The Complexity of Minimum Ratio Spanning Tree Problems

We examine the complexity of two minimum spanning tree problems with rational objective functions. We show that the Minimum Ratio Spanning Tree problem is NP-hard when the denominator is unrestricted in sign, thereby sharpening a previous complexity result. We then consider an extension of this problem where the objective function is the sum of two linear ratios whose numerators and denominators are strictly positive. This problem is shown to be NP-hard as well. We conclude with some results characterizing sufficient conditions for a globally optimal solution.     

Unified encoding for hyper-heuristics with application to bioinformatics

This paper introduces a new approach to applying hyper-heuristic algorithms to solve combinatorial problems with less effort, taking into account the modelling and algorithm construction process. We propose a unified encoding of a solution and a set of low level heuristics which are domain-independent and which change the solution itself. This approach enables us to address NP-hard problems and generate good approximate solutions in a reasonable time without a large amount of additional work required to tailor search methodologies for the problem in hand. In particular, we focused on solving DNA sequencing by hybrydization with errors, which is known to be strongly NP-hard. The approach was extensively tested by solving multiple instances of well-known combinatorial problems and compared with results generated by meta heuristics that have been tailored for specific problem domains.     

A robust heuristic for the Generalized Assignment Problem

The Generalized Assignment Problem, in the class of NP-hard problems, occurs in a wide range of applications — vehicle packing, computers, and logistics, to name only a few. Previous research has been concentrated on optimization methodologies for the GAP. Because the Generalized Assignment Problem is NP-hard, optimization methods tend to require larger computation times for large-scale problems. This paper presents a new heuristic,Variable-Depth-Search Heuristic (VDSH). We show that on the sets of large test problems, the quality of the solution found by VDSH exceeds that of the leading heuristic by an average of over twenty percent, while maintaining acceptable solution times. On difficult problem instances, VDSH provides solutions having costs 140% less than those found by the leading heuristic. A duality gap analysis of VDSH demonstrates the robustness of our heuristics.     

On the complexity of minmax regret linear programming

We consider linear programming problems with uncertain objective function coefficients. For each coefficient of the objective function, an interval of uncertainty is known, and it is assumed that any coefficient can take on any value from the corresponding interval of uncertainty, regardless of the values taken by other coefficients. It is required to find a minmax regret solution. This problem received considerable attention in the recent literature, but its computational complexity status remained unknown. We prove that the problem is strongly NP-hard. This gives the first known example of a minmax regret optimization problem that is NP-hard in the case of interval-data representation of uncertainty but is polynomially solvable in the case of discrete-scenario representation of uncertainty.     

Complexity of the min-max and min-max regret assignment problems

This paper investigates the complexity of the min-max and min-max regret assignment problems both in the discrete scenario and interval data cases. We show that these problems are strongly NP-hard for an unbounded number of scenarios. We also show that the interval data min-max regret assignment problem is strongly NP-hard.     

When are NP-hard location problems easy?

We discuss in this paper several location problems for which it is an NP-hard problem to find an approximate solution. Given certain assumptions on the input distributions, we present polynomial algorithms that deliver a solution asymptotically close to the optimum with probability that is asymptotically one (the exact nature of this asymptotic convergence is described in the paper). In that sense the subproblems defined on the specified family of inputs are in fact easy.This research was supported in part by the National Science Foundation under grant ECS-8204695.     

Fast local search methods for solving limited memory influence diagrams

Limited memory influence diagrams are graph-based models that describe decision problems with limited information such as planning with teams and/or agents with imperfect recall. Solving a (limited memory) influence diagram is an NP-hard problem, often approached through local search. In this work we give a closer look at k-neighborhood local search approaches. We show that finding a k-neighboring strategy that improves on the current solution is W[1]-hard and hence unlikely to be polynomial-time tractable. We also show that finding a strategy that resembles an optimal strategy (but may have low expected utility) is NP-hard. We then develop fast schema to perform approximate k-local search; experiments show that our methods improve on current local search algorithms both with respect to time and to accuracy.     

Minimum concave-cost network flow problems: Applications,complexity, and algorithms

We discuss a wide range of results for minimum concave-cost network flow problems, including related applications, complexity issues, and solution techniques. Applications from production and inventory planning, and transportation and communication network design are discussed. New complexity results are proved which show that this problem is NP-hard for cases with cost functions other than fixed charge. An overview of solution techniques for this problem is presented, with some new results given regarding the implementation of a particular branch-and-bound approach.     

Two NP-Hard Art-Gallery Problems for Ortho-Polygons

D. T. Lee and A. K. Lin [2] proved that VERTEX-GUARDING and POINT-GUARDING are NP-hard for simple polygons. We prove that those problems are NP-hard for ortho-polygons, too.     

On uniform k-partition problems

We study various uniform k-partition problems which consist in partitioning m sets, each of cardinality k, into k sets of cardinality m such that each of these sets contains exactly one element from every original set. The problems differ according to the particular measure of “set uniformity” to be optimized. Most problems are polynomial and corresponding solution algorithms are provided. A few of them are proved to be NP-hard. Examples of applications to scheduling and routing problems are also discussed.     

Budgeted matching and budgeted matroid intersection via the gasoline puzzle

Many polynomial-time solvable combinatorial optimization problems become NP-hard if an additional complicating constraint is added to restrict the set of feasible solutions. In this paper, we consider two such problems, namely maximum-weight matching and maximum-weight matroid intersection with one additional budget constraint. We present the first polynomial-time approximation schemes for these problems. Similarly to other approaches for related problems, our schemes compute two solutions to the Lagrangian relaxation of the problem and patch them together to obtain a near-optimal solution. However, due to the richer combinatorial structure of the problems considered here, standard patching techniques do not apply. To circumvent this problem, we crucially exploit the adjacency relations on the solution polytope and, surprisingly, the solution to an old combinatorial puzzle.     

Tabu search for graph partitioning

In this paper, we develop a tabu search procedure for solving the uniform graph partitioning problem. Tabu search, an abstract heuristic search method, has been shown to have promise in solving several NP-hard problems, such as job shop and flow shop scheduling, vehicle routing, quadratic assignment, and maximum satisfiability. We compare tabu search to other heuristic procedures for graph partitioning, and demonstrate that tabu search is superior to other solution approaches for the uniform graph partitioning problem both with respect to solution quality and computational requirements.     

一类带单源约束的选址运输问题算法研究

带单源约束的选址运输问题是在经典的选址运输问题基础上考虑每个顾客需求的产品仅由一家工厂供应的情况。所建立的模型是整数规划，是NP难的。本文先考虑了开办费用为零的带单源约束的选址运输问题，即带单源约束的运输问题。松弛其中一种变量约束，借鉴求解运输问题的表上作业法，给出了一种修正的表上作业法，然后将算法推广。最后给出了将算法应用在Excel随机生成的测试问题上所得到的结果，与LINDO求得的最优解相比，差距很小。由此得出结论：对规模较小的带单源约束的选址运输问题，本文提出的算法是简便且行之有效的。     

One-dimensional machine location problems in a multi-product flowline with equidistant locations

An assignment of machines to locations along a straight track is required to optimize material flow in many manufacturing systems. The assignment of M unique machines to M locations along a linear material handling track with the objective of minimizing the total machine-to-machine material transportation cost is formulated as a quadratic assignment problem (QAP). The distance matrix in problems involving equally-spaced machine locations in one dimension is seen to possess some unique characteristics called amoebic properties. Since an optimal solution to a problem with large M is computationally intractable (the QAP is NP-hard), a number of the amoebic properties are exploited to devise heuristics and a lower bound on the optimal solution. Computational results which demonstrate the performance of the heuristics and the lower bound are presented.     

The elastic generalized assignment problem

The generalized assignment problem (GAP) has been studied by numerous researchers over the past 30 years or so. Simply stated, one must find a minimum-cost assignment of tasks to agents such that each task is assigned to exactly one agent and such that each agent's resource capacity is honoured. The problem is known to be NP-hard. In this paper, we study the elastic generalized assignment problem (EGAP). The elastic version of GAP allows agent resource capacity to be violated at additional cost. Another version allows undertime costs to be assessed as well if an agent's resource capacity is not used to its full extent. The EGAP is also NP-hard. We describe a special-purpose branch-and-bound algorithm that utilizes linear programming cuts, feasible solution generators, Lagrangean relaxation and subgradient optimization. We present computational results on a large collection of randomly generated ‘hard’ problems with up to 4000 binary variables.     

A FEASIBLE DIRECTION ALGORITHM WITHOUT LINE SEARCH FOR SOLVING MAX-BISECTION PROBLEMS

This paper concerns the solution of the NP-hard max-bisection problems. NCP func-tions are employed to convert max-bisection problems into continuous nonlinear program-ming problems. Solving the resulting continuous nonlinear programming problem generatesa solution that gives an upper bound on the optimal value of the max-bisection problem.From the solution, the greedy strategy is used to generate a satisfactory approximate so-lution of the max-bisection problem. A feasible direction method without line searches isproposed to solve the resulting continuous nonlinear programming, and the convergenceof the algorithm to KKT point of the resulting problem is proved. Numerical experimentsand comparisons on well-known test problems, and on randomly generated test problemsshow that the proposed method is robust, and very efficient.     

Single-machine scheduling problems with actual time-dependent and job-dependent learning effect

In this study, we introduce an actual time-dependent and job-dependent learning effect into single-machine scheduling problems. We show that the complexity results of the makespan minimization problem and the sum of weighted completion time minimization problem are all NP-hard. The complexity result of the maximum lateness minimization problem is NP-hard in the strong sense. We also provide three special cases which can be solved by polynomial time algorithms.     

Complexity of two dual criteria scheduling problems

In this article we answer the complexity question of two dual criteria scheduling problems which had been open for a long time. We show that both problems are binary NP-hard.     

Half thresholding eigenvalue algorithm for semidefinite matrix completion

The semidefinite matrix completion(SMC) problem is to recover a low-rank positive semidefinite matrix from a small subset of its entries. It is well known but NP-hard in general. We first show that under some cases, SMC problem and S1/2relaxation model share a unique solution. Then we prove that the global optimal solutions of S1/2regularization model are fixed points of a symmetric matrix half thresholding operator. We give an iterative scheme for solving S1/2regularization model and state convergence analysis of the iterative sequence.Through the optimal regularization parameter setting together with truncation techniques, we develop an HTE algorithm for S1/2regularization model, and numerical experiments confirm the efficiency and robustness of the proposed algorithm.     

Computational complexity in additive hedonic games

We investigate the computational complexity of several decision problems in hedonic coalition formation games and demonstrate that attaining stability in such games remains NP-hard even when they are additive. Precisely, we prove that when either core stability or strict core stability is under consideration, the existence problem of a stable coalition structure is NP-hard in the strong sense. Furthermore, the corresponding decision problems with respect to the existence of a Nash stable coalition structure and of an individually stable coalition structure turn out to be NP-complete in the strong sense.