Abstract

A set function $f$ on a finite set $V$ is submodular if $f(X) + f(Y) \geq f(X \cup Y) + f(X \cap Y)$ for any pair $X, Y \subseteq V$. The symmetric difference transformation (SD-transformation) of $f$ by a canonical set $S \subseteq V$ is a set function $g$ given by $g(X) = f(X \vartriangle S)$ for $X \subseteq V$, where $X \vartriangle S = (X \setminus S) \cup (S \setminus X)$ denotes the symmetric difference between $X$ and $S$. Submodularity and SD-transformations are regarded as the counterparts of convexity and affine transformations in a discrete space, respectively. However, submodularity is not preserved under SD-transformations, in contrast to the fact that convexity is invariant under affine transformations. This paper presents a characterization of SD-transformations preserving submodularity. Then, we are concerned with the problem of discovering a canonical set $S$, given the SD-transformation $g$ of a submodular function $f$ by $S$, provided that $g(X)$ is given by a function value oracle. A submodular function $f$ on $V$ is said to be strict if $f(X) + f(Y) > f(X \cup Y) + f(X \cap Y)$ holds whenever both $X \setminus Y$ and $Y \setminus X$ are nonempty. We show that the problem is solved by using $\mathrm{O}(|V|)$ oracle calls when $f$ is strictly submodular, although it requires exponentially many oracle calls in general.

Keywords

  1. submodular functions
  2. symmetric difference

MSC codes

  1. 90C27
  2. 52A41
  3. 26B25

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References

1.
F. Bach, Learning with submodular functions: A convex optimization perspective, Found. Trends Mach. Learn., 6 (2013), pp. 145--373.
2.
R. E. Bixby, W. H. Cunningham, and D. M. Topkis, The partial order of a polymatroid extreme point, Math. Oper. Res., 10 (1985), pp. 367--378.
3.
H. S. M. Coxeter, Regular Polytopes, Dover, New York, 1973.
4.
U. Feige, V. S. Mirrokni, and J. Vondrák, Maximizing non-monotone submodular functions, SIAM J. Comput., 40 (2011), pp. 1133--1153.
5.
S. Fujishige, Submodular Functions and Optimization, 2nd ed., Elsevier, Amsterdam, 2005.
6.
S. Fujishige, A Note on Submodular Function Minimization by Chubanov's LP Algorithm, Optimization online, 6217, 2017.
7.
J. A. Gillenwater, R. K. Iyer, B. Lusch, R. Kidambi, and J. A. Bilmes, Submodular Hamming metrics, in Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 2015), Curran, Red Hook, NJ, pp. 3141--3149.
8.
M. X. Goemans, N. J. A. Harvey, S. Iwata, and V. S. Mirrokni, Approximating submodular functions everywhere, in Proceedings of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2009), SIAM, Philadelphia, 2009, pp. 535--544.
9.
S. Iwata, L. Fleischer, and S. Fujishige, A combinatorial, strongly polynomial-time algorithm for minimizing submodular functions, J. ACM, 48 (2001), pp. 761--777.
10.
S. Iwata and J. B. Orlin, A simple combinatorial algorithm for submodular function minimization, in Proceedings of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2009), SIAM, Philadelphia, 2009, pp. 1230--1237.
11.
N. Kamiyama, A note on submodular function minimization with covering type linear constraints, Algorithmica, 80 (2018), pp. 2957--2971.
12.
Y. T. Lee, A. Sidford, and S. C. Wong, A faster cutting plane method and its implications for combinatorial and convex optimization, in Proceedings of the 56th Annual Symposium on Foundations of Computer Science (FOCS 2015), IEEE Computer Society, Los Alamitos, CA, pp. 1049--1065.
13.
L. Lovász, Submodular functions and convexity, in Mathematical Programming: The State of the Art, A. Bachem, B. Korte B., M. Grötschel, eds., Springer, Berlin, 1983, pp. 235--257.
14.
K. Murota, Discrete Convex Analysis, SIAM, Philadelphia, 2003.
15.
G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher, An analysis of approximations for maximizing submodular set functions I, Math. Program., 14 (1978), pp. 265--294.
16.
M. Queyranne, Minimizing symmetric submodular functions, Math. Program., 82 (1998), pp. 3--12.
17.
R. T. Rockafellar, Convex Analysis, Princeton University Press, Princeton, NJ, 1970.
18.
A. Schrijver, A combinatorial algorithm minimizing submodular functions in strongly polynomial time, J. Combin. Theory, Ser. B, 80 (2000), pp. 346--355.
19.
A. Schrijver, Combinatorial Optimization, Springer, Berlin, 2003.

Information & Authors

Information

Published In

cover image SIAM Journal on Discrete Mathematics
SIAM Journal on Discrete Mathematics
Pages: 571 - 585
ISSN (online): 1095-7146

History

Submitted: 7 February 2019
Accepted: 27 November 2019
Published online: 3 March 2020

Keywords

  1. submodular functions
  2. symmetric difference

MSC codes

  1. 90C27
  2. 52A41
  3. 26B25

Authors

Affiliations

Funding Information

Precursory Research for Embryonic Science and Technology https://doi.org/10.13039/501100009023 : JPMJPR16E4

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