article
The complexity of general-valued CSPs
published
yes
Vladimir
Kolmogorov
author 3D50B0BA-F248-11E8-B48F-1D18A9856A87
Andrei
Krokhin
author
Michal
Rolinek
author 3CB3BC06-F248-11E8-B48F-1D18A9856A87
VlKo
department
Discrete Optimization in Computer Vision: Theory and Practice
project
An instance of the valued constraint satisfaction problem (VCSP) is given by a finite set of variables, a finite domain of labels, and a sum of functions, each function depending on a subset of the variables. Each function can take finite values specifying costs of assignments of labels to its variables or the infinite value, which indicates an infeasible assignment. The goal is to find an assignment of labels to the variables that minimizes the sum. We study, assuming that P 6= NP, how the complexity of this very general problem depends on the set of functions allowed in the instances, the so-called constraint language. The case when all allowed functions take values in f0;1g corresponds to ordinary CSPs, where one deals only with the feasibility issue, and there is no optimization. This case is the subject of the algebraic CSP dichotomy conjecture predicting for which constraint languages CSPs are tractable (i.e., solvable in polynomial time) and for which they are NP-hard. The case when all allowed functions take only finite values corresponds to a finitevalued CSP, where the feasibility aspect is trivial and one deals only with the optimization issue. The complexity of finite-valued CSPs was fully classified by Thapper and Živný. An algebraic necessary condition for tractability of a general-valued CSP with a fixed constraint language was recently given by Kozik and Ochremiak. As our main result, we prove that if a constraint language satisfies this algebraic necessary condition, and the feasibility CSP (i.e., the problem of deciding whether a given instance has a feasible solution) corresponding to the VCSP with this language is tractable, then the VCSP is tractable. The algorithm is a simple combination of the assumed algorithm for the feasibility CSP and the standard LP relaxation. As a corollary, we obtain that a dichotomy for ordinary CSPs would imply a dichotomy for general-valued CSPs.
SIAM2017
eng
SIAM Journal on Computing10.1137/16M1091836
4631087 - 1110
https://research-explorer.app.ist.ac.at/record/1637
Kolmogorov V, Krokhin A, Rolinek M. 2017. The complexity of general-valued CSPs. SIAM Journal on Computing. 46(3), 1087–1110.
V. Kolmogorov, A. Krokhin, M. Rolinek, SIAM Journal on Computing 46 (2017) 1087–1110.
Kolmogorov, Vladimir, Andrei Krokhin, and Michal Rolinek. “The Complexity of General-Valued CSPs.” <i>SIAM Journal on Computing</i>. SIAM, 2017. <a href="https://doi.org/10.1137/16M1091836">https://doi.org/10.1137/16M1091836</a>.
Kolmogorov, Vladimir, et al. “The Complexity of General-Valued CSPs.” <i>SIAM Journal on Computing</i>, vol. 46, no. 3, SIAM, 2017, pp. 1087–110, doi:<a href="https://doi.org/10.1137/16M1091836">10.1137/16M1091836</a>.
Kolmogorov V, Krokhin A, Rolinek M. The complexity of general-valued CSPs. <i>SIAM Journal on Computing</i>. 2017;46(3):1087-1110. doi:<a href="https://doi.org/10.1137/16M1091836">10.1137/16M1091836</a>
Kolmogorov, V., Krokhin, A., & Rolinek, M. (2017). The complexity of general-valued CSPs. <i>SIAM Journal on Computing</i>. SIAM. <a href="https://doi.org/10.1137/16M1091836">https://doi.org/10.1137/16M1091836</a>
V. Kolmogorov, A. Krokhin, and M. Rolinek, “The complexity of general-valued CSPs,” <i>SIAM Journal on Computing</i>, vol. 46, no. 3. SIAM, pp. 1087–1110, 2017.
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