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57
Parameterized Complexity: Exponential SpeedUp for Planar Graph Problems
 in Electronic Colloquium on Computational Complexity (ECCC
, 2001
"... A parameterized problem is xed parameter tractable if it admits a solving algorithm whose running time on input instance (I; k) is f(k) jIj , where f is an arbitrary function depending only on k. Typically, f is some exponential function, e.g., f(k) = c k for constant c. We describe general techniqu ..."
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Cited by 61 (21 self)
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A parameterized problem is xed parameter tractable if it admits a solving algorithm whose running time on input instance (I; k) is f(k) jIj , where f is an arbitrary function depending only on k. Typically, f is some exponential function, e.g., f(k) = c k for constant c. We describe general techniques to obtain growth of the form f(k) = c p k for a large variety of planar graph problems. The key to this type of algorithm is what we call the "Layerwise Separation Property" of a planar graph problem. Problems having this property include planar vertex cover, planar independent set, and planar dominating set.
Upper Bounds for Vertex Cover Further Improved
"... . The problem instance of Vertex Cover consists of an undirected graph G = (V; E) and a positive integer k, the question is whether there exists a subset C V of vertices such that each edge in E has at least one of its endpoints in C with jCj k. We improve two recent worst case upper bounds fo ..."
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Cited by 43 (16 self)
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. The problem instance of Vertex Cover consists of an undirected graph G = (V; E) and a positive integer k, the question is whether there exists a subset C V of vertices such that each edge in E has at least one of its endpoints in C with jCj k. We improve two recent worst case upper bounds for Vertex Cover. First, Balasubramanian et al. showed that Vertex Cover can be solved in time O(kn + 1:32472 k k 2 ), where n is the number of vertices in G. Afterwards, Downey et al. improved this to O(kn+ 1:31951 k k 2 ). Bringing the exponential base significantly below 1:3, we present the new upper bound O(kn + 1:29175 k k 2 ). 1 Introduction Vertex Cover is a problem of central importance in computer science: { It was among the rst NPcomplete problems [7]. { There have been numerous eorts to design ecient approximation algorithms [3], but it is also known to be hard to approximate [1]. { It is of central importance in parameterized complexity theory and has one ...
New Upper Bounds for Maximum Satisfiability
 Journal of Algorithms
, 1999
"... The (unweighted) Maximum Satisfiability problem (MaxSat) is: given a boolean formula in conjunctive normal form, find a truth assignment that satisfies the most number of clauses. This paper describes exact algorithms that provide new upper bounds for MaxSat. We prove that MaxSat can be solved i ..."
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Cited by 36 (2 self)
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The (unweighted) Maximum Satisfiability problem (MaxSat) is: given a boolean formula in conjunctive normal form, find a truth assignment that satisfies the most number of clauses. This paper describes exact algorithms that provide new upper bounds for MaxSat. We prove that MaxSat can be solved in time O(F  1.3803 K ), where F  is the length of a formula F in conjunctive normal form and K is the number of clauses in F . We also prove the time bounds O(F 1.3995 k ), where k is the maximum number of satisfiable clauses, and O(1.1279 F  ) for the same problem. For Max2Sat this implies a bound of O(1.2722 K ). # An extended abstract of this paper was presented at the 26th International Colloquium on Automata, Languages, and Programming (ICALP'99), LNCS 1644, SpringerVerlag, pages 575584, held in Prague, Czech Republic, July 1115, 1999. + Supported by a Feodor Lynen fellowship (1998) of the Alexander von HumboldtStiftung, Bonn, and the Center for Discrete Ma...
New WorstCase Upper Bounds for SAT
 Journal of Automated Reasoning
, 2000
"... In 1980 Monien and Speckenmeyer proved that satisfiability of a propositional formula consisting of K clauses (of arbitrary length) can be checked in time of the order 2^{K/3}. Recently Kullmann and Luckhardt proved the worstcase upper bound 2^{L/9}, where L is the length of the input formula. The ..."
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Cited by 35 (8 self)
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In 1980 Monien and Speckenmeyer proved that satisfiability of a propositional formula consisting of K clauses (of arbitrary length) can be checked in time of the order 2^{K/3}. Recently Kullmann and Luckhardt proved the worstcase upper bound 2^{L/9}, where L is the length of the input formula. The algorithms leading to these bounds are based on the splitting method which goes back to the Davis{Putnam procedure. Transformation rules (pure literal elimination, unit propagation etc.) constitute a substantial part of this method. In this paper we present a new transformation rule and two algorithms using this rule. We prove that these algorithms have the worstcase upper bounds 2^{0.30897K} and 2^{0.10299L}, respectively.
A new algorithm for optimal constraint satisfaction and its implications
 Alexander D. Scott) Mathematical Institute, University of Oxford
, 2004
"... We present a novel method for exactly solving (in fact, counting solutions to) general constraint satisfaction optimization with at most two variables per constraint (e.g. MAX2CSP and MIN2CSP), which gives the first exponential improvement over the trivial algorithm; more precisely, it is a cons ..."
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Cited by 33 (1 self)
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We present a novel method for exactly solving (in fact, counting solutions to) general constraint satisfaction optimization with at most two variables per constraint (e.g. MAX2CSP and MIN2CSP), which gives the first exponential improvement over the trivial algorithm; more precisely, it is a constant factor improvement in the base of the runtime exponent. In the case where constraints have arbitrary weights, there is a (1 + ǫ)approximation with roughly the same runtime, modulo polynomial factors. Our algorithm may be used to count the number of optima in MAX2SAT and MAXCUT instances in O(m 3 2 ωn/3) time, where ω < 2.376 is the matrix product exponent over a ring. This is the first known algorithm solving MAX2SAT and MAXCUT in provably less than c n steps in the worst case, for some c < 2; similar new results are obtained for related problems. Our main construction may also be used to show that any improvement in the runtime exponent of either kclique solution (even when k = 3) or matrix multiplication over GF(2) would improve the runtime exponent for solving 2CSP optimization. As a corollary, we prove that an n o(k)time kclique algorithm implies SNP ⊆ DTIME[2 o(n)], for any k(n) ∈ o ( √ n / log n). Further extensions of our technique yield connections between the complexity of some (polynomial time) high dimensional geometry problems and that of some general NPhard problems. For example, if there are sufficiently faster algorithms for computing the diameter of n points in ℓ1, then there is an (2 −ǫ) n algorithm for MAXLIN. Such results may be construed as either lower bounds on these highdimensional problems, or hope that better algorithms exist for more general NPhard problems. 1
Automated Generation of Search Tree Algorithms for Hard Graph Modification Problems
 Algorithmica
, 2004
"... We present a framework for an automated generation of exact search tree algorithms for NPhard problems. The purpose of our approach is twofoldrapid development and improved upper bounds. ..."
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Cited by 24 (10 self)
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We present a framework for an automated generation of exact search tree algorithms for NPhard problems. The purpose of our approach is twofoldrapid development and improved upper bounds.
Reflections on multivariate algorithmics and problem parameterization
 In Proceedings of the 27th International Symposium on Theoretical Aspects of Computer Science (STACS ’10), volume 5 of LIPIcs
"... Abstract. Research on parameterized algorithmics for NPhard problems has steadily grown over the last years. We survey and discuss how parameterized complexity analysis naturally develops into the field of multivariate algorithmics. Correspondingly, we describe how to perform a systematic investiga ..."
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Cited by 24 (19 self)
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Abstract. Research on parameterized algorithmics for NPhard problems has steadily grown over the last years. We survey and discuss how parameterized complexity analysis naturally develops into the field of multivariate algorithmics. Correspondingly, we describe how to perform a systematic investigation and exploitation of the “parameter space ” of computationally hard problems.
New WorstCase Upper Bounds for MAX2SAT with Application to MAXCUT
, 2000
"... The maximum 2satisfiability problem (MAX2SAT) is: given a Boolean formula in 2CNF, find a truth assignment that satisfies the maximum possible number of its clauses. MAX2SAT is MAXSNPcomplete. Recently, this problem received much attention in the contexts of approximation (polynomialtime) a ..."
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Cited by 22 (7 self)
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The maximum 2satisfiability problem (MAX2SAT) is: given a Boolean formula in 2CNF, find a truth assignment that satisfies the maximum possible number of its clauses. MAX2SAT is MAXSNPcomplete. Recently, this problem received much attention in the contexts of approximation (polynomialtime) algorithms and exact (exponentialtime) algorithms. In this paper, we present an exact algorithm solving MAX2SAT in time poly(L) 2^(K/5), where K is the number of clauses and L is their total length. Since, in our analysis, we count only clauses containing exactly two literals, this bound implies the bound poly(L) 2^(L/10). Our results significantly improve previous bounds: poly(L) 2^(K/2.88) [30] and poly(L) 2^(K/3.44) (implicit in [4]). As an application, we derive upper bounds for the (MAXSNPcomplete) maximum cut problem (MAXCUT), showing that it can be solved in time poly(M) 2^(M/3), where M is the number of edges in the given graph. This is of special interest for graphs with low vertex degree.
Solving MAXrSAT above a Tight Lower Bound
, 2010
"... We present an exact algorithm that decides, for every fixed r ≥ 2 in time O(m) + 2 O(k2) whether a given multiset of m clauses of size r admits a truth assignment that satisfies at least ((2 r − 1)m + k)/2 r clauses. Thus MaxrSat is fixedparameter tractable when parameterized by the number of sat ..."
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Cited by 21 (8 self)
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We present an exact algorithm that decides, for every fixed r ≥ 2 in time O(m) + 2 O(k2) whether a given multiset of m clauses of size r admits a truth assignment that satisfies at least ((2 r − 1)m + k)/2 r clauses. Thus MaxrSat is fixedparameter tractable when parameterized by the number of satisfied clauses above the tight lower bound (1 − 2 −r)m. This solves an open problem of Mahajan, Raman and Sikdar (J. Comput. System Sci., 75, 2009). Our algorithm is based on a polynomialtime data reduction procedure that reduces a problem instance to an equivalent algebraically represented problem with O(k 2) variables. This is done by representing the instance as an appropriate polynomial, and by applying a probabilistic argument combined with some simple tools from Harmonic analysis to show that if the polynomial cannot be reduced to one of size O(k 2), then there is a truth assignment satisfying the required number of clauses. We introduce a new notion of bikernelization from a parameterized problem to another one and apply it to prove that the abovementioned parameterized MaxrSat admits a polynomialsize kernel. Combining another probabilistic argument with tools from graph matching theory and signed graphs, we show that if an instance of Max2Sat with m clauses has at least 3k variables after application of certain polynomial time reduction rules to it, then there is a truth assignment that satisfies at least (3m + k)/4 clauses. We also outline how the fixedparameter tractability and polynomialsize kernel results on MaxrSat can be extended to more general families of Boolean
GraphModeled Data Clustering: FixedParameter Algorithms for Clique Generation
 In Proc. 5th CIAC, volume 2653 of LNCS
, 2003
"... We present e#cient fixedparameter algorithms for the NPcomplete edge modification problems Cluster Editing and Cluster Deletion. ..."
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Cited by 19 (6 self)
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We present e#cient fixedparameter algorithms for the NPcomplete edge modification problems Cluster Editing and Cluster Deletion.