The property of being polynomial for Mal’tsev constraint satisfaction problems

A combinatorial constraint satisfaction problem aims at expressing in unified terms a wide spectrum of problems in various branches of mathematics, computer science, and AI. The generalized satisfiability problem is NP-complete, but many of its restricted versions can be solved in a polynomial time. It is known that the computational complexity of a restricted constraint satisfaction problem depends only on a set of polymorphisms of relations which are admitted to be used in the problem. For the case where a set of such relations is invariant under some Mal’tsev operation, we show that the corresponding constraint satisfaction problem can be solved in a polynomial time. __________ Translated from Algebra i Logika, Vol. 45, No. 6, pp. 655–686, November–December, 2006.     

The external constraint 4 nonempty part sandwich problem

List partitions generalize list colourings. Sandwich problems generalize recognition problems. The polynomial dichotomy (NP-complete versus polynomial) of list partition problems is solved for 4-dimensional partitions with the exception of one problem (the list stubborn problem) for which the complexity is known to be quasipolynomial. Every partition problem for 4 nonempty parts and only external constraints is known to be polynomial with the exception of one problem (the 2K₂-partition problem) for which the complexity of the corresponding list problem is known to be NP-complete. The present paper considers external constraint 4 nonempty part sandwich problems. We extend the tools developed for polynomial solutions of recognition problems obtaining polynomial solutions for most corresponding sandwich versions. We extend the tools developed for NP-complete reductions of sandwich partition problems obtaining the classification into NP-complete for some external constraint 4 nonempty part sandwich problems. On the other hand and additionally, we propose a general strategy for defining polynomial reductions from the 2K₂-partition problem to several external constraint 4 nonempty part sandwich problems, defining a class of 2K₂-hard problems. Finally, we discuss the complexity of the Skew Partition Sandwich Problem.     

有预算限制的最大多种物资流问题

研究有预算限制的最大多种物资流问题,给出了这个问题的不依赖物资数k的全多项式时间近似算法,其算法复杂性是O~(-ε2m2).同时,利用有预算限制的最大多种物资流问题的研究结果,我们也得到了费用最小的最大多种物资流问题的近似算法和算法复杂性.     

The complexity of recursive constraint satisfaction problems

We investigate the complexity of finding solutions to infinite recursive constraint satisfaction problems. We show that, in general, the problem of finding a solution to an infinite recursive constraint satisfaction problem is equivalent to the problem of finding an infinite path through a recursive tree. We also identify natural classes of infinite recursive constraint satisfaction problems where the problem of finding a solution to the infinite recursive constraint satisfaction problem is equivalent to the problem of finding an infinite path through finitely branching recursive trees or recursive binary trees. There are a large number of results in the literature on the complexity of the problem of finding an infinite path through a recursive tree. Our main result allows us to automatically transfer such results to give equivalent results about the complexity of the problem of finding a solution to a recursive constraint satisfaction problem.     

Minimax problems of discrete optimization invariant under majority operators

A special class of discrete optimization problems that are stated as a minimax modification of the constraint satisfaction problem is studied. The minimax formulation of the problem generalizes the classical problem to realistic situations where the constraints order the elements of the set by the degree of their feasibility, rather than defining a dichotomy between feasible and infeasible subsets. The invariance of this ordering under an operator is defined, and the discrete minimization of functions invariant under majority operators is proved to have polynomial complexity. A particular algorithm for this minimization is described.     

Polynomial time approximation schemes and parameterized complexity

In this paper, we study the relationship between the approximability and the parameterized complexity of NP optimization problems. We introduce a notion of polynomial fixed-parameter tractability and prove that, under a very general constraint, an NP optimization problem has a fully polynomial time approximation scheme if and only if the problem is polynomial fixed-parameter tractable. By enforcing a constraint of planarity on the W-hierarchy studied in parameterized complexity theory, we obtain a class of NP optimization problems, the planar W-hierarchy, and prove that all problems in this class have efficient polynomial time approximation schemes (EPTAS). The planar W-hierarchy seems to contain most of the known EPTAS problems, and is significantly different from the class introduced by Khanna and Motwani in their efforts in characterizing optimization problems with polynomial time approximation schemes.     

On scheduling with the non-idling constraint

In this paper, we give an overview of the main results obtained on the complexity of scheduling under the non-idling constraint, i.e, when the jobs assigned to each machine must be processed with no intermediate delay. That constraint is met in practice when the cost of intermediate idle time is too high due to the idle time itself and/or the machine restarting. The non idling constraint is a strong constraint that often needs a new solving approach and most results about classical scheduling problems do not easily extend to the non-idling variant of the problem. In this survey, we mainly consider the non-idling variants of the basic scheduling problems. So, we first present basic properties, complexity results and some algorithms concerning the one-machine non-idling scheduling problem. Then we consider the $m$ -machine non idling scheduling problem. We show that a few basic problems may be solved by rather easy extensions of the algorithm solving their classical counterpart. However, the complexity status of the non idling version of quite easy polynomial basic problems remains an open question. We finally consider a more constrained version of non-idling, called the “homogeneously non idling” constraint, where for any subset of machines, the union of their busy intervals must make an interval and we present the structural property that leads to a polynomial algorithm for unit time jobs and a weak precedence. We conclude by giving some research directions that seem quite interesting to study both for theoretical and practical issues.     

On the Complexity of the Vertex 3-Coloring Problem for the Hereditary Graph Classes With Forbidden Subgraphs of Small Size

The 3-coloring problem for a given graph consists in verifying whether it is possible to divide the vertex set of the graph into three subsets of pairwise nonadjacent vertices. A complete complexity classification is known for this problem for the hereditary classes defined by triples of forbidden induced subgraphs, each on at most 5 vertices. In this article, the quadruples of forbidden induced subgraphs is under consideration, each on atmost 5 vertices. For all but three corresponding hereditary classes, the computational status of the 3-coloring problem is determined. Considering two of the remaining three classes, we prove their polynomial equivalence and polynomial reducibility to the third class.     

Polynomial Time Samplable Distributions

This paper studies the complexity of the polynomial-time samplable (P-samplable) distributions, which can be approximated within an exponentially small factor by sampling algorithms in time polynomial in the length of their outputs. The paper shows that common assumptions in complexity theory yield the separation of polynomial-time samplable distributions from the polynomial-time computable distributions with respect to polynomial domination, average-polynomial domination, polynomial equivalence, and average-polynomial equivalence.     

Packing chained items in aligned bins with applications to container transshipment and project scheduling

Bin packing problems are at the core of many well-known combinatorial optimization problems and several practical applications alike. In this work we introduce a novel variant of an abstract bin packing problem which is subject to a chaining constraint among items. The problem stems from an application of container handling in rail freight terminals, but is also of relevance in other fields, such as project scheduling. The paper provides a structural analysis which establishes computational complexity of several problem versions and develops (pseudo-)polynomial algorithms for specific subproblems. We further propose and evaluate simple and fast heuristics for optimization versions of the problem.     

A new constraint test-case generator and the importance of hybrid optimizers

In the past decade, significant progress has been made in understanding problem complexity of discrete constraint problems. In contrast, little similar work has been done for constraint problems in the continuous domain. In this paper, we study the complexity of typical methods for non-linear constraint problems and present hybrid solvers with improved performance. To facilitate the empirical study, we propose a new test-case generator for generating non-linear constraint satisfaction problems (CSPs) and constrained optimization problems (COPs). The optimization methods tested include a sequential quadratic programming (SQP) method, a penalty method with a fixed penalty function, a penalty method with a sequence of penalty functions, and an augmented Lagrangian method. For hybrid solvers, we focus on the form that combines two or more optimization methods in sequence. In the experiments, we apply these methods to solve a series of continuous constraint problems with increasing constraint-to-variable ratios. The test problems include artificial benchmark problems from the test-case generator and problems derived from controlling a hyper-redundant modular manipulator. We obtain novel results on complexity phase transition phenomena of the various methods. Specifically, for constraint satisfaction problems, the SQP method is the best on weakly constrained problems, whereas the augmented Lagrangian method is the best on highly constrained ones. Although the static penalty method performs poorly by itself, by combining it with the SQP method, we show a hybrid solver that is significantly better than any of the individual methods on problems with moderate to large constraint-to-variable ratios. For constrained optimization problems, the hybrid solver obtains much better solutions than SQP, while spending comparable amount of time. In addition, the hybrid solver is flexible and can achieve good results on time-bounded applications by setting parameters according to the time limits.     

一个约束乘积最大问题的强多项式时间算法

本文讨论了约束乘积最大问题最优解的结构特征,在此基础上给出了一个计算时间为O(n2)的强多项式时间算法,并且对于单边约束的情形给出了复杂度更低(O(nlnn))的强多项式时间算法.     

Equivalence and equation solvability problems for the alternating group A4

It is observed in this paper that the complexities of the equivalence and the equation solvability problems are not determined by the clone of the algebra. In particular, we prove that for the alternating group on four elements these problems have complexity in P; if we extend the group by the commutator as an extra operation, then the equivalence problem is coNP-complete and the equation solvability problem is NP-complete.     

Convex separable minimization with box constraints

A minimization problem with convex separable objective function subject to a convex separable inequality constraint of the form “less than or equal to” and bounds on the variables (box constraints) is considered. Necessary and sufficient condition is proved for a feasible solution to be an optimal solution to this problem. An iterative algorithm of polynomial complexity for solving problems of the considered form is suggested and its convergence is proved. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Implementing pure adaptive search for global optimization using Markov chain sampling

The Pure Adaptive Search (PAS) algorithm for global optimization yields a sequence of points, each of which is uniformly distributed in the level set corresponding to its predecessor. This algorithm has the highly desirable property of solving a large class of global optimization problems using a number of iterations that increases at most linearly in the dimension of the problem. Unfortunately, PAS has remained of mostly theoretical interest due to the difficulty of generating, in each iteration, a point uniformly distributed in the improving feasible region. In this article, we derive a coupling equivalence between generating an approximately uniformly distributed point using Markov chain sampling, and generating an exactly uniformly distributed point with a certain probability. This result is used to characterize the complexity of a PAS-implementation as a function of (a) the number of iterations required by PAS to achieve a certain solution quality guarantee, and (b) the complexity of the sampling algorithm used. As an application, we use this equivalence to show that PAS, using the so-called Random ball walk Markov chain sampling method for generating nearly uniform points in a convex region, can be used to solve most convex programming problems in polynomial time.     

Generating Functions for Computing the Myerson Value

The complexity of a computational problem is the order of computational resources which are necessary and sufficient to solve the problem. The algorithm complexity is the cost of a particular algorithm. We say that a problem has polynomial complexity if its computational complexity is a polynomial in the measure of input size. We introduce polynomial time algorithms based in generating functions for computing the Myerson value in weighted voting games restricted by a tree. Moreover, we apply the new generating algorithm for computing the Myerson value in the Council of Ministers of the European Union restricted by a communication structure.     

Independence equivalence classes of paths

The independence equivalence class of a graph G is the set of graphs that have the same independence polynomial as G. A graph whose independence equivalence class contains only itself, up to isomorphism, is independence unique. Beaton, Brown and Cameron [2] showed that paths with an odd number of vertices are independence unique and raised the problem of finding the independence equivalence class of paths with an even number of vertices. The problem is completely solved in this paper.     

Minimizing the total completion time in a unit-time open shop with release times

We consider the problem of minimizing the total completion time in a unit-time open shop with release times where the number of machines is constant. Brucker and Krämer (1994) proved that this problem is solvable in polynomial time. However, the time complexity of the algorithm presented there is a polynom of a very high degree and, thus, the algorithm is not practicable even for a small number of machines. We give an O(n²) algorithm for the considered problem which is based on dynamic programming. The result is applied to solve a previously open problem with a special resource constraint raised by De Werra et al. (1991).     

Constructive generation of very hard 3-colorability instances

Graph colorability (COL), is a typical constraint satisfaction problem to which phase transition phenomena (PTs), are important in the computational complexity of combinatorial search algorithms. PTs are significant and subtle because, in the PT region, extraordinarily hard problem instances are found, which may require exponential-order computational time to solve. To clarify PT mechanism, many studies have been undertaken to produce very hard instances, many of which were based on generate-and-test approaches. We propose a rather systematic or constructive algorithm that repeats the embedding of 4-critical graphs to arbitrarily generate large extraordinarily hard 3-colorability instances. We demonstrated experimentally that the computational cost to solve our generated instances is of an exponential order of the number of vertices by using a few actual coloring algorithms and constraint satisfaction algorithms.     

一类Kirchhoff 板弯问题自适应高阶混合元方法及理论分析

本文针对Kirchhoff 板弯问题提出了一个基于高阶Hellan-Herrmann-Johnson (简记为H-H-J)方法的自适应有限元算法, 分析了它的收敛性和计算复杂度. 证明了算法在执行过程中, 相应的拟能量误差会以几何级数单调衰减, 从而得到收敛性. 利用此单调下降性质, 进一步给出了算法的计算复杂度. 推导过程中的一个关键步骤是建立基于平衡方程的单元误差表示(error indicator) 与平衡方程右端载荷震荡项(data oscillation) 的局部等价关系.