frac12, \qquad \left\|\frac xa-p\right\|_1 = 2-\frac{4v}{3} > \frac12.

For completeness, a=1a=1 gives equality identically. If 0<a<10<a<1, universal improvement occurs only for d=1,x=(a)d=1,x=(a), or d=2,x=(a/2,a/2)d=2,x=(a/2,a/2): testing p=ejp=e_j first forces all coordinates positive and then xj≥a/2x_j\ge a/2 for every jj; the two-dimensional equal-coordinate case follows from the triangle inequality.

Source: P. Sonnentag, “Finding the Smallest Possible Exact Aggregation of a Markov Chain,” arXiv:2507.11157, equation (4.1) and Conjectures 4.1.3–4.1.5.