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On the Optimality of Pseudo-polynomial Algorithms for Integer Programming

Authors Fedor V. Fomin, Fahad Panolan, M. S. Ramanujan, Saket Saurabh

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Author Details

Fedor V. Fomin
  • Department of Informatics, University of Bergen, Norway
Fahad Panolan
  • Department of Informatics, University of Bergen, Norway
M. S. Ramanujan
  • University of Warwick, United Kingdom
Saket Saurabh
  • Institute of Mathematical Sciences, HBNI, Chennai, India and University of Bergen, Norway

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Fedor V. Fomin, Fahad Panolan, M. S. Ramanujan, and Saket Saurabh. On the Optimality of Pseudo-polynomial Algorithms for Integer Programming. In 26th Annual European Symposium on Algorithms (ESA 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 112, pp. 31:1-31:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.ESA.2018.31

Abstract

In the classic Integer Programming (IP) problem, the objective is to decide whether, for a given m x n matrix A and an m-vector b=(b_1,..., b_m), there is a non-negative integer n-vector x such that Ax=b. Solving (IP) is an important step in numerous algorithms and it is important to obtain an understanding of the precise complexity of this problem as a function of natural parameters of the input. The classic pseudo-polynomial time algorithm of Papadimitriou [J. ACM 1981] for instances of (IP) with a constant number of constraints was only recently improved upon by Eisenbrand and Weismantel [SODA 2018] and Jansen and Rohwedder [ArXiv 2018]. We continue this line of work and show that under the Exponential Time Hypothesis (ETH), the algorithm of Jansen and Rohwedder is nearly optimal. We also show that when the matrix A is assumed to be non-negative, a component of Papadimitriou's original algorithm is already nearly optimal under ETH. This motivates us to pick up the line of research initiated by Cunningham and Geelen [IPCO 2007] who studied the complexity of solving (IP) with non-negative matrices in which the number of constraints may be unbounded, but the branch-width of the column-matroid corresponding to the constraint matrix is a constant. We prove a lower bound on the complexity of solving (IP) for such instances and obtain optimal results with respect to a closely related parameter, path-width. Specifically, we prove matching upper and lower bounds for (IP) when the path-width of the corresponding column-matroid is a constant.

Subject Classification

ACM Subject Classification
  • Theory of computation → Integer programming
Keywords
  • Integer Programming
  • Strong Exponential Time Hypothesis
  • Branch-width of a matrix
  • Fine-grained Complexity

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References

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