Sums of squares based approximation algorithms for MAX-SAT.

*(English)*Zbl 1152.68058Summary: We investigate the Semidefinite Programming based sums of squares (SOS) decomposition method, designed for global optimization of polynomials, in the context of the (Maximum) Satisfiability problem. To be specific, we examine the potential of this theory for providing tests for unsatisfiability and providing MAX-SAT upper bounds. We compare the SOS approach with existing upper bound and rounding techniques for the MAX-2-SAT case of M. X. Goemans and D. P. Williamson [“Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming”, J. Assoc. Comput. Mach. 42, No. 6, 1115–1145 (1995; Zbl 0885.68088)] and U. Feige and M. X. Goemans [“Approximating the value of two prover proof systems, with applications to MAX2SAT and MAXDICUT”, in: Proceedings of the Third Israel Symposium on Theory of Computing and Systems. 182–189 (1995)] and the MAX-3-SAT case of H. Karloff and U. Zwick [“A 7/8-approximation algorithm for MAX 3SAT?”, in: Proceedings of the 38th Annual IEEE Symposium on Foundations of Computer Science, Miami Beach, FL, USA, IEEE Press, New York (1997)], which are based on Semidefinite Programming as well. We prove that for each of these algorithms there is an SOS-based counterpart which provides upper bounds at least as tight, but observably tighter in particular cases. Also, we propose a new randomized rounding technique based on the optimal solution of the SOS Semidefinite Program (SDP) which we experimentally compare with the appropriate existing rounding techniques. Further we investigate the implications to the decision variant SAT and compare experimental results with those yielded from the higher lifting approach of M. F. Anjos [“On semidefinite programming relaxations for the satisfiability problem”, Math. Methods Oper. Res. 60, No. 3, 349–367 (2004; Zbl 1087.90056); “An improved semidefinite programming relaxation for the satisfiability problem”, Math. Program. 102A, No. 3, 589–608 (2005; Zbl 1059.90117); “Semidefinite optimization approaches for satisfiability and maximum-satisfiability problems”, J. Satisf. Boolean Model. Comput. 1, No. 1, 1–47 (2006; Zbl 1194.90064)].

We give some impression of the fraction of the so-called unit constraints in the various SDP relaxations. From a mathematical viewpoint these constraints should be easily dealt within an algorithmic setting, but seem hard to be avoided as extra constraints in an SDP setting. Finally, we briefly indicate whether this work could have implications in finding counterexamples to uncovered cases in Hilbert’s Positivstellensatz.

We give some impression of the fraction of the so-called unit constraints in the various SDP relaxations. From a mathematical viewpoint these constraints should be easily dealt within an algorithmic setting, but seem hard to be avoided as extra constraints in an SDP setting. Finally, we briefly indicate whether this work could have implications in finding counterexamples to uncovered cases in Hilbert’s Positivstellensatz.

##### MSC:

68W25 | Approximation algorithms |

68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |

90C22 | Semidefinite programming |

##### Keywords:

maximum satisfiability; semidefinite programming; sums of squares; approximation algorithms
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\textit{H. van Maaren} et al., Discrete Appl. Math. 156, No. 10, 1754--1779 (2008; Zbl 1152.68058)

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##### References:

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