The negative partial-sum conjecture for normalization of aggregated Markov-chain distributions
Let an aggregation of a Markov chain have approximated transient distribution and transient distribution , where , and let 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,
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
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 , 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
ProofThis solution needs a summarySee full solution
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 is optimal.
Let , , and let be an arbitrary probability vector. We ask when
holds for every such .
For , the exact criterion is
To prove this, put and for . Direct coordinatewise evaluation gives
For , let . Optimizing over the probability simplex gives exactly
Indeed, for each active set the summand is bounded by both available probability mass and total capacity; conversely, first allocate its baseline masses , then distribute the remaining mass up to these capacities. Therefore the normalization fails for some precisely when
equivalently when the boxed forbidden subset exists. In particular, always implies improvement.
Now set
If , the cases and are immediate. For , every positive-coordinate subset satisfies
Thus normalization improves the error universally whenever total negative mass is at least . In particular:
- A coordinate proves Conjecture 4.1.3.
- A sum of distinct coordinates at most implies , proving the indexed Conjecture 4.1.5.
For the remaining source conjecture, suppose some . Again only needs consideration. Any subset containing has sum at least
since the last quadratic has maximum . Any positive-coordinate subset omitting has sum at most
Hence no forbidden subset exists, proving Conjecture 4.1.4.
The constant is sharp: for every , take
Then , but
For completeness, gives equality identically. If , universal improvement occurs only for , or : testing first forces all coordinates positive and then for every ; 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.