Randomized Algorithms for 3-SAT

Schoning proposed a simple yet efficient randomized algorithm for solving the k-SAT problem. In the case of 3-SAT, the algorithm has an expected running time of poly(n)·(4/3)ⁿ = O(1.334ⁿ). In this paper we present randomized algorithms and show that one of them has O(1.3302ⁿ) expected running time, improving Schoning's algorithm. (Note. At this point, the fastest randomized algorithm for 3-SAT is the one given by Iwama and Tamaki that runs in O(1.324ⁿ).)     

Satisfiability with Index Dependency

We study the Boolean satisfiability problem (SAT) restricted on input formulas for which there are linear arithmetic constraints imposed on the indices of variables occurring in the same clause.This can be seen as a structural counterpart of Schaefer’s dichotomy theorem which studies the SAT problem with additional constraints on the assigned values of variables in the same clause.More precisely,let k-SAT(m,A) denote the SAT problem restricted on instances of k-CNF formulas,in every clause of which the indices of the last k m variables are totally decided by the first m ones through some linear equations chosen from A.For example,if A contains i3 = i1 + 2i2 and i4 = i2 i1 + 1,then a clause of the input to 4-SAT(2,A) has the form yi1 ∨ yi2 ∨ yi1+2i2 ∨ yi2 i1+1,with yi being xi or xi.We obtain the following results: 1) If m 2,then for any set A of linear constraints,the restricted problem k-SAT(m,A) is either in P or NP-complete assuming P = NP.Moreover,the corresponding #SAT problem is always #P-complete,and the Max-SAT problem does not allow a polynomial time approximation scheme assuming P = NP.2) m = 1,that is,in every clause only one index can be chosen freely.In this case,we develop a general framework together with some techniques for designing polynomial-time algorithms for the restricted SAT problems.Using these,we prove that for any A,#2-SAT(1,A) and Max-2-SAT(1,A) are both polynomial-time solvable,which is in sharp contrast with the hardness results of general #2-SAT and Max-2-SAT.For fixed k 3,we obtain a large class of non-trivial constraints A,under which the problems k-SAT(1,A),#k-SAT(1,A) and Max-k-SAT(1,A) can all be solved in polynomial time or quasi-polynomial time.     

Quantum cooperative search algorithm for 3-SAT

Grover's search algorithm, one of the most popular quantum algorithms, provides a good solution to solve NP complexity problems, but requires a large number of quantum bits (qubits) for its functionality. In this paper, a novel algorithm called quantum cooperative search is proposed to make Grover's search algorithm work on 3-SAT problems with a small number of qubits. The proposed algorithm replaces some qubits with classical bits and finds assignments to these classical bits using the traditional 3-SAT algorithms including evolutionary algorithms and heuristic local search algorithms. In addition, the optimal configuration of the proposed algorithm is suggested by mathematical analysis. The experimental results show that the quantum cooperative search algorithm composed by Grover's search and heuristic local search performs better than other pure traditional 3-SAT algorithms in most cases. The mathematical analysis of the appropriate number of qubits is also verified by the experiments.     

The Minimum Range Assignment Problem on Linear Radio Networks

Given a set S of radio stations located on a line and an integer h ≥ 1 , the MIN ASSIGNMENT problem consists in finding a range assignment of minimum power consumption provided that any pair of stations can communicate in at most h hops. Previous positive results for this problem are only known when h=|S|-1 or in the uniform chain case (i.e., when the stations are equally spaced). As for the first case, Kirousis et al. [7] provided a polynomial-time algorithm while, for the second case, they derive a polynomial-time approximation algorithm. This paper presents the first polynomial-time, approximation algorithm for the MIN ASSIGNMENT problem. The algorithm guarantees a 2-approximation ratio and runs in O(hn 3 ) time. We also prove that, for fixed h and for ``well spaced'' instances (a broad generalization of the uniform chain case), the problem admits a polynomial-time approximation scheme . This result significantly improves over the approximability result given by Kirousis {et al}. Both our approximation results are obtained via new algorithms that exactly solve two natural variants of the MIN ASSIGNMENT problem: the problem in which every station must reach a fixed one in at most h hops and the problem in which the goal is to select a subset of bases such that all the other stations must reach one base in at most h-1 hops. Finally, we show that for h=2 the MIN ASSIGNMENT problem can be exactly solved in O(n 3 ) time.     

Derandomizing the HSSW Algorithm for 3-SAT

We present a (full) derandomization of HSSW algorithm for 3-SAT, proposed by Hofmeister, Schöning, Schuler, and Watanabe (in STACS 2002, pp. 192–202, 2002). Thereby, we obtain an O(1.3303 ⁿ )-time deterministic algorithm for 3-SAT, which is currently fastest.     

Almost 2-SAT is fixed-parameter tractable

We consider the following problem. Given a 2-cnf formula, is it possible to remove at most k clauses so that the resulting 2-cnf formula is satisfiable? This problem is known to different research communities in theoretical computer science under the names Almost 2-SAT, All-but-k 2-SAT, 2-cnf deletion, and 2-SAT deletion. The status of the fixed-parameter tractability of this problem is a long-standing open question in the area of parameterized complexity. We resolve this open question by proposing an algorithm that solves this problem in O(k¹⁵×k×m³) time showing that this problem is fixed-parameter tractable.     

Approximation algorithm for the weighted flow-time minimization on a single machine with a fixed non-availability interval

In this article, we consider the non-resumable case of the single machine scheduling problem with a fixed non-availability interval. We aim to minimize the weighted sum of completion times. No polynomial 2-approximation algorithm for this problem has been previously known. We propose a 2-approximation algorithm with O(n²) time complexity where n is the number of jobs. We show that this bound is tight. The obtained result outperforms all the previous polynomial approximation algorithms for this problem.     

The Minimum Range Assignment Problem on Linear Radio Networks

Abstract. Given a set S of radio stations located on a line and an integer h ≥ 1 , the MIN ASSIGNMENT problem consists in finding a range assignment of minimum power consumption provided that any pair of stations can communicate in at most h hops. Previous positive results for this problem are only known when h=|S|-1 or in the uniform chain case (i.e., when the stations are equally spaced). As for the first case, Kirousis et al. [7] provided a polynomial-time algorithm while, for the second case, they derive a polynomial-time approximation algorithm. This paper presents the first polynomial-time, approximation algorithm for the MIN ASSIGNMENT problem. The algorithm guarantees a 2-approximation ratio and runs in O(hn ³ ) time. We also prove that, for fixed h and for ``well spaced' instances (a broad generalization of the uniform chain case), the problem admits a polynomial-time approximation scheme . This result significantly improves over the approximability result given by Kirousis {et al}. Both our approximation results are obtained via new algorithms that exactly solve two natural variants of the MIN ASSIGNMENT problem: the problem in which every station must reach a fixed one in at most h hops and the problem in which the goal is to select a subset of bases such that all the other stations must reach one base in at most h-1 hops. Finally, we show that for h=2 the MIN ASSIGNMENT problem can be exactly solved in O(n ³ ) time.     

Improving exact algorithms for MAX-2-SAT

We study three new techniques that will speed up the branch-and-bound algorithm for the MAX-2-SAT problem: The first technique is a group of new lower bound functions for the algorithm and we show that these functions are admissible and consistently better than other known lower bound functions. The other two techniques are based on the strongly connected components of the implication graph of a 2CNF formula: One uses the graph to simplify the formula and the other uses the graph to design a new variable ordering. The experiments show that the simplification can reduce the size of the input substantially no matter what is the clause-to-variable ratio and that the new variable ordering performs much better when the clause-to-variable ratio is less than 2. A direct outcome of this research is a high-performance implementation of an exact algorithm for MAX-2-SAT which outperforms any implementation we know about in the same category. We also show that our implementation is a feasible and effective tool to solve large instances of the Max-Cut problem in graph theory.Preliminary results of this paper appeared in [20,21]. This research was supported in part by NSF under grant CCR-0098093.     

On Semidefinite Programming Relaxations of (2+p)-SAT

Recently, de Klerk, van Maaren and Warners [10] investigated a relaxation of 3-SAT via semidefinite programming. Thus a 3-SAT formula is relaxed to a semidefinite feasibility problem. If the feasibility problem is infeasible then a certificate of unsatisfiability of the formula is obtained. The authors proved that this approach is exact for several polynomially solvable classes of logical formulae, including 2-SAT, pigeonhole formulae and mutilated chessboard formulae. In this paper we further explore this approach, and investigate the strength of the relaxation on (2+p)-SAT formulae, i.e., formulae with a fraction p of 3-clauses and a fraction (1–p) of 2-clauses. In the first instance, we provide an empirical computational evaluation of our approach. Secondly, we establish approximation guarantees of randomized and deterministic rounding schemes when the semidefinite feasibility problem is feasible, and also present computational results for the rounding schemes. In particular, we do a numerical and theoretical comparison of this relaxation and the stronger relaxation by Karloff and Zwick [15].     

A Probabilistic Algorithm for k -SAT Based on Limited Local Search and Restart

A simple probabilistic algorithm for solving the NP-complete problem k -SAT is reconsidered. This algorithm follows a well-known local-search paradigm: randomly guess an initial assignment and then, guided by those clauses that are not satisfied, by successively choosing a random literal from such a clause and changing the corresponding truth value, try to find a satisfying assignment. Papadimitriou [11] introduced this random approach and applied it to the case of 2-SAT, obtaining an expected O(n ² ) time bound. The novelty here is to restart the algorithm after 3n unsuccessful steps of local search. The analysis shows that for any satisfiable k -CNF formula with n variables the expected number of repetitions until a satisfying assignment is found this way is (2? (k-1)/ k) ⁿ . Thus, for 3-SAT the algorithm presented here has a complexity which is within a polynomial factor of (\frac 4 3 ) ⁿ . This is the fastest and also the simplest among those algorithms known up to date for 3-SAT achieving an o(2 ⁿ ) time bound. Also, the analysis is quite simple compared with other such algorithms considered before.     

k-Anonymization with Minimal Loss of Information

The technique of k-anonymization allows the releasing of databases that contain personal information while ensuring some degree of individual privacy. Anonymization is usually performed by generalizing database entries. We formally study the concept of generalization, and propose three information-theoretic measures for capturing the amount of information that is lost during the anonymization process. The proposed measures are more general and more accurate than those that were proposed by Meyerson and Williams and Aggarwal et al. We study the problem of achieving k-anonymity with minimal loss of information. We prove that it is NP-hard and study polynomial approximations for the optimal solution. Our first algorithm gives an approximation guarantee of O(ln k) for two of our measures as well as for the previously studied measures. This improves the best-known O(k)-approximation in. While the previous approximation algorithms relied on the graph representation framework, our algorithm relies on a novel hypergraph representation that enables the improvement in the approximation ratio from O(k) to O(ln k). As the running time of the algorithm is O(n^2k}), we also show how to adapt the algorithm in in order to obtain an O(k)-approximation algorithm that is polynomial in both n and k.     

Improving configuration checking for satisfiable random <Emphasis Type="Italic">k</Emphasis>-SAT instances

Local search algorithms based on the Configuration Checking (CC) strategy have been shown to be efficient in solving satisfiable random k-SAT instances. The purpose of the CC strategy is to avoid the cycling problem, which corresponds to revisiting already flipped variables too soon. It is done by considering the neighborhood of the formula variables. In this paper, we propose to improve the CC strategy on the basis of an empirical study of a powerful algorithm using this strategy. The first improvement introduces a new and simple criterion, which refines the selection of the variables to flip for the 3-SAT instances. The second improvement is achieved by using the powerful local search algorithm Novelty with the adaptive noise setting. This algorithm enhances the efficiency of the intensification and diversification phases when solving k-SAT instances with k ≥ 4. We name the resulting local search algorithm Ncca+ and show its effectiveness when treating satisfiable random k-SAT instances issued from the SAT Challenge 2012. Ncca+ won the bronze medal of the random SAT track of the SAT Competition 2013.     

New algorithms for approximate Nash equilibria in bimatrix games

We consider the problem of computing additively approximate Nash equilibria in non-cooperative two-player games. We provide a new polynomial time algorithm that achieves an approximation guarantee of 0.36392. We first provide a simpler algorithm, that achieves a 0.38197-approximation, which is exactly the same factor as the algorithm of Daskalakis, Mehta and Papadimitriou. This algorithm is then tuned, improving the approximation error to 0.36392. Our method is relatively fast and simple, as it requires solving only one linear program and it is based on using the solution of an auxiliary zero-sum game as a starting point. Finally we also exhibit a simple reduction that allows us to compute approximate equilibria for multi-player games by using algorithms for two-player games.     

Random 2-SAT with Prescribed Literal Degrees

Two classic "phase transitions" in discrete mathematics are the emergence of a giant component in a random graph as the density of edges increases, and the transition of a random 2-SAT formula from satisfiable to unsatisfiable as the density of clauses increases. The random-graph result has been extended to the case of prescribed degree sequences, where the almost-sure nonexistence or existence of a giant component is related to a simple property of the degree sequence. We similarly extend the satisfiability result, by relating the almost-sure satisfiability or unsatisfiability of a random 2-SAT formula to an analogous property of its prescribed literal-degree sequence. The extension has proved useful in analyzing literal-degree-based algorithms for (uniform) random 3-SAT.     

基于寻找可满足2SAT子问题的SAT算法*

可满足问题（SAT）是一个NP-Hard问题。提出了一种求解SAT的新算法（FFSAT）。该算法将SAT问题转换为寻找一个可满足的2-SAT子问题。SAT问题虽然是NP完全问题,但是当所有子句长度不大于2时,SAT问题可以在线性时间求解。使用2-SAT算法-BinSat求解2-SAT子问题,当它不满足时,根据赋值选择新的2-SAT子问题。实验结果表明,采用本算法的结果优于UnitWalk。     

A Probabilistic Algorithm for k -SAT Based on Limited Local Search and Restart

Abstract. A simple probabilistic algorithm for solving the NP-complete problem k -SAT is reconsidered. This algorithm follows a well-known local-search paradigm: randomly guess an initial assignment and then, guided by those clauses that are not satisfied, by successively choosing a random literal from such a clause and changing the corresponding truth value, try to find a satisfying assignment. Papadimitriou [11] introduced this random approach and applied it to the case of 2-SAT, obtaining an expected O(n ² ) time bound. The novelty here is to restart the algorithm after 3n unsuccessful steps of local search. The analysis shows that for any satisfiable k -CNF formula with n variables the expected number of repetitions until a satisfying assignment is found this way is (2⋅ (k-1)/ k) ⁿ . Thus, for 3-SAT the algorithm presented here has a complexity which is within a polynomial factor of (\frac 4 3 ) ⁿ . This is the fastest and also the simplest among those algorithms known up to date for 3-SAT achieving an o(2 ⁿ ) time bound. Also, the analysis is quite simple compared with other such algorithms considered before.     

Enhanced prefetching and caching strategies for single- and multi-disk systems

We study integrated offline caching and prefetching algorithms both in the single- and the multi-disk case. For the problem of minimizing the execution time of a given request sequence, we present simple algorithms. In the single-disk case we present a combinatorial algorithm with an approximation ratio of 3/2. An optimal solution can be computed using a linear program but this requires a very large number of variables. Our new result improves on all previous combinatorial approximation algorithms. For the multi-disk case we give combinatorial 2-approximation algorithms. Additionally, we consider this problem in the resource augmentation model where the approximation algorithms may use more cache lines than the optimal solution they are compared to. Here, we give strategies using one additional cache line per disk that outperform the optimum solution without additional cache in the single-disk case and achieve (1+o(1))-approximations in the multi-disk case. In contrast to some of the previous approaches, all the algorithms we present are combinatorial and easy to implement.     

Improved Approximation Algorithms for the Spanning Star Forest Problem

A star graph is a tree of diameter at most two. A star forest is a graph that consists of node-disjoint star graphs. In the spanning star forest problem, given an unweighted graph G, the objective is to find a star forest that contains all vertices of G and has the maximum number of edges. This problem is the complement of the dominating set problem in the following sense: On a graph with n vertices, the size of the maximum spanning star forest is equal to n minus the size of the minimum dominating set. We present a 0.71-approximation algorithm for this problem, improving upon the approximation factor of 0.6 of Nguyen et al. (SIAM J. Comput. 38:946–962, 2008). We also present a 0.64-approximation algorithm for the problem on node-weighted graphs. Finally, we present improved hardness of approximation results for the weighted (both edge-weighted and node-weighted) versions of the problem. Our algorithms use a non-linear rounding scheme, which might be of independent interest.     

A deterministic (2−2/(k+1))n algorithm for k-SAT based on local search

Local search is widely used for solving the propositional satisfiability problem. Papadimitriou (1991) showed that randomized local search solves 2-SAT in polynomial time. Recently, Schöning (1999) proved that a close algorithm for k-SAT takes time (2−2/k)ⁿ up to a polynomial factor. This is the best known worst-case upper bound for randomized 3-SAT algorithms (cf. also recent preprint by Schuler et al.).We describe a deterministic local search algorithm for k-SAT running in time (2−2/(k+1))ⁿ up to a polynomial factor. The key point of our algorithm is the use of covering codes instead of random choice of initial assignments. Compared to other “weakly exponential” algorithms, our algorithm is technically quite simple. We also describe an improved version of local search. For 3-SAT the improved algorithm runs in time 1.481ⁿ up to a polynomial factor. Our bounds are better than all previous bounds for deterministic k-SAT algorithms.