Inverse multi-objective combinatorial optimization

Inverse multi-objective combinatorial optimization consists of finding a minimal adjustment of the objective functions coefficients such that a given set of feasible solutions becomes efficient. An algorithm is proposed for rendering a given feasible solution into an efficient one. This is a simplified version of the inverse problem when the cardinality of the set is equal to one. The adjustment is measured by the Chebyshev distance. It is shown how to build an optimal adjustment in linear time based on this distance, and why it is right to perform a binary search for determining the optimal distance. These results led us to develop an approach based on the resolution of mixed-integer linear programs. A second approach based on a branch-and-bound is proposed to handle any distance function that can be linearized. Finally, the initial inverse problem is solved by a cutting plane algorithm.     

Parallel computing in combinatorial optimization

This is a review of the literature on parallel computers and algorithms that is relevant for combinatorial optimization. We start by describing theoretical as well as realistic machine models for parallel computations. Next, we deal with the complexity theory for parallel computations and illustrate the resulting concepts by presenting a number of polylog parallel algorithms andP-completeness results. Finally, we discuss the use of parallelism in enumerative methods.     

Multi-start methods for combinatorial optimization

Multi-start methods strategically sample the solution space of an optimization problem. The most successful of these methods have two phases that are alternated for a certain number of global iterations. The first phase generates a solution and the second seeks to improve the outcome. Each global iteration produces a solution that is typically a local optimum, and the best overall solution is the output of the algorithm. The interaction between the two phases creates a balance between search diversification (structural variation) and search intensification (improvement), to yield an effective means for generating high-quality solutions. This survey briefly sketches historical developments that have motivated the field, and then focuses on modern contributions that define the current state-of-the-art. We consider two categories of multi-start methods: memory-based and memoryless procedures. The former are based on identifying and recording specific types of information (attributes) to exploit in future constructions. The latter are based on order statistics of sampling and generate unconnected solutions. An interplay between the features of these two categories provides an inviting area for future exploration.     

Semidefinite programming in combinatorial optimization

We discuss the use of semidefinite programming for combinatorial optimization problems. The main topics covered include (i) the Lovász theta function and its applications to stable sets, perfect graphs, and coding theory, (ii) the automatic generation of strong valid inequalities, (iii) the maximum cut problem and related problems, and (iv) the embedding of finite metric spaces and its relationship to the sparsest cut problem. Part of this work is supported by NSF contract 9623859-CCR, a Sloan Foundation Fellowship, and ARPA Contract N00014-95-1-1246.     

Extended formulations in combinatorial optimization

This survey is concerned with the size of perfect formulations for combinatorial optimization problems. By “perfect formulation”, we mean a system of linear inequalities that describes the convex hull of feasible solutions, viewed as vectors. Natural perfect formulations often have a number of inequalities that is exponential in the size of the data needed to describe the problem. Here we are particularly interested in situations where the addition of a polynomial number of extra variables allows a formulation with a polynomial number of inequalities. Such formulations are called “compact extended formulations”. We survey various tools for deriving and studying extended formulations, such as Fourier’s procedure for projection, Minkowski–Weyl’s theorem, Balas’ theorem for the union of polyhedra, Yannakakis’ theorem on the size of an extended formulation, dynamic programming, and variable discretization. For each tool that we introduce, we present one or several examples of how this tool is applied. In particular, we present compact extended formulations for several graph problems involving cuts, trees, cycles and matchings, and for the mixing set. We also present Bienstock’s approximate compact extended formulation for the knapsack problem, Goemans’ result on the size of an extended formulation for the permutahedron, and the Faenza-Kaibel extended formulation for orbitopes.     

Totally balanced combinatorial optimization games

Combinatorial optimization games deal with cooperative games for which the value of every subset of players is obtained by solving a combinatorial optimization problem on the resources collectively owned by this subset. A solution of the game is in the core if no subset of players is able to gain advantage by breaking away from this collective decision of all players. The game is totally balanced if and only if the core is non-empty for every induced subgame of it.?We study the total balancedness of several combinatorial optimization games in this paper. For a class of the partition game [5], we have a complete characterization for the total balancedness. For the packing and covering games [3], we completely clarify the relationship between the related primal/dual linear programs for the corresponding games to be totally balanced. Our work opens up the question of fully characterizing the combinatorial structures of totally balanced packing and covering games, for which we present some interesting examples: the totally balanced matching, vertex cover, and minimum coloring games. Received: November 5, 1998 / Accepted: September 8, 1999?Published online February 23, 2000     

Extended formulations in combinatorial optimization

This survey is concerned with the size of perfect formulations for combinatorial optimization problems. By “perfect formulation”, we mean a system of linear inequalities that describes the convex hull of feasible solutions, viewed as vectors. Natural perfect formulations often have a number of inequalities that is exponential in the size of the data needed to describe the problem. Here we are particularly interested in situations where the addition of a polynomial number of extra variables allows a formulation with a polynomial number of inequalities. Such formulations are called “compact extended formulations”. We survey various tools for deriving and studying extended formulations, such as Fourier’s procedure for projection, Minkowski-Weyl’s theorem, Balas’ theorem for the union of polyhedra, Yannakakis’ theorem on the size of an extended formulation, dynamic programming, and variable discretization. For each tool that we introduce, we present one or several examples of how this tool is applied. In particular, we present compact extended formulations for several graph problems involving cuts, trees, cycles and matchings, and for the mixing set, and we present the proof of Fiorini, Massar, Pokutta, Tiwary and de Wolf of an exponential lower bound for the cut polytope. We also present Bienstock’s approximate compact extended formulation for the knapsack problem, Goemans’ result on the size of an extended formulation for the permutahedron, and the Faenza-Kaibel extended formulation for orbitopes.     

Algorithmic extremal problems in combinatorial optimization

An efficient approximation algorithm generator for the generalized maximum ψ-satisfiability problem is presented which produces an efficient approximation algorithm ψ-MAXMEAN¹ for each finite set ψ of relations. The algorithms ψ-MAXMEAN¹ are shown to be best-possible in the class of polynomial algorithms (if P ≠ NP), in both absolute and relative terms. The algorithms are of wide applicability, because of the central position of the generalized maximum satisfiability problem among the class of combinatorial optimization problems.     

Measures of balance in combinatorial optimization

4OR - The concept of balance plays an important role in many combinatorial optimization problems. Yet there exist various ways of expressing balance, and it is not always obvious how best to...     

Adaptive stability in combinatorial optimization problems

We consider a general approach to the construction of necessary, sufficient, and necessary and sufficient conditions that allow to ‘adapt’ a known optimal solution of an abstract combinatorial problem with a certain structure to a change in the initial data set for a fixed cost function ‘easily’ from the combinatorial point of view. We call this approach adaptive stability. Apparently, it is the first time that the approach is described for an abstract problem in a rigorous mathematical formalization.     

A logic language for combinatorial optimization

CHIP (Constraint Handling In Prolog) is a new logic programming language combining the declarative aspect of logic programming for stating search problems with the efficiency of constraint handling techniques for solving them. CHIP has been applied to many real-life problems in Operations Research and hardware design with an efficiency comparable to specific programs written in procedural languages. The main advantage of CHIP is the short development time of the programs and their great modifiability and extensibility. In this paper, we discuss the application of the finite domain part of CHIP to the solving of discrete combinatorial problems occurring in Operations Research. The basic mechanisms underlying CHIP are explained through simple examples. Solutions in CHIP of several real-life problems (e.g., cutting stock, warehouses location problems) are presented and compared with usual approaches, showing the versatility and the interest of the approach.     

Metaheuristic algorithms for combinatorial optimization problems

We present the main results in the author’s Ph.D. thesis (Iori 2004), defended at the University of Bologna in April 2004 and supervised by S. Martello. The thesis is written in English and is available from the author upon request. It proposes exact and metaheuristic algorithms for solving some relevant combinatorial optimization problems, with particular emphasis on scheduling, two-dimensional cutting and packing and capacitated vehicle routing. The performance of each algorithm is tested through extensive computational experiments and comparison with other approaches in the literature.Received: 21 September 2004, AMS classification: 90-08, 90C27, 90C59     

K best solutions to combinatorial optimization problems

We review the Lawler-Murty [24,20] procedure for finding theK best solutions to combinatorial optimization problems. Then we introduce an alternative algorithm which is based on a binary search tree procedure. We apply both algorithms to the problems of finding theK best bases in a matroid, perfect matchings, and best cuts in a network.Partially supported by the National Science Foundation, No. ECS-8412926.     

Cones of multipowers and combinatorial optimization problems

The cone of multipowers is dual to the cone of nonnegative polynomials. The relation of the former cone to combinatorial optimization problems is examined. Tensor extensions of polyhedra of combinatorial optimization problems are used for this purpose. The polyhedron of the MAX-2-CSP problem (optimization version of the two-variable constraint satisfaction problem) of tensor degree 4k is shown to be the intersection of the cone of 4k-multipowers and a suitable affine space. Thus, in contrast to SDP relaxations, the relaxation to a cone of multipowers becomes tight even for an extension of degree 4.     

Persistency in combinatorial optimization problems on matroids

Optimization problems on matroids are generalizations of such important combinatorial optimization problems like the problem of minimum spanning tree of a graph, the bipartite matching problem, flow problems, etc. We analyze algorithms for finding the maximum weight independent set of a matroid and for finding a maximum cardinality intersection of two matroids and extend them to obtain the so-called “persistency” partition of the basic set of the matroid, where contain elements belonging to all optimum solutions; contain elements not belonging to any optimum solution; contain elements that belong to some but not to all optimum solutions.