LU and Cholesky factorizations are computational tools for efficiently solving linear systems that play a central role in solving linear programs and several other classes of mathematical programs. In many documented ...
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LU and Cholesky factorizations are computational tools for efficiently solving linear systems that play a central role in solving linear programs and several other classes of mathematical programs. In many documented cases, however, the roundoff errors accrued during the construction and implementation of these factorizations lead to the misclassification of feasible problems as infeasible and vice versa. Hence, reducing these roundoff errors or eliminating them altogether is imperative to guarantee the correctness of the solutions provided by optimization solvers. To achieve this goal without having to use rational arithmetic, we introduce two roundoff-error-free factorizations that require storing the same number of individual elements and performing a similar number of operations as the traditional LU and Cholesky factorizations. Additionally, we present supplementary roundoff-error-free forward and backward substitution algorithms, thereby providing a complete tool set for solving systems of linear equations exactly and efficiently. An important property shared by the featured factorizations and substitution algorithms is that their individual coefficients' maximum word length-i.e., the maximum number of digits required for expression-is bounded polynomially. Unlike the rational arithmetic methods used in practice to solve linear systems exactly, however, the algorithms herein presented do not require any gcd calculations to bound the entries' word length. We also derive various other related theoretical results, including the total computational complexity of all the roundoff-error-free processes herein presented.
We investigate the empirical performance of the long-standing state-of-the-art exact TSP solver Concorde on various classes of Euclidean TSP instances and show that, surprisingly, the time spent until the first optima...
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We investigate the empirical performance of the long-standing state-of-the-art exact TSP solver Concorde on various classes of Euclidean TSP instances and show that, surprisingly, the time spent until the first optimal solution is found accounts for a large fraction of Concorde's overall running time. This finding holds for the widely studied random uniform Euclidean (RUE) instances as well as for several other widely studied sets of Euclidean TSP instances. On RUE instances, the median fraction of Concorde's total running time spent until an optimal solution is found ranges from 0.77 for to 0.97 for;on TSPLIB, National and VLSI instances, we pegged it at 0.86, 0.74 and 0.61, respectively, with a tendency of even smaller values for larger instances.
The maximum clique problem (MCP) is to determine in a graph a clique (i.e., a complete subgraph) of maximum cardinality. The MCP is notable for its capability of modeling other combinatorial problems and real-world ap...
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The maximum clique problem (MCP) is to determine in a graph a clique (i.e., a complete subgraph) of maximum cardinality. The MCP is notable for its capability of modeling other combinatorial problems and real-world applications. As one of the most studied NP-hard problems, many algorithms are available in the literature and new methods are continually being proposed. Given that the two existing surveys on the MCP date back to 1994 and 1999 respectively, one primary goal of this paper is to provide an updated and comprehensive review on both exact and heuristic MCP algorithms, with a special focus on recent developments. To be informative, we identify the general framework followed by these algorithms and pinpoint the key ingredients that make them successful. By classifying the main search strategies and putting forward the critical elements of the most relevant clique methods, this review intends to encourage future development of more powerful methods and motivate new applications of the clique approaches. (C) 2014 Elsevier B.V. All rights reserved.
We study the induced subgraph isomorphism problem and the general subgraph isomorphism problem for small pattern graphs. We present a new general method for detecting induced subgraphs of a host graph isomorphic to a ...
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We study the induced subgraph isomorphism problem and the general subgraph isomorphism problem for small pattern graphs. We present a new general method for detecting induced subgraphs of a host graph isomorphic to a fixed pattern graph by reduction to polynomial testing for nonidentity with zero over a field of finite characteristic. It yields new upper time bounds for several pattern graphs on five vertices and provides an alternative combinatorial method for the majority of pattern graphs on four and three vertices. Since our method avoids the large overhead of fast matrix multiplication, it can be of practical interest even for larger pattern graphs. Next, we derive new upper time bounds on counting the number of isomorphisms between a fixed pattern graph with an independent set of size s and a subgraph of the host graph. We also consider a weighted version of the counting problem, when one counts the number of isomorphisms between the pattern graph and lightest subgraphs, providing a slightly slower combinatorial algorithm.
This paper proposes a new matrix-based project planning method that takes into consideration task importance or probability of completions thus determines and ranks the importance or probability of possible project sc...
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This paper proposes a new matrix-based project planning method that takes into consideration task importance or probability of completions thus determines and ranks the importance or probability of possible project scenarios and project structures. The proposed algorithm is fast, aims to select the most important project scenarios or the least cost/time demanding project structures. The algorithm is generic, can host several types of goals dictated by the characteristics of project management and as such can be the fundamental element of a project expert- and decision-making system. (C) 2015 Elsevier Ltd. All rights reserved.
In this paper, we present a state-of-the-art survey on the vehicle routing problem with multiple depots (MDVRP). Our review considered papers published between 1988 and 2014, in which several variants of the model are...
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In this paper, we present a state-of-the-art survey on the vehicle routing problem with multiple depots (MDVRP). Our review considered papers published between 1988 and 2014, in which several variants of the model are studied: time windows, split delivery, heterogeneous fleet, periodic deliveries, and pickup and delivery. The review also classifies the approaches according to the single or multiple objectives that are optimized. Some lines for further research are presented as well. (C) 2014 Elsevier Ltd. All rights reserved.
In 2007, the Second International Timetabling Competition (ITC-2007) has been organized and a formal definition of the Curriculum-Based Course Timetabling (CB-CTT) problem has been given, by taking into account severa...
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In 2007, the Second International Timetabling Competition (ITC-2007) has been organized and a formal definition of the Curriculum-Based Course Timetabling (CB-CTT) problem has been given, by taking into account several real-world constraints and objectives while keeping the problem general. CB-CTT consists of finding the best weekly assignment of university course lectures to rooms and time periods. A feasible schedule must satisfy a set of hard constraints and must also take into account a set of soft constraints, whose violation produces penalty terms to be minimized in the objective function. From ITC-2007, many researchers have developed advanced models and methods to solve CB-CTT. This survey is devoted to review the main works on the topic, with focus on mathematical models, lower bounds, and exact and heuristic algorithms. Besides giving an overview of these approaches, we highlight interesting extensions that could make the study of CB-CTT even more challenging and closer to reality.
One of the most important distinctions that must be made in clustering research is the difference between models (or problems) and the methods for solving those problems. Nowhere is this more evident than with the eva...
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One of the most important distinctions that must be made in clustering research is the difference between models (or problems) and the methods for solving those problems. Nowhere is this more evident than with the evaluation of the popular affinity propagation algorithm (apcluster.m), which is a MATLAB implementation of a neural clustering method that has received significant attention in the biological sciences and other disciplines. Several authors have undertaken comparisons of apcluster.m with methods designed for models that fall within the class of uncapacitated facility location problems (UFLPs). These comparative models include the p-center (or K-center) model and, more importantly, the p-median (or K-median) model. The results across studies are conflicting and clouded by the fact that, although similar, the optimization model underlying apcluster.m is slightly different from the p-median model and appreciably different from the pcenter model. In this paper, we clarify that apcluster.m is actually a heuristic for a 'maximization version' of another model in the class of UFLPs, which is known as the simple plant location problem (SPLP). An exact method for the SPLP is described, and the apcluster.m program is compared to a fast heuristic procedure (sasplp.m) in both a simulation experiment and across numerous datasets from the literature. Although the exact method is the preferred approach when computationally feasible, both apcluster.m and sasplp.m are efficient and effective heuristic approaches, with the latter slightly outperforming the former in most instances.
Two-mode partitioning applications are increasingly common in the physical and social sciences with a variety of models and methods spanning these applications. Two-mode KL-means partitioning (TMKLMP) is one type of t...
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Two-mode partitioning applications are increasingly common in the physical and social sciences with a variety of models and methods spanning these applications. Two-mode KL-means partitioning (TMKLMP) is one type of two-mode partitioning model with a conceptual appeal that stems largely from the fact that it is a generalization of the ubiquitous (one-mode) K-means clustering problem. A number of heuristic methods have been proposed for TMKLMP, ranging from a two-mode version of the K-means heuristic to metaheuristic approaches based on simulated annealing, genetic algorithms, variable neighborhood search, fuzzy steps, and tabu search. We present an exact algorithm for TMKLMP based on branch-and-bound programming and demonstrate its utility for the clustering of brand switching, manufacturing cell formation, and journal citation data. Although the proposed branchand-bound algorithm does not obviate the need for approximation methods for large two-mode data sets, it does provide a first step in the development of methods that afford a guarantee of globally-optimal solutions for TMKLMP.
The motif finding problem is one of the important and challenging problems in bioinformatics. A variety of sequential algorithms have been proposed to find exact motifs, but the running time is still not suitable due ...
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The motif finding problem is one of the important and challenging problems in bioinformatics. A variety of sequential algorithms have been proposed to find exact motifs, but the running time is still not suitable due to high computational complexity of finding motifs. In this paper we parallelize three efficient sequential algorithms which are HEPPMSprune, PMS5 and PMS6. We implement the algorithms on a Dual Quad-Core machine using openMP to measure the performance of each algorithm. Our experiment on simulated data show that: (1) the parallel PMS6 is faster than the other algorithms in case of challenging instances, while the parallel HEPPMSprune is faster than the other algorithms in most of solvable instances;(2) the scalability of parallel HEPPMSprune is linear for all instances, while the scalability of parallel PMS5 and PMS6 is linear in case of challenging instances only;(3) the memory used by HEPPMSprune is less than that of the other algorithms.
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