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Source. Theorem 4.11, p. 35, with the definition of -fold matrices, pp. 6 and 31, of Shmuel Onn, Convex Discrete Optimization, arXiv:math/0703575v1 [math.OC] (20 March 2007), published in the Encyclopedia of Optimization (2009), 513--550, as identified on the source card. Labels and pages are those of the arXiv preprint.
Setting
An matrix is a matrix with rows and columns whose first rows form and last rows form . Its -fold matrix is the matrix
with copies of side by side in the top rows and copies of down the block diagonal below (pp. 6 and 31). Bounds take values in . Solves has the meaning recalled on the Theorem 2.4 page: an optimal solution, or an assertion of infeasibility or of unboundedness.
Statement
Theorem 4.11 (p. 35). Fix an integer matrix . There is a polynomial time algorithm that, given , bounds , and $b\in\mathbb Z^{r+ns}$, with input encoded as , solves
The overview (p. 7) states it as: for every fixed integer matrix , the linear -fold integer programming problem with any , , , and can be solved in polynomial time. The paper calls it the main result of Section 4 (p. 35).
Proof pointer
P. 35, combining Lemma 4.9 (p. 34), which turns a feasible point into an optimal one, and Lemma 4.10 (pp. 34--35), which finds a feasible point or reports none through an auxiliary -fold program with slack columns. Lemma 4.9 computes by Theorem 4.7 (p. 32) and then augments along Graver basis elements (Theorem 4.4, p. 30, resting on Lemma 4.2). Theorem 4.7 rests on Lemma 4.6 (p. 32): the Graver complexity of , the largest number of nonzero blocks in an element of any , is finite, so for at least that number is a union of embedded copies of , with the Graver complexity, and has elements.
Dependencies
Lemmas 4.6, 4.9 and 4.10, Theorems 4.4 and 4.7, and Lemma 4.2. Read depth: claims checked; the statement and the definition of were read clause by clause, the proofs for their structure.
Bears on
No Erdős problem in the corpus.