An NC Parallel Algorithm for Edge-Coloring Series–Parallel Multigraphs

Many combinatorial problems can be efficiently solved in parallel for series–parallel multigraphs. The edge-coloring problem is one of a few combinatorial problems for which no NC parallel algorithm has been obtained for series–parallel multigraphs. This paper gives an NC parallel algorithm for the problem on series–parallel multigraphsG. It takesO(log n) time withO(Δn/log n) processors, wherenis the number of vertices and Δ is the maximum degree ofG.     

Edge-Coloring Bipartite Graphs

Given a bipartite graph G with n nodes, m edges, and maximum degree Δ, we find an edge-coloring for G using Δ colors in time T + O(m log Δ), where T is the time needed to find a perfect matching in a k-regular bipartite graph with O(m) edges and k ≤ Δ. Together with best known bounds for T this implies on edge-coloring algorithm which improves on the algorithm of Hopcroft and Cole. Our algorithm can also be used to find a (Δ + 2)-edge-coloring for G in time O(m log Δ). The previous best approximation algorithm with the same time bound needed Δ + log Δ colors.     

On levels in arrangements and voronoi diagrams

This paper gives efficient, randomized algorithms for the following problems: (1) construction of levels of order 1 tok in an arrangement of hyperplanes in any dimension and (2) construction of higher-order Voronoi diagrams of order 1 tok in any dimension. A new combinatorial tool in the form of a mathematical series, called a θ series, is associated with an arrangement of hyperplanes inR ^d . It is used to study the combinatorial as well as algorithmic complexity of the geometric problems under consideration.     

The power of geometric duality

This paper uses a new formulation of the notion of duality that allows the unified treatment of a number of geometric problems. In particular, we are able to apply our approach to solve two long-standing problems of computational geometry: one is to obtain a quadratic algorithm for computing the minimum-area triangle with vertices chosen amongn points in the plane; the other is to produce an optimal algorithm for the half-plane range query problem. This problem is to preprocessn points in the plane, so that given a test half-plane, one can efficiently determine all points lying in the half-plane. We describe an optimalO(k + logn) time algorithm for answering such queries, wherek is the number of points to be reported. The algorithm requiresO(n) space andO(n logn) preprocessing time. Both of these results represent significant improvements over the best methods previously known. In addition, we give a number of new combinatorial results related to the computation of line arrangements.     

Local search intensified: Very large-scale variable neighborhood search for the multi-resource generalized assignment problem

We introduce a heuristic for the Multi-Resource Generalized Assignment Problem (MRGAP) based on the concepts of Very Large-Scale Neighborhood Search and Variable Neighborhood Search. The heuristic is a simplified version of the Very Large-Scale Variable Neighborhood Search for the Generalized Assignment Problem. Our algorithm can be viewed as a k-exchange heuristic; but unlike traditional k-exchange algorithms, we choose larger values of k resulting in neighborhoods of very large size with high probability. Searching this large neighborhood (approximately) amounts to solving a sequence of smaller MRGAPs either by exact algorithms or by heuristics. Computational results on benchmark test problems are presented. We obtained improved solutions for many instances compared to some of the best known heuristics for the MRGAP within reasonable running time. The central idea of our heuristic can be used to develop efficient heuristics for other hard combinatorial optimization problems as well.     

A technique for speeding up the solution of the Lagrangean dual

We propose techniques for the solution of the LP relaxation and the Lagrangean dual in combinatorial optimization and nonlinear programming problems. Our techniques find the optimal solution value and the optimal dual multipliers of the LP relaxation and the Lagrangean dual in polynomial time using as a subroutine either the Ellipsoid algorithm or the recent algorithm of Vaidya. Moreover, in problems of a certain structure our techniques find not only the optimal solution value, but the solution as well. Our techniques lead to significant improvements in the theoretical running time compared with previously known methods (interior point methods, Ellipsoid algorithm, Vaidya's algorithm). We use our method to the solution of the LP relaxation and the Langrangean dual of several classical combinatorial problems, like the traveling salesman problem, the vehicle routing problem, the Steiner tree problem, thek-connected problem, multicommodity flows, network design problems, network flow problems with side constraints, facility location problems,K-polymatroid intersection, multiple item capacitated lot sizing problem, and stochastic programming. In all these problems our techniques significantly improve the theoretical running time and yield the fastest way to solve them.     

On polynomial solvability of some problems of a vector subset choice in a Euclidean space of fixed dimension

The problems under study are connected with the choice of a vector subset from a given finite set of vectors in the Euclidean space ℝ ^k . The sum norm and averaged square of the sumnorm are considered as the target functions (to be maximized). The optimal combinatorial algorithms with time complexity O(k ² n ^2k ) are developed for these problems. Thus, the polynomial solvability of these problems is proved for k fixed.     

The union of minimal hitting sets: Parameterized combinatorial bounds and counting

A k-hitting set in a hypergraph is a set of at most k vertices that intersects all hyperedges. We study the union of all inclusion-minimal k-hitting sets in hypergraphs of rank r (where the rank is the maximum size of hyperedges). We show that this union is relevant for certain combinatorial inference problems and give worst-case bounds on its size, depending on r and k. For r=2 our result is tight, and for each r3 we have an asymptotically optimal bound and make progress regarding the constant factor. The exact worst-case size for r3 remains an open problem. We also propose an algorithm for counting all k-hitting sets in hypergraphs of rank r. Its asymptotic runtime matches the best one known for the much more special problem of finding one k-hitting set. The results are used for efficient counting of k-hitting sets that contain any particular vertex.     

An approximation algorithm for sorting by reversals and transpositions

Genome rearrangement algorithms are powerful tools to analyze gene orders in molecular evolution. Analysis of genomes evolving by reversals and transpositions leads to a combinatorial problem of sorting by reversals and transpositions, the problem of finding a shortest sequence of reversals and transpositions that sorts one genome into the other. In this paper we present a 2k-approximation algorithm for sorting by reversals and transpositions for unsigned permutations where k is the approximation ratio of the algorithm used for cycle decomposition. For the best known value of k our approximation ratio becomes 2.8386+δ for any δ>0. We also derive a lower bound on reversal and transposition distance of an unsigned permutation.     

Equivalence in Finite-Variable Logics is Complete for Polynomial Time

of first-order logic whose formulas contain at most k variables (for some ). We show that for each , equivalence in the logic is complete for polynomial time. Moreover, we show that the same completeness result holds for the powerful extension of with counting quantifiers (for every ). The k-dimensional Weisfeiler–Lehman algorithm is a combinatorial approach to graph isomorphism that generalizes the naive color-refinement method (for ). Cai, Fürer and Immerman [6] proved that two finite graphs are equivalent in the logic if, and only if, they can be distinguished by the k-dimensional Weisfeiler-Lehman algorithm. Thus a corollary of our main result is that the question of whether two finite graphs can be distinguished by the k-dimensional Weisfeiler–Lehman algorithm is P-complete for each . Received: March 23, 1998     

An annotated bibliography of combinatorial optimization problems with fixed cardinality constraints

In this paper, we consider combinatorial optimization problems with additional cardinality constraints. In k-cardinality combinatorial optimization problems, a cardinality constraint requires feasible solutions to contain exactly k elements of a finite set E. Problems of this type have applications in many areas, e.g. in the mining and oil industry, telecommunications, circuit layout, and location planning. We formally define the problem, mention some examples and summarize general results. We provide an annotated bibliography of combinatorial optimization problems of which versions with cardinality constraint have been considered in the literature.     

Heuristically guided algorithm for k-parity matroid problems

It is known that a large class of “hard” combinatorial optimization problems can be put in the form of a k-parity (weighted) matroid problem. In this paper we describe a heuristically guided algorithm for solving the above class of problems, which utilizes the information obtainable from the problem domain by computing, at each step, a possibly tight lower bound to the solution.     

Dyson’s New Symmetry and Generalized Rogers-Ramanujan Identities

We present a generalization, which we call (k, m)-rank, of Dyson’s notion of rank to integer partitions with k successive Durfee rectangles and give two combinatorial symmetries associated with this new definition. We prove these symmetries bijectively. Using the two symmetries we give a new combinatorial proof of generalized Rogers-Ramanujan identities. We also describe the relationship between (k, m)-rank and Garvan’s k-rank.     

Combinatorial Stokes formulas via minimal resolutions

We describe an explicit chain map from the standard resolution to the minimal resolution for the finite cyclic group Zk of order k. We then demonstrate how such a chain map induces a “Zk-combinatorial Stokes theorem,” which in turn implies “Dold's theorem” that there is no equivariant map from an n-connected to an n-dimensional free Zk-complex. Thus we build a combinatorial access road to problems in combinatorics and discrete geometry that have previously been treated with methods from equivariant topology. The special case k=2 for this is classical; it involves Tucker's (1949) combinatorial lemma which implies the Borsuk-Ulam theorem, its proof via chain complexes by Lefschetz (1949), the combinatorial Stokes formula of Fan (1967), and Meunier's work (2006).     

Pure adaptive search for finite global optimization

Pure Adaptive Search is a stochastic algorithm which has been analyzed for continuous global optimization. When a uniform distribution is used in PAS, it has been shown to have complexity which is linear in dimension. We define strong and weak variations of PAS in the setting of finite global optimization and prove analogous results. In particular, for then-dimensional lattice {1,,k} ⁿ , the expected number of iterations to find the global optimum is linear inn. Many discrete combinatorial optimization problems, although having intractably large domains, have quite small ranges. The strong version of PAS for all problems, and the weak version of PAS for a limited class of problems, has complexity the order of the size of the range.The authors would like to thank the Department of Mathematics and Statistics at the University of Canterbury for support of this research.     

The orthogonal convex skull problem

We give a combinatorial definition of the notion of a simple orthogonal polygon beingk-concave, wherek is a nonnegative integer. (A polygon is orthogonal if its edges are only horizontal or vertical.) Under this definition an orthogonal polygon which is 0-concave is convex, that is, it is a rectangle, and one that is 1-concave is orthoconvex in the usual sense, and vice versa. Then we consider the problem of computing an orthoconvex orthogonal polygon of maximal area contained in a simple orthogonal polygon. This is the orthogonal version of the potato peeling problem. AnO(n ²) algorithm is presented, which is a substantial improvement over theO(n ⁷) time algorithm for the general problem.The work of the first author was supported under a Natural Sciences and Engineering Research Council of Canada Grant No. A-5692 and the work of the second author was partially supported by NSF Grants Nos. DCR-84-01898 and DCR-84-01633.     

Reductions in Computational Complexity Using Clifford Algebras

A number of combinatorial problems are treated using properties of abelian null-square-generated and idempotent-generated subalgebras of Clifford algebras. For example, the problem of deciding whether or not a graph contains a Hamiltonian cycle is known to be NP-complete. By considering entries of Λ^k, where Λ is an appropriate nilpotent adjacency matrix, the k-cycles in any finite graph are recovered. Within the algebra context (i.e., considering the number of multiplications performed within the algebra), these problems are reduced to matrix multiplication, which is in complexity class P. The Hamiltonian cycle problem is one of many problems moved from classes NP-complete and #P-complete to class P in this context. Other problems considered include the set covering problem, counting the edge-disjoint cycle decompositions of a finite graph, computing the permanent of an arbitrary matrix, computing the girth and circumference of a graph, and finding the longest path in a graph.     

Topologically sweeping visibility complexes via pseudotriangulations

This paper describes a new algorithm for constructing the set of free bitangents of a collection ofn disjoint convex obstacles of constant complexity. The algorithm runs in timeO(n logn + k), where,k is the output size, and uses,O(n) space. While earlier algorithms achieve the same optimal running time, this is the first optimal algorithm that uses only linear space. The visibility graph or the visibility complex can be computed in the same time and space. The only complicated data structure used by the algorithm is a splittable queue, which can be implemented easily using red-black trees. The algorithm is conceptually very simple, and should therefore be easy to implement and quite fast in practice. The algorithm relies on greedy pseudotriangulations, which are subgraphs of the visibility graph with many nice combinatorial properties. These properties, and thus the correctness of the algorithm, are partially derived from properties of a certain partial order on the faces of the visibility complex. A preliminary version of this work appeared in theProceedings of the 11th Annual ACM Symposium on Computational Geometry, Vancouver, June 1995, pages 248–257.     

Parameterizing above Guaranteed Values: MaxSat and MaxCut

In this paper we investigate the parameterized complexity of the problems MaxSat and MaxCut using the framework developed by Downey and Fellows. LetGbe an arbitrary graph havingnvertices andmedges, and letfbe an arbitrary CNF formula withmclauses onnvariables. We improve Cai and Chen'sO(2^2ckcm) time algorithm for determining if at leastkclauses of ac-CNF formulafcan be satisfied; our algorithm runs inO(|f| + k²φ^k) time for arbitrary formulae and inO(cm + ckφ^k) time forc-CNF formulae, where φ is the golden ratio . We also give an algorithm for finding a cut of size at leastk; our algorithm runs inO(m + n + k4^k) time. We then argue that the standard parameterization of these problems is unsuitable, because nontrivial situations arise only for large parameter values (k ≥ m/2), in which range the fixed-parameter tractable algorithms are infeasible. A more meaningful question in the parameterized setting is to ask whether m/2 + kclauses can be satisfied, or m/2 + kedges can be placed in a cut. We show that these problems remain fixed-parameter tractable even under this parameterization. Furthermore, for up to logarithmic values of the parameter, our algorithms for these versions also run in polynomial time.     

Globally optimal solutions of max–min systems

A variety of problems in computer science, operations research, control theory, etc., can be modeled as non-linear and non-differentiable max–min systems. This paper introduces the global optimization into such systems. The criteria for the existence and uniqueness of the globally optimal solutions are established using the high matrix, optimal max-only projection set and k ^s -control vector of max–min functions. It is also shown that the global optimization can be accomplished through the partial max-only projection representation with algebraic and combinatorial features. The methods are constructive and lead to an algorithm of finding all globally optimal solutions.