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DESIGN, IMPLEMENTATION, AND EVALUATION OF THE CONSTRAINT LANGUAGE cc(FD)
 J. LOGIC PROGRAMMING 1994:19, 20:1679
, 1994
"... This paper describes the design, implementation, and applications of the constraint logic language cc(FD). cc(FD) is a declarative nondeterministic constraint logic language over finite domains based on the cc framework [33], an extension of the CLP scheme [21]. Its constraint solver includes (nonl ..."
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Cited by 166 (9 self)
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This paper describes the design, implementation, and applications of the constraint logic language cc(FD). cc(FD) is a declarative nondeterministic constraint logic language over finite domains based on the cc framework [33], an extension of the CLP scheme [21]. Its constraint solver includes (nonlinear) arithmetic constraints over natural numbers which are approximated using domain and interval consistency. The main novelty of cc(FD) is the inclusion of a number of generalpurpose combinators, in particular cardinality, constructive disjunction, and blocking implication, in conjunction with new constraint operations such as constraint entailment and generalization. These combinators signi cantly improve the operational expressiveness, extensibility, and flexibility of CLP languages and allow issues such as the definition of nonprimitive constraints and disjunctions to be tackled at the language level. The implementation of cc(FD) (about 40,000 lines of C) includes a WAMbased engine [44], optimal arcconsistency algorithms based on AC5 [40], and incremental implementation of the combinators. Results on numerous problems, including scheduling, resource allocation, sequencing, packing, and hamiltonian paths are reported and indicate that cc(FD) comes close to procedural languages on a number of combinatorial problems. In addition, a small cc(FD) program was able to nd the optimal solution and prove optimality to a famous 10/10 disjunctive scheduling problem [29], which was left open for more than 20 years and nally solved in 1986.
A Parallel Genetic Algorithm for the Set Partitioning Problem
, 1994
"... In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed stea ..."
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Cited by 66 (1 self)
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In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed steadystate genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steadystate genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty realworld set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, highquality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
The CrossEntropy Method for Combinatorial and Continuous Optimization
, 1999
"... We present a new and fast method, called the crossentropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on ..."
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Cited by 55 (6 self)
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We present a new and fast method, called the crossentropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on KullbackLeibler crossentropy, importance sampling, Markov chain and Boltzmann distribution. We use importance sampling as an important ingredient for adaptive adjustment of the temperature in the Boltzmann distribution and use KullbackLeibler crossentropy to find the optimal solution. In fact, we use the mode of a unimodal importance sampling distribution, like the mode of beta distribution, as an estimate of the optimal solution for continuous optimization and Markov chains approach for combinatorial optimization. In the later case we show almost surely convergence of our algorithm to the optimal solution. Supporting numerical results for both continuous and combinatorial optimization problems are given as well. Our empirical studies suggest that the crossentropy method has polynomial in the size of the problem running time complexity.
Gomory Cuts Revisited
, 1996
"... In this paper, we investigate the use of Gomory's mixed integer cuts within a branchandcut framework. It has been argued in the literature that "a marriage of classical cutting planes and tree search is out of the question as far as the solution of largescale combinatorial optimization problems i ..."
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Cited by 45 (5 self)
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In this paper, we investigate the use of Gomory's mixed integer cuts within a branchandcut framework. It has been argued in the literature that "a marriage of classical cutting planes and tree search is out of the question as far as the solution of largescale combinatorial optimization problems is concerned" [16] because the cuts generated at one node of the search tree need not be valid at other nodes. We show in this paper that it is possible, using a simple lifting procedure, to make Gomory cuts generated in a node of the enumeration tree globally valid in the case of mixed 01 programs. Other issues addressed in this paper are of computational nature, such as strategies for generating the cutting planes, deciding between branching and cutting, etc. The result is a robust mixed integer program solver. 1 Introduction In the late fifties and early sixties, Gomory [6], [7], [8] proposed to solve integer programs by using cutting planes, thus reducing integer programming to the solu...
Genetic algorithms and scatter search: unsuspected potentials
 Statistics and Computing
, 1994
"... We provide a tutorial survey of connections between genetic algorithms and scatter search that have useful implications for developing new methods for optimization problems. The links between these approaches are rooted in principles underlying mathematical relaxations, which became inherited and ex ..."
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Cited by 25 (3 self)
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We provide a tutorial survey of connections between genetic algorithms and scatter search that have useful implications for developing new methods for optimization problems. The links between these approaches are rooted in principles underlying mathematical relaxations, which became inherited and extended by scatter search. Hybrid methods incorporating elements of genetic algorithms and scatter search are beginning to be explored in the literature, and we demonstrate that the opportunity exists to develop more advanced procedures that make fuller use of scatter search strategies and their recent extensions.
Correlated label propagation with application to multilabel learning
 IN: CVPR ’06: PROCEEDINGS OF THE 2006 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION
, 2006
"... Many computer vision applications, such as scene analysis and medical image interpretation, are illsuited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multilabel learning where a given image can be tagged with multip ..."
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Cited by 22 (0 self)
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Many computer vision applications, such as scene analysis and medical image interpretation, are illsuited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multilabel learning where a given image can be tagged with multiple class labels. A serious problem with existing approaches is that they are unable to exploit correlations between class labels. This paper presents a novel framework for multilabel learning termed Correlated Label Propagation (CLP) that explicitly models interactions between labels in an efficient manner. As in standard label propagation, labels attached to training data points are propagated to test data points; however, unlike standard algorithms that treat each label independently, CLP simultaneously copropagates multiple labels. Existing work eschews such an approach since naive algorithms for label copropagation are intractable. We present an algorithm based on properties of submodular functions that efficiently finds an optimal solution. Our experiments demonstrate that CLP leads to significant gains in precision/recall against standard techniques on two realworld computer vision tasks involving several hundred labels.
Independenttree ad hoc multicast routing (ITAMAR)
 ACM MOBILE NETWORKS AND APPLICATIONS
, 2003
"... Multicasting is an efficient means of one to many communication and is typically implemented by creating a multicasting tree. Because of the severe battery power and transmission bandwidth limitations in ad hoc networks, multicast routing can significantly improve the performance of this type of ne ..."
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Cited by 18 (0 self)
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Multicasting is an efficient means of one to many communication and is typically implemented by creating a multicasting tree. Because of the severe battery power and transmission bandwidth limitations in ad hoc networks, multicast routing can significantly improve the performance of this type of networks. However, due to the frequent and hardtopredict topological changes of ad hoc networks, maintenance of a multicasting tree to ensure its availability, could be a difficult task. We borrow from the concept of Alternate Path routing, which has been studied for providing QOS routing, effective congestion control, security, and route failure protection, to propose a scheme in which a set of multicasting trees is continuously maintained. In our scheme, a tree is used until it fails at which time it is replaced by an alternative tree in the set, so that the time between failure of a tree and resumption of multicast routing is minimal. In this paper, we introduce the scheme and present a number of heuristics to compute a set of alternate trees. The heuristics are then compared in terms of transmission cost, improvement in the average time between multicast failures and the probability of usefulness. Simulations show significant gains over a wide range of network operational conditions. In particular, we show that using alternate trees has the potential of improving mean time between interruption by 100600 % in a 50 node network (for most multicast group sizes) with small increase in the tree cost and the route discovery overhead.
On Combinatorial Auction and Lagrangean Relaxation for Distributed Resource Scheduling
 IIE Transactions
, 1998
"... Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the b ..."
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Cited by 18 (4 self)
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Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangeanbased decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a #rst step toward developing a distributed scheduling framework that maintains systemwide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four di#erent versions of the auction mechanism using job shop scheduling proble...
Global Search Methods For Solving Nonlinear Optimization Problems
, 1997
"... ... these new methods, we develop a prototype, called Novel (Nonlinear Optimization Via External Lead), that solves nonlinear constrained and unconstrained problems in a unified framework. We show experimental results in applying Novel to solve nonlinear optimization problems, including (a) the lear ..."
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Cited by 15 (1 self)
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... these new methods, we develop a prototype, called Novel (Nonlinear Optimization Via External Lead), that solves nonlinear constrained and unconstrained problems in a unified framework. We show experimental results in applying Novel to solve nonlinear optimization problems, including (a) the learning of feedforward neural networks, (b) the design of quadraturemirrorfilter digital filter banks, (c) the satisfiability problem, (d) the maximum satisfiability problem, and (e) the design of multiplierless quadraturemirrorfilter digital filter banks. Our method achieves better solutions than existing methods, or achieves solutions of the same quality but at a lower cost.
Lagrangean/Surrogate Heuristics for pMedian Problems
, 2000
"... : The pmedian problem is the problem of locating p facilities (medians) on a network so as to minimize the sum of all the distances from each demand point to its nearest facility. A successful approach to approximately solve this problem is the use of Lagrangean heuristics, based upon Lagrangean re ..."
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Cited by 14 (9 self)
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: The pmedian problem is the problem of locating p facilities (medians) on a network so as to minimize the sum of all the distances from each demand point to its nearest facility. A successful approach to approximately solve this problem is the use of Lagrangean heuristics, based upon Lagrangean relaxation and subgradient optimization. The Lagrangean/surrogate is an alternative relaxation proposed recently to correct the erratic behavior of subgradient like methods employed to solve the Lagrangean dual. We propose in this paper Lagrangean/surrogate heuristics to pmedian problems. Lagrangean and surrogate relaxations are combined relaxing in the surrogate way the assignment constraints in the pmedian formulation. Then, the Lagrangean relaxation of the surrogate constraint is obtained and approximately optimized (onedimensional dual). Lagrangean/surrogate relaxations are very stable (low oscillating) and reach the same good results of Lagrangean (alone) heuristics in less computation...