A special class of set covering problems

A class of set covering problems is being introduced. This class is obtained from reformulation of a well-known combinatorial problem of Erdös on the hypercube. An algorithmic method of solution to the problem is proposed. Max-flow algorithms are the main ingredients of our method.The computational results which will be presented here, improves the best existing bound related to the combinatorial problem. This, at the same time, provides a good approximate solution to the corresponding set covering problem of more than a thousand variables and constraints.Moreover, we show that our special class of problems can be recognized from the class of all set covering problems, by a polynomial algorithm with O (MN) complexity, where M and N are numbers of constraints and variables of a given instant, respectively.This research is supported by EPSCOR-NSF of Puerto Rico and by FIPI, the institutional grant of University of Puerto Rico.     

Algorithms for large scale set covering problems

This paper is concerned with the set covering problem (SCP), and in particular with the development of a new algorithm capable of solving large-scale SCPs of the size found in real-life situations. The set covering problem has a wide variety of practical applications which, in general, yield large and sparse instances, normally with hundreds of rows and thousands of columns. In this paper, we present an algorithm capable of solving problems of this size and test problems up to 400 rows and 4000 columns are solved. The method developed in this paper consists of a tree-search procedure based on a combination of decomposition and state space relaxation which is a technique developed for obtaining lower bounds on the dynamic program associated with a combinatorial optimization problem. The large size SCPs are decomposed into many smaller SCPs which are then solved or bounded by state space relaxation (SSR). Before using the decomposition and SSR, reductions both in the number of columns and the number of rows of the problem are made by applying Lagrangian relaxation, linear programming and heuristic methods.     

A Lagrangian-based heuristic for large-scale set covering problems

We present a new Lagrangian-based heuristic for solving large-scale set-covering problems arising from crew-scheduling at the Italian Railways (Ferrovie dello Stato). Our heuristic obtained impressive results when compared to state-of-the-art codes on a test-bed provided by the company, which includes instances with sizes ranging from 50,000 variables and 500 constraints to 1,000,000 variables and 5000 constraints. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Corresponding author.This research was performed while the author was affiliated with IASI, CNR and Dipartimento di Informatica e Sistemistica, Università di Roma, La Sapienza, Italy.This research was partially supported by National Research Program Metodi di Ottimizzazione per le Decisioni, MURST, Roma, Italy.     

An indirect genetic algorithm for set covering problems

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, ie the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post-optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.     

Solving large set covering problems for crew scheduling

This paper presents a computationally effective heuristic method which produces good-quality solutions for large-scale set covering problems with thousands of constraints and about one million variables. The need to solve such large-scale problems arises from a crew scheduling problem of mass transit agencies where the number of work shifts required has to be minimized. This problem may be formulated as a large-scale non-unicost set covering problem whose rows are trips to be performed while columns stand for round trips. The proposed method is mainly based on lagragian relaxation and sub-gradient optimization. After the reduction of the number of rows and columns by the logical tests, “greedy” heuristic algorithms provide upper and lower bounds which are continuously improved to produce goodquality solutions. Computational results, regarding randomly generated problems and real life problems concerning crew scheduling at Italian Railways Company, show that good-quality solutions can be obtained at an acceptable computational cost. This work was supported by the project “Progetto Finalizzato Transporti 2” of National Research Council of Italy (C.N.R.) contract No. 94.01436PF74 and by “Ferrovie dello Stato S.p.A.”     

A note on some computationally difficult set covering problems

Fulkerson et al. have given two examples of set covering problems that are empirically difficult to solve. They arise from Steiner triple systems and the larger problem, which has a constraint matrix of size 330 × 45 has only recently been solved. In this note, we show that the Steiner triple systems do indeed give rise to a series of problems that are probably hard to solve by implicit enumeration. The main result is that for ann variable problem, branch and bound algorithms using a linear programming relaxation, and/or elimination by dominance require the examination of a super-polynomial number of partial solutionsThis paper was written while the author was a CORE Fellow at the Université de Louvain, Louvain-la-Neuve, Belgium.     

Genetic algorithms applied to computationally difficult set covering problems

The set covering problem is a known NP-hard combinatorial optimisation problem for covering the rows of a matrix by a subset of columns at minimum cost. Genetic algorithms (GA) are a class of iteration procedures that simulate the evolution process of a structured population. The objective of this work is to show that a somewhat classical GA implementation reaches high quality computational results for difficult set covering problems arising in computing the 1-width of incidence matrices of Steiner triple systems. In computational tests all optimal and best known solutions were found for incidence matrices A₉, A₁₅, A₂₇, A₄₅, A₈₁ and A₂₄₃ with reasonable times for a microcomputer implementation. Other tests with classical set covering problems confirm the good results for an additional class of instances.     

Using a facility location algorithm to solve large set covering problems

Erlenkotter has developed an efficient exact (guarantees optimality) algorithm to solve the uncapacitated facility location problem (UFLP). In this paper, we use his algorithm to solve large instances of an important subset of the UFLP; the set covering problem (SCP). In addition, we present further empirical evidence that a heuristic algorithm developed by Vasko and Wilson for the SCP is capable of quickly generating good solutions to large SCP's.     

An interior point algorithm to solve computationally difficult set covering problems

We present an interior point approach to the zero–one integer programming feasibility problem based on the minimization of a nonconvex potential function. Given a polytope defined by a set of linear inequalities, this procedure generates a sequence of strict interior points of this polytope, such that each consecutive point reduces the value of the potential function. An integer solution (not necessarily feasible) is generated at each iteration by a rounding scheme. The direction used to determine the new iterate is computed by solving a nonconvex quadratic program on an ellipsoid. We illustrate the approach by considering a class of difficult set covering problems that arise from computing the 1-width of the incidence matrix of Steiner triple systems.     

Absolute bounds on optimal cost for a class of set covering problems

We study the class of (m constraint,n variable) set covering problems which have no more thank variables represented in each constraint. Letd denote the maximum column sum in the constraint matrix, letr=[m/d]?1, and letZ _g denote the cost of a greedy heuristic solution. Then we prove $$\begin{gathered} Zg \leqslant 1 + r + m - d - \left[ {mk \cdot MAX\left\{ {\frac{{2r}}{{2n - r - 1}},\ln \frac{n}{{n - r}}} \right\}} \right. \hfill \\ \left. { - kd \cdot MIN\left\{ {\frac{{r(r + 1)}}{{2(n - r)}},n \cdot \ln \frac{{n - 1}}{{n - r - 1}} - 1} \right\}} \right]. \hfill \\ \end{gathered} $$ This provides the firsta priori nontrivial upper bound discovered on heuristic solution cost (and thus on optimal solution cost) for the set covering problem. An example demonstrates that this bound is attainable, both for a greedy heuristic solution and for the optimal solution. Numerical examples show that this bound is substantially better than existing bounds for many problem instances. An important subclass of these problems occurs when the constraint matrix is a circulant, in which casem=n andk=d=[αη] for some 0<α<1. For this subclass we prove $$\mathop {\lim }\limits_{n \to \infty } Zg/n \leqslant \frac{{\alpha ^2 }}{2}[1/\alpha ][1/\alpha ].$$      

On directed graphs with an independent covering set

We prove that if a directed graph,D, contains no odd directed cycle and, for all but finitely many vertices, EITHER the in-degrees are finite OR the out-degrees are at most one, thenD contains an independent covering set (i.e. there is a kernel). We also give an example of a countable directed graph which has no directed cycle, each vertex has out-degree at most two, and which has no independent covering set.Research supported by N.S.E.R.C. grants #69-0982 and #69-0259.     

Clustering heuristics for set covering

We introduce a new class of set covering heuristics, based on clustering techniques. In its simplest form, a heuristic in this class may be described as follows: firstly, partition the column set into clusters formed by columns that are close to each other (e.g. in the Hamming distance sense). Then select a best (e.g. a cheapest) column in each cluster; if the selected columns form a coverC, then extract fromC a prime cover and stop; else, modify the partition (e.g. by increasing the number of clusters) and repeat. We describe two implementations of this general algorithmic strategy, relying on the Single Linkage and the Leader clustering algorithm, respectively. Numerical experiments performed on 72 randomly generated test problems with 200 or 400 rows and 1000 columns indicate that the above two heuristics often yield cheaper covers than other well-known (greedy-type) heuristics when the cost-range is not too narrow.The present work is based on R.K. Kwatera's dissertation, written under the supervision of B. Simeone. A preliminary version was presented at EURO VIII, Paris, July 1988.     

Computing the minimal covering set

We present the first polynomial-time algorithm for computing the minimal covering set of a (weak) tournament. The algorithm draws upon a linear programming formulation of a subset of the minimal covering set known as the essential set. On the other hand, we show that no efficient algorithm exists for two variants of the minimal covering set–the minimal upward covering set and the minimal downward covering set–unless P equals NP. Finally, we observe a strong relationship between von Neumann–Morgenstern stable sets and upward covering on the one hand, and the Banks set and downward covering on the other.     

On a new class of nonlinear set covering problems arising in wireless network design

This is a summary of the author’s PhD thesis, supervised by Edoardo Amaldi and defended on 28 April 2006 at Politecnico di Milano, Dipartimento di Matematica. The thesis is written in English and is available from the author upon request. The thesis investigates a class of nonlinear set covering variants arising from the problem of designing single-frequency Wireless Local Area Networks (WLANs) with maximum efficiency. In the first part of the thesis a basic hyperbolic formulation of the problem is considered. After a complexity and approximability study, the problem is tackled by linearization techniques, and by Lagrangean and Dantzig–Wolfe decompositions. The second part of the thesis focuses on variants accounting for various relevant features of the WLAN application. A Branch-and-Price algorithm is presented, and extensions to the multiple-frequency WLAN design problem are considered.      

On covering an independent set in a grid with a second independent set

We show that for every independent set O in an n × m grid, n, m > 1, there is a second independent set X with the property that every member of O is adjacent to at least one member of X . The proof gives a construction for X . Equivalently, we show that for every maximal independent set in a grid, there is a second, disjoint, maximal independent set. © 1996 John Wiley & Sons, Inc.     

An algorithm for set covering problem

In this paper we present an algorithm for the set covering problem that combines problem reduction tests with dual ascent, subgradient optimisation and linear programming. Computational results are presented for problems involving up to 400 rows and 4000 columns.     

Discrete cooperative covering problems

A family of discrete cooperative covering problems is analysed in this paper. Each facility emits a signal that decays by the distance and each demand point observes the total signal emitted by all facilities. A demand point is covered if its cumulative signal exceeds a given threshold. We wish to maximize coverage by selecting locations for p facilities from a given set of potential sites. Two other problems that can be solved by the max-cover approach are the equivalents to set covering and p-centre problems. The problems are formulated, analysed and solved on networks. Optimal and heuristic algorithms are proposed and extensive computational experiments reported.