I gave Claude’s new Fable AI the problem. Below is me walking through the response. Here is the log: https://claude.ai/share/826092ab-b1b5-4eef-95c0-c51c24b731b0.

Prompt Given:

Consider the following linear program:

where is the -identity matrix, is the boundary matrix for } for some simplicial complex embedded in , and are real non-negative vectors, with , and is a -chain. In the majority of cases, the optimal solution is integral; even if the boundary matrix is not totally unimodular. However, I was able to find one instance by playing with the weights in which simplex method gave a fractional solution. Forcing integral constraints gave an integral solution with the same objective value. Details on that example are listed here: https://notes.thrall.me/Daily/2026/2026-07-09. The example was constructed from a Mobius strip, as the presence of a Mobius strip enforces a non-TU boundary matrix.

Please characterize the set of solutions in which the optimal solution is integral. Make sure to prove your statements. You may work on this as long you’d like.


The feasibility region of the flat norm is given by

where . For and , denote by the submatrix of with rows and columns . Each vertex of is then indexed by a pair where is nonsingular. The vertex is integral if and only if is in the lattice .

We start by reducing the problem into an problem by eliminating the -variables:

Lemma 1: Define

Then the LP value is and the LP has integral optimal solution iff attains its minimum at .

Proof. Let be a feasible solution to the LP with objective , set and . Clearly, as

, and . Thus, the optimum is given by .

Vertices of

Lemma 2: A point of a standard-form polyhedron is a vertex iff the columns of indexed by its strictly positive coordinates are linearly independent.

Proof. Assume that the columns are linearly dependent, where . Thus, there is a vector with and . Consider for small . Then and for small enough . Thus, we found two vectors such that

Now assume that the columns are linearly independent. Let be feasible with . Notice that for , forces . Moreover, implies that . So . Since is linearly independent, we get . Thus, .

Lemma 3: A point is a vertex iff and for all and the submatrix has full rank, where and .

Proof. Note that the active columns (up to sign) are

Let and be real-vectors such that

Looking at rows () of the above sum, we get that . Thus, our active columns are linearly independent iff has trivial kernel, i.e., full rank. Now assume that for some , then for all we get implying that our active columns are not linearly independent. Thus, is not a vertex.

Lemma 4: Every vertex arises as follows: choose and with and , set

split into positive and negative parts. Conversely, every such choice produces a vertex of .

Proof. Let have full rank, pick independent rows where . To satisfy feasibility, we need , indeed, since we get

Thus, .

For the converse, let , and for , with . Notice that

Question: Why are the active columns linearly independent in the converse direction? Claude just says its the same argument as before.

Let be a vertex represented by as in Lemma 4. Define the lattice

By Lemma 4, it is clear that is integral iff as and for some . A fancier way of saying this is that the class vanishes in the finite abelian group

where a coset is given by

The order of this group is .

Proposition 5: Let be a vertex represented by as in Lemma 4 with . Then

  1. is integral iff
  2. The least common denominator of the entries of equals the order of in .
  3. If , then is integral.

Proof.

  1. See paragraph above.
  2. Let be the smallest integer such that . Then . The number here is the order by definition.
  3. Follows from Cramer’s rule:

where is with column replaced by .

Torsion

Claude simply cites Dey–Hirani–Krishnamoorthy’s Theorem 5.2.

Lemma 6: Let be a set of -simplices and a set of -simplices of . Let be the subcomplex given by the closure of . Let be the subcomplex given by the -skeleton of together with all -simplices of not in . Then

Characterization Theorems

Theorem 1: Fix . The following are equivalent:

  1. For every , the LP has an integral optimal solution;
  2. every vertex of is integral;
  3. for every with square nonsingular, in

Theorem 2: The following are equivalent:

  1. For every and all , the LP has an integral optimal solution;
  2. is totally unimodular
  3. is torsion-free for all subcomplex pairs .

Theorem 3: Fix . Parameterize objectives by with , and for a vertex let , so the cost of is . Then

  1. The instance has an integral optimum iff . In particular, if some optimal vertex has vanishing certificate , then an integral optimum exists.
  2. Each vertex owns a closed polyhedral cone and these finitely many cones cover the parameter space, and instances without integral optima occur only for .
  3. If is strictly positive and the optimal vertex is unique, then the optimal face is exactly , and the instance has an integral optimum iff for ‘s data.

The Counterexample

Claude also discussed the counterexample from 2026-07-09. Recall that our fractional optimum was

In the language above, is supported on 15 triangles and vanishes on 15 edges :

Our group corresponds to as is the smallest positive integer such that .

However, we did find that this solution tied with an integral solution. Consider our two solutions and . According to Theorem 3, there are two closed polyhedral cones:

Our example showed that . Theoretically, one could find a perturbation such that , that is, points in the direction of , but is small enough that it stays in the closed polyhedral cone.

The direction should be straightforward: if is sitting on a face of , then perturb normal to the face. However, this needs to be done without computing . The perturbation would also have to be done in a manner that does not change the objective. Hence, we would have to somehow extract .