Results 1  10
of
20
Lower Bounds for the Quadratic Assignment Problem Based Upon a Dual Formulation
"... A new bounding procedure for the Quadratic Assignment Problem (QAP) is described which extends the Hungarian method for the Linear Assignment Problem (LAP) to QAPs, operating on the four dimensional cost array of the QAP objective function. The QAP is iteratively transformed in a series of equivalen ..."
Abstract

Cited by 39 (8 self)
 Add to MetaCart
A new bounding procedure for the Quadratic Assignment Problem (QAP) is described which extends the Hungarian method for the Linear Assignment Problem (LAP) to QAPs, operating on the four dimensional cost array of the QAP objective function. The QAP is iteratively transformed in a series of equivalent QAPs leading to an increasing sequence of lower bounds for the original problem. To this end, two classes of operations which transform the four dimensional cost array are defined. These have the property that the values of the transformed objective function Z' are the corresponding values of the old objective function Z, shifted by some amount C. In the case that all entries of the transformed cost array are nonnegative, then C is a lower bound for the initial QAP. If, moreover, there exists a feasible solution U to the QAP, such that its value in the transformed problem is zero, then C is the optimal value of Z and U is an optimal solution for the original QAP. The transformations are iteratively applied until no significant increase in constant C as above is found, resulting in the so called Dual Procedure (DP). Several strategies are listed for appropriately determining C, or equivalently, transforming the cost array. The goal is the modification of the elements in the cost array so as to obtain new equivalent problems which bring the QAP closer to solution. In some cases the QAP is actually solved, though solution is not guaranteed. The close relationship between the DP and the Linear Programming formulation of Adams and Johnson is presented. The DP attempts to solve Adams and Johnsons CLP, a continuous relaxation of a linearization of the QAP. This explains why the DP produces bounds close to the optimum values for CLP calculated by Johnson in her dissertation and by...
Computing lower bounds for the quadratic assignment problem with an interior point algorithm for linear programming
 Operations Research
, 1995
"... A typical example of the quadratic assignment problem (QAP) is the facility location problem, in which a set of n facilities are to be assigned, at minimum cost, to an equal number of locations. Between each pair of facilities, there is a given amount of flow, contributing a cost equal to the produc ..."
Abstract

Cited by 36 (4 self)
 Add to MetaCart
(Show Context)
A typical example of the quadratic assignment problem (QAP) is the facility location problem, in which a set of n facilities are to be assigned, at minimum cost, to an equal number of locations. Between each pair of facilities, there is a given amount of flow, contributing a cost equal to the product of the flow and the distance between locations to which the facilities are assigned. Proving optimality of solutions to quadratic assignment problems has been limited to instances of small dimension (n less than or equal to 20), in part because known lower bounds for the QAP are of poor quality. In this paper, we compute lower bounds for a wide range of quadratic assignment problems using a linear programmingbased lower bound studied by Drezner (1994). On the majority of quadratic assignment problems tested, the computed lower bound is the new best known lower bound. In 87 percent of the instances, we produced the best known lower bound. On several instances, including some of dimension n equal to 20, the lower bound is tight. The linear programs, which can be large even for moderate values of n, are solved with an interior point code that uses a preconditioned conjugate gradient algorithm to compute the directions taken at each iteration by the interior point algorithm. Attempts to
INTERIOR POINT METHODS FOR COMBINATORIAL OPTIMIZATION
, 1995
"... Research on using interior point algorithms to solve combinatorial optimization and integer programming problems is surveyed. This paper discusses branch and cut methods for integer programming problems, a potential reduction method based on transforming an integer programming problem to an equivale ..."
Abstract

Cited by 16 (9 self)
 Add to MetaCart
(Show Context)
Research on using interior point algorithms to solve combinatorial optimization and integer programming problems is surveyed. This paper discusses branch and cut methods for integer programming problems, a potential reduction method based on transforming an integer programming problem to an equivalent nonconvex quadratic programming problem, interior point methods for solving network flow problems, and methods for solving multicommodity flow problems, including an interior point column generation algorithm.
Tree Elaboration Strategies In Branch and Bound Algorithms For Solving the Quadratic Assignment Problem
, 1999
"... This paper presents a new strategy for selecting nodes in a branchandbound algorithm for solving exactly the Quadratic Assignment Problem (QAP). It was developed when it was learned that older strategies failed on the larger size problems. The strategy is a variation of polytomic depthfirst searc ..."
Abstract

Cited by 12 (3 self)
 Add to MetaCart
(Show Context)
This paper presents a new strategy for selecting nodes in a branchandbound algorithm for solving exactly the Quadratic Assignment Problem (QAP). It was developed when it was learned that older strategies failed on the larger size problems. The strategy is a variation of polytomic depthfirst search of Mautor and Roucairol which extends a node by all assignments of an unassigned facility to unassigned locations based upon the counting of 'forbidden' locations. A forbidden location is one where the addition of the corresponding leader (linear cost) element would increase the lower bound beyond the upper bound. We learned that this fortuitous situation never occurs near the root on Nugent problems larger than 15. One has to make better estimates of the bound if the strategy is to work. We have, therefore, designed and implemented an increasingly improved set of bound calculations. The better of these bound calculations to be utilized near the root and the less accurate (poorer bounds) utilized further into the tree. The result is an effective and powerful technique for shortening the run times of problem instances in the range of size 16 to 25. Run times were decreased generally by five or sixtoone and the number of nodes evaluated was decreased as much as 10toone. Later improvements in our strategy produced a better than 3to1 reduction in runtime so that overall improvement in run time was as high as 20to1 as compared to our earlier results. At the end of our paper, we compare the performance of the four most successful algorithms for exact solution of the QAP.
A Branch and Bound Algorithm for the Quadratic Assignment Problem using a Lower Bound Based on Linear Programming
 In C. Floudas and P.M. Pardalos, editors, State of the Art in Global Optimization: Computational Methods and Applications
, 1995
"... In this paper, we study a branch and bound algorithm for the quadratic assignment problem (QAP) that uses a lower bound based on the linear programming (LP) relaxation of a classical integer programming formulation of the QAP. Computational experience with the branch and bound algorithm on several Q ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
(Show Context)
In this paper, we study a branch and bound algorithm for the quadratic assignment problem (QAP) that uses a lower bound based on the linear programming (LP) relaxation of a classical integer programming formulation of the QAP. Computational experience with the branch and bound algorithm on several QAP test problems is reported. The linear programming relaxations are solved with an implementation of an interior point algorithm that uses a preconditioned conjugate gradient algorithm to compute directions. The branch and bound algorithm is compared with a similar branch and bound algorithm that uses the GilmoreLawler lower bound (GLB) instead of the LPbased bound. The LPbased algorithm examines a small portion of the nodes explored by the GLBbased algorithm. 1 Introduction The quadratic assignment problem (QAP), first proposed by Koopmans and Beckmann [16], can be stated as min p2\Pi n X i=1 n X j=1 a ij b p(i)p(j) ; To appear in Proceedings of State of the Art in Global Opti...
Kernelizing Sorting, Permutation and Alignment for Minimum Volume PCA
 IN CONFERENCE ON LEARNING THEORY
, 2004
"... We propose an algorithm for permuting or sorting multiple sets (or bags) of objects such that they can ultimately be represented efficiently using kernel principal component analysis. This framework generalizes sorting from scalars to arbitrary inputs since all computations involve inner products ..."
Abstract

Cited by 10 (1 self)
 Add to MetaCart
(Show Context)
We propose an algorithm for permuting or sorting multiple sets (or bags) of objects such that they can ultimately be represented efficiently using kernel principal component analysis. This framework generalizes sorting from scalars to arbitrary inputs since all computations involve inner products which can be done in Hilbert space and kernelized.
A Survey of the Quadratic Assignment Problem, with Applications
 JOURNAL OF APPLICABLE MATHEMATICS
, 2003
"... The Quadratic Assignment Problem (QAP) is one of the most interesting and most challenging combinatorial optimization problems in existence. This thesis will be a survey of the QAP. An introduction discussing the origins of the problem will be provided first. Next, formal problem descriptions and ma ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
(Show Context)
The Quadratic Assignment Problem (QAP) is one of the most interesting and most challenging combinatorial optimization problems in existence. This thesis will be a survey of the QAP. An introduction discussing the origins of the problem will be provided first. Next, formal problem descriptions and mathematical formulations will be given. Issues pertaining to the computational complexity of the QAP, lower bounds and exact algorithms will also be addressed. Some commonly used heuristic procedures will then be introduced. Finally, some applications of the QAP will be analyzed.
The dual active set algorithm and the iterative solution of linear programs
 Novel Approaches to Hard Discrete Optimization
"... ..."
(Show Context)
Semidefinite Programming Approaches To The Quadratic Assignment Problem
, 2000
"... The Quadratic Assignment Problem, QAP, is arguably the hardest of the NPhard problems. One of the main reasons is that it is very difficult to get good quality bounds for branch and bound algorithms. We show that many of the bounds that have appeared in the literature can be ranked and put into a u ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
The Quadratic Assignment Problem, QAP, is arguably the hardest of the NPhard problems. One of the main reasons is that it is very difficult to get good quality bounds for branch and bound algorithms. We show that many of the bounds that have appeared in the literature can be ranked and put into a unified Semidefinite Programming, SDP, framework. This is done using redundant quadratic constraints and Lagrangian relaxation. Thus, the final SDP relaxation ends up being the strongest.
Implementation Of A Variance Reduction Based Lower Bound In A Branch And Bound Algorithm For The Quadratic Assignment Problem
, 1997
"... . The efficient implementation of a branch and bound algorithm for the quadratic assignment problem (QAP), incorporating the lower bound, based on variance reduction, of Li, Pardalos, Ramakrishnan, and Resende (1994), is presented. A new data structure for efficient implementation of branch and boun ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
. The efficient implementation of a branch and bound algorithm for the quadratic assignment problem (QAP), incorporating the lower bound, based on variance reduction, of Li, Pardalos, Ramakrishnan, and Resende (1994), is presented. A new data structure for efficient implementation of branch and bound algorithms for the QAP is introduced. Computational experiments with the branch and bound algorithm on different classes of QAP test problems are reported. The branch and bound algorithm using the new lower bounds is compared with the same algorithm utilizing the commonly applied GilmoreLawler lower bound. Both implementations use a greedy randomized adaptive search procedure for obtaining initial upper bounds. The algorithms report all optimal permutations. Optimal solutions for previously unsolved instances from the literature, of dimensions n = 16 and n = 20, have been found with the new algorithm. In addition, the new algorithm has been tested on a class of large data variance problem...