Relevant bibliographies by topics / Combinatorial optimization Computer algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Combinatorial optimization Computer algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Combinatorial optimization Computer algorithms"

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Korolyov, Vyacheslav, and Oleksandr Khodzinskyi. "Solving Combinatorial Optimization Problems on Quantum Computers." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 5–13. http://dx.doi.org/10.34229/2707-451x.20.2.1.

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Introduction. Quantum computers provide several times faster solutions to several NP-hard combinatorial optimization problems in comparison with computing clusters. The trend of doubling the number of qubits of quantum computers every year suggests the existence of an analog of Moore's law for quantum computers, which means that soon they will also be able to get a significant acceleration of solving many applied large-scale problems. The purpose of the article is to review methods for creating algorithms of quantum computer mathematics for combinatorial optimization problems and to analyze the influence of the qubit-to-qubit coupling and connections strength on the performance of quantum data processing. Results. The article offers approaches to the classification of algorithms for solving these problems from the perspective of quantum computer mathematics. It is shown that the number and strength of connections between qubits affect the dimensionality of problems solved by algorithms of quantum computer mathematics. It is proposed to consider two approaches to calculating combinatorial optimization problems on quantum computers: universal, using quantum gates, and specialized, based on a parameterization of physical processes. Examples of constructing a half-adder for two qubits of an IBM quantum processor and an example of solving the problem of finding the maximum independent set for the IBM and D-wave quantum computers are given. Conclusions. Today, quantum computers are available online through cloud services for research and commercial use. At present, quantum processors do not have enough qubits to replace semiconductor computers in universal computing. The search for a solution to a combinatorial optimization problem is performed by achieving the minimum energy of the system of coupled qubits, on which the task is mapped, and the data are the initial conditions. Approaches to solving combinatorial optimization problems on quantum computers are considered and the results of solving the problem of finding the maximum independent set on the IBM and D-wave quantum computers are given. Keywords: quantum computer, quantum computer mathematics, qubit, maximal independent set for a graph.
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Calégari, Patrice, Frédéric Guidec, Pierre Kuonen, and Frank Nielsen. "Combinatorial optimization algorithms for radio network planning." Theoretical Computer Science 263, no. 1-2 (July 2001): 235–45. http://dx.doi.org/10.1016/s0304-3975(00)00245-0.

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Iori, Manuel. "Metaheuristic algorithms for combinatorial optimization problems." 4OR 3, no. 2 (June 2005): 163–66. http://dx.doi.org/10.1007/s10288-005-0052-3.

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Ramaswamy, Vasu, and Vadim Shapiro. "Combinatorial Laws for Physically Meaningful Design." Journal of Computing and Information Science in Engineering 4, no. 1 (March 1, 2004): 3–10. http://dx.doi.org/10.1115/1.1645863.

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A typical computer representation of a design includes geometric and physical information organized in a suitable combinatorial data structure. Queries and transformations of these design representations are used to formulate most algorithms in computational design, including analysis, optimization, evolution, generation, and synthesis. Formal properties, and in particular existence and validity of the computed solutions, must be assured and preserved by all such algorithms. Using tools from algebraic topology, we show that a small set of the usual combinatorial operators: boundary (∂), coboundary (δ), and dualization *–are sufficient to represent a variety of physical laws and invariants. Specific examples include geometric integrity, balance and equilibrium, and surface smoothing. Our findings point a way toward systematic development of data structures and algorithms for design in a common formal computational framework.
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Yagiura, Mutsunori, and Toshihide Ibaraki. "On metaheuristic algorithms for combinatorial optimization problems." Systems and Computers in Japan 32, no. 3 (2001): 33–55. http://dx.doi.org/10.1002/1520-684x(200103)32:3<33::aid-scj4>3.0.co;2-p.

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Markakis, Vangelis, Ioannis Milis, and Vangelis Th Paschos. "Special Issue: “Combinatorial Optimization: Theory of Algorithms and Complexity”." Theoretical Computer Science 540-541 (June 2014): 1. http://dx.doi.org/10.1016/j.tcs.2014.05.015.

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Ehrgott, M. "Approximation algorithms for combinatorial multicriteria optimization problems." International Transactions in Operational Research 7, no. 1 (January 2000): 5–31. http://dx.doi.org/10.1111/j.1475-3995.2000.tb00182.x.

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Mbarek, Fatma, and Volodymyr Mosorov. "Load Balancing Based on Optimization Algorithms: An Overview." Journal of Telecommunications and Information Technology 4, no. 2019 (December 30, 2019): 3–12. http://dx.doi.org/10.26636/jtit.2019.131819.

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Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.
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Kotsireas, I. S., C. Koukouvinos, P. M. Pardalos, and O. V. Shylo. "Periodic complementary binary sequences and Combinatorial Optimization algorithms." Journal of Combinatorial Optimization 20, no. 1 (November 26, 2008): 63–75. http://dx.doi.org/10.1007/s10878-008-9194-5.

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Ahmadian, Ali, Ali Elkamel, and Abdelkader Mazouz. "An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network." Energies 12, no. 16 (August 8, 2019): 3052. http://dx.doi.org/10.3390/en12163052.

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Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.
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Dissertations / Theses on the topic "Combinatorial optimization Computer algorithms"

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Minkoff, Maria 1976. "Approximation algorithms for combinatorial optimization under uncertainty." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87452.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 87-90).
Combinatorial optimization problems arise in many fields of industry and technology, where they are frequently used in production planning, transportation, and communication network design. Whereas in the context of classical discrete optimization it is usually assumed that the problem inputs are known, in many real-world applications some of the data may be subject to an uncertainty, often because it represents information about the future. In the field of stochastic optimization uncertain parameters are usually represented as random variables that have known probability distributions. In this thesis we study a number of different scenarios of planning under uncertainty motivated by applications from robotics, communication network design and other areas. We develop approximation algorithms for several NP-hard stochastic combinatorial optimization problems in which the input is uncertain - modeled by probability distribution - and the goal is to design a solution in advance so as to minimize expected future costs or maximize expected future profits. We develop techniques for dealing with certain probabilistic cost functions making it possible to derive combinatorial properties of an optimum solution. This enables us to make connections with already well-studied combinatorial optimization problems and apply some of the tools developed for them. The first problem we consider is motivated by an application from AI, in which a mobile robot delivers packages to various locations. The goal is to design a route for robot to follow so as to maximize the value of packages successfully delivered subject to an uncertainty in the robot's lifetime.
(cont.) We model this problem as an extension of the well-studied Prize-Collecting Traveling Salesman problem, and develop a constant factor approximation algorithm for it, solving an open question along the way. Next we examine several classical combinatorial optimization problems such as bin-packing, vertex cover, and shortest path in the context of a "preplanning" framework, in which one can "plan ahead" based on limited information about the problem input, or "wait and see" until the entire input becomes known, albeit incurring additional expense. We study this time-information tradeoff, and show how to approximately optimize the choice of what to purchase in advance and what to defer. The last problem studied, called maybecast is concerned with designing a routing network under a probabilistic distribution of clients using locally available information. This problem can be modeled as a stochastic version of the Steiner tree problem. However probabilistic objective function turns it into an instance of a challenging optimization problem with concave costs.
by Maria Minkoff.
Ph.D.
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Kanade, Gaurav Nandkumar. "Combinatorial optimization problems in geometric settings." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1152.

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We consider several combinatorial optimization problems in a geometric set- ting. The first problem we consider is the problem of clustering to minimize the sum of radii. Given a positive integer k and a set of points with interpoint distances that satisfy the definition of being a "metric", we define a ball centered at some input point and having some radius as the set of all input points that are at a distance smaller than the radius of the ball from its center. We want to cover all input points using at most k balls so that the sum of the radii of the balls chosen is minimized. We show that when the points lie in some Euclidean space and the distance measure is the standard Euclidean metric, we can find an exact solution in polynomial time under standard assumptions about the model of computation. The second problem we consider is the Network Spanner Topology Design problem. In this problem, given a set of nodes in the network, represented by points in some geometric setting - either a plane or a 1.5-D terrain, we want to compute a height assignment function h that assigns a height to a tower at every node such that the set of pairs of nodes that can form a direct link with each other under this height function forms a connected spanner. A pair of nodes can form a direct link if they are within a bounded distance B of each other and the heights of towers at the two nodes are sufficient to achieve Line-of-Sight connectivity - i.e. the straight line connecting the top of the towers lies above any obstacles. In the planar setting where the obstacles are modeled as having a certain maximum height and minimum clearance distance, we give a constant factor approximation algorithm. In the case where the points lie on a 1.5-D terrain we illustrate that it might be hard to use Computational Geometry to achieve efficient approximations. The final problem we consider is the Multiway Barrier Cut problem. Here, given a set of points in the plane and a set of unit disk sensors also in the plane such that any path in the plane between any pair of input points hits at least one of the given sensor disks we consider the problem of finding the minimum size subset of these disks that still achieves this separation. We give a constant factor approximation algorithm for this problem.
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Allwright, James. "Parallel algorithms for combinatorial optimization on transputer arrays." Thesis, University of Southampton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.255769.

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Cui, Xinwei. "Using genetic algorithms to solve combinatorial optimization problems." FIU Digital Commons, 1991. http://digitalcommons.fiu.edu/etd/2684.

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Genetic algorithms are stochastic search techniques based on the mechanics of natural selection and natural genetics. Genetic algorithms differ from traditional analytical methods by using genetic operators and historic cumulative information to prune the search space and generate plausible solutions. Recent research has shown that genetic algorithms have a large range and growing number of applications. The research presented in this thesis is that of using genetic algorithms to solve some typical combinatorial optimization problems, namely the Clique, Vertex Cover and Max Cut problems. All of these are NP-Complete problems. The empirical results show that genetic algorithms can provide efficient search heuristics for solving these combinatorial optimization problems. Genetic algorithms are inherently parallel. The Connection Machine system makes parallel implementation of these inherently parallel algorithms possible. Both sequential genetic algorithms and parallel genetic algorithms for Clique, Vertex Cover and Max Cut problems have been developed and implemented on the SUN4 and the Connection Machine systems respectively.
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Krishnaswamy, Ravishankar. "Approximation Techniques for Stochastic Combinatorial Optimization Problems." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/157.

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The focus of this thesis is on the design and analysis of algorithms for basic problems in Stochastic Optimization, specifically a class of fundamental combinatorial optimization problems where there is some form of uncertainty in the input. Since many interesting optimization problems are computationally intractable (NP-Hard), we resort to designing approximation algorithms which provably output good solutions. However, a common assumption in traditional algorithms is that the exact input is known in advance. What if this is not the case? What if there is uncertainty in the input? With the growing size of input data and their typically distributed nature (e.g., cloud computing), it has become imperative for algorithms to handle varying forms of input uncertainty. Current techniques, however, are not robust enough to deal with many of these problems, thus necessitating the need for new algorithmic tools. Answering such questions, and more generally identifying the tools for solving such problems, is the focus of this thesis. The exact problems we study in this thesis are the following: (a) the Survivable Network Design problem where the collection of (source,sink) pairs is drawn randomly from a known distribution, (b) the Stochastic Knapsack problem with random sizes/rewards for jobs, (c) the Multi-Armed Bandits problem, where the individual Markov Chains make random transitions, and finally (d) the Stochastic Orienteering problem, where the random tasks/jobs are located at different vertices on a metric. We explore different techniques for solving these problems and present algorithms for all the above problems with near-optimal approximation guarantees. We also believe that the techniques are fairly general and have wider applicability than the context in which they are used in this thesis.
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Agnihotri, Ameya Ramesh. "Combinatorial optimization techniques for VLSI placement." Diss., Online access via UMI:, 2007.

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Che, Chan Hou. "Generalized minimum spanning tree problem /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?IELM%202006%20CHE.

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Björklund, Henrik. "Combinatorial Optimization for Infinite Games on Graphs." Doctoral thesis, Uppsala University, Department of Information Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4751.

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Games on graphs have become an indispensable tool in modern computer science. They provide powerful and expressive models for numerous phenomena and are extensively used in computer- aided verification, automata theory, logic, complexity theory, computational biology, etc.

The infinite games on finite graphs we study in this thesis have their primary applications in verification, but are also of fundamental importance from the complexity-theoretic point of view. They include parity, mean payoff, and simple stochastic games.

We focus on solving graph games by using iterative strategy improvement and methods from linear programming and combinatorial optimization. To this end we consider old strategy evaluation functions, construct new ones, and show how all of them, due to their structural similarities, fit into a unifying combinatorial framework. This allows us to employ randomized optimization methods from combinatorial linear programming to solve the games in expected subexponential time.

We introduce and study the concept of a controlled optimization problem, capturing the essential features of many graph games, and provide sufficent conditions for solvability of such problems in expected subexponential time.

The discrete strategy evaluation function for mean payoff games we derive from the new controlled longest-shortest path problem, leads to improvement algorithms that are considerably more efficient than the previously known ones, and also improves the efficiency of algorithms for parity games.

We also define the controlled linear programming problem, and show how the games are translated into this setting. Subclasses of the problem, more general than the games considered, are shown to belong to NP intersection coNP, or even to be solvable by subexponential algorithms.

Finally, we take the first steps in investigating the fixed-parameter complexity of parity, Rabin, Streett, and Muller games.

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Wang, Lei. "Some approximation algorithms for multi-agent systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42726.

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This thesis makes a number of contributions to the theory of approximation algorithm design for multi-agent systems. In particular, we focus on two research directions. The first direction is to generalize the classical framework of combinatorial optimization to the submodular setting, where we assume that each agent has a submodular cost function. We show hardness results from both the information-theoretic and computational aspects for several fundamental optimization problems in the submodular setting, and provide matching approximation algorithms for most of them. The second direction is to introduce game-theoretic issues to approximation algorithm design. Towards this direction, we study the application of approximation algorithms in the theory of truthful mechanism design. We study both the standard objectives of revenue and social welfare, by designing efficient algorithms that satisfy the requirement of truthfulness and guarantee approximate optimality.
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Abuali, Faris Nabih. "Using determinant and cycle basis schemes in genetic algorithms for graph and network applications /." Access abstract and link to full text, 1995. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/9529027.

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Books on the topic "Combinatorial optimization Computer algorithms"

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Combinatorial heuristic algorithms with FORTRAN. Berlin: Springer-Verlag, 1986.

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Jungnickel, Dieter. Graphs, Networks and Algorithms. 4th ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Pardalos, P. M. (Panos M.), 1954- and SpringerLink (Online service), eds. Data Correcting Approaches in Combinatorial Optimization. New York, NY: Springer New York, 2012.

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Habib, Youssef, ed. Iterative computer algorithms with applications in engineering: Solving combinatorial optimization problems. Los Alamitos, Calif: IEEE Computer Society, 1999.

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Neumann, Frank. Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Hansen, P., and Celso C. Ribeiro. Essays and surveys in metaheuristics. New York: Springer, 2002.

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Wahde, M. Biologically inspired optimization methods: An introduction. Southampton, UK: WIT Press, 2008.

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Dieter, Kratsch, and SpringerLink (Online service), eds. Exact Exponential Algorithms. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Hromkovič, Juraj. Algorithmics for Hard Problems: Introduction to Combinatorial Optimization, Randomization, Approximation, and Heuristics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Osman, Ibrahim H. Meta-Heuristics: Theory and Applications. Boston, MA: Springer US, 1996.

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Book chapters on the topic "Combinatorial optimization Computer algorithms"

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Lovasz, Laszlo. "Randomized algorithms in combinatorial optimization." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 153–79. Providence, Rhode Island: American Mathematical Society, 1995. http://dx.doi.org/10.1090/dimacs/020/03.

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Raghavan, Prabhakar. "Randomized approximation algorithms in combinatorial optimization." In Lecture Notes in Computer Science, 300–317. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58715-2_133.

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Shylo, Oleg, Dmytro Korenkevych, and Panos M. Pardalos. "Global Equilibrium Search Algorithms for Combinatorial Optimization Problems." In Lecture Notes in Computer Science, 277–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32964-7_28.

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Mattavelli, Marco, Vincent Noel, and Edoardo Amaldi. "Fast Line Detection Algorithms Based on Combinatorial Optimization." In Lecture Notes in Computer Science, 410–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45129-3_37.

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Achasova, S. M. "Cellular neural-like algorithms with heuristics for solving combinatorial optimization problems." In Lecture Notes in Computer Science, 330–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63371-5_33.

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Junger, M., G. Reinelt, and Stefan Thienel. "Practical problem solving with cutting plane algorithms in combinatorial optimization." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 111–52. Providence, Rhode Island: American Mathematical Society, 1995. http://dx.doi.org/10.1090/dimacs/020/02.

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Roux, Olivier, Cyril Fonlupt, and Denis Robilliard. "Co-operative Improvement for a Combinatorial Optimization Algorithm." In Lecture Notes in Computer Science, 231–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10721187_17.

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Radhakrishnan, Anisha, and G. Jeyakumar. "Evolutionary Algorithm for Solving Combinatorial Optimization—A Review." In Innovations in Computer Science and Engineering, 539–45. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4543-0_57.

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Elf, Matthias, Carsten Gutwenger, Michael Jünger, and Giovanni Rinaldi. "Branch-and-Cut Algorithms for Combinatorial Optimization and Their Implementation in ABACUS." In Lecture Notes in Computer Science, 157–222. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45586-8_5.

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Li, Nan, and Yi Luo. "An Improved Co-Evolution Genetic Algorithm for Combinatorial Optimization Problems." In Lecture Notes in Computer Science, 506–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21515-5_60.

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Conference papers on the topic "Combinatorial optimization Computer algorithms"

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Venkataraman, Ganesh, Zhuo Feng, Jiang Hu, and Peng Li. "Combinatorial Algorithms for Fast Clock Mesh Optimization." In 2006 IEEE/ACM International Conference on Computer Aided Design. IEEE, 2006. http://dx.doi.org/10.1109/iccad.2006.320175.

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Ibrahimpur, Sharat, and Chaitanya Swamy. "Approximation Algorithms for Stochastic Minimum-Norm Combinatorial Optimization." In 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2020. http://dx.doi.org/10.1109/focs46700.2020.00094.

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Liu, Jihong, and Sen Zeng. "A Survey of Assembly Planning Based on Intelligent Optimization Algorithms." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49445.

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Assembly planning is one of the NP complete problems, which is even more difficult to solve for complex products. Intelligent optimization algorithms have obvious advantages to deal with such combinatorial problems. Various intelligent optimization algorithms have been applied to assembly sequence planning and optimization in the last decade. This paper surveys the state-of-the-art of the assembly planning methods based on the intelligent optimization algorithms. Five intelligent optimization algorithms, i.e. genetic algorithm (GA), artificial neural networks (ANN), simulated annealing (SA), ant colony algorithm (ACO) and artificial immune algorithm (AIA), and their applications in assembly planning and optimization are introduced respectively. The application features of the algorithms are summarized. At last, the future research directions of the assembly planning based on the intelligent optimization algorithms are discussed.
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Ding, Hua-fu, Xiao-lu Liu, and Xue Liu. "An improved genetic algorithm for combinatorial optimization." In 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2011. http://dx.doi.org/10.1109/csae.2011.5953170.

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Yakovlev, Sergiy, Oleksiy Kartashov, and Olga Yarovaya. "On Class of Genetic Algorithms in Optimization Problems on Combinatorial Configurations." In 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2018. http://dx.doi.org/10.1109/stc-csit.2018.8526746.

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Ramaswamy, Vasu, and Vadim Shapiro. "Combinatorial Laws for Physically Meaningful Design." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dtm-48654.

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A typical computer representation of a design includes geometric and physical information organized in a suitable combinatorial data structure. Queries and transformations of these design representations are used to formulate most algorithms in computational design, including analysis, optimization, evolution, generation, and synthesis. Formal properties, and in particular existence and validity of the computed solutions, must be assured and preserved by all such algorithms. Using tools from algebraic topology, we show that a small set of the usual combinatorial operators: boundary (∂), coboundary (δ), and dualization (*) — are sufficient to represent a variety of physical laws and invariants. Specific examples include geometric integrity, balance and equilibrium, and surface smoothing. Our findings point a way toward systematic development of data structures and algorithms for design in a common formal computational framework.
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Taian, Liu, Wang Yunjia, and Liu Wentong. "Research on Least Squares Support Vector Machine Combinatorial Optimization Algorithm." In 2009 International Forum on Computer Science-Technology and Applications. IEEE, 2009. http://dx.doi.org/10.1109/ifcsta.2009.116.

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Jin, Jin, Zhong Ma, Lin Xue, and Changhui Tian. "A New Cluster Analysis Based on Combinatorial Particle Swarm Optimization Algorithm." In International Conference on Education, Management, Computer and Society. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/emcs-16.2016.114.

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Callanan, Jesse, Oladapo Ogunbodede, Maulikkumar Dhameliya, Jun Wang, and Rahul Rai. "Hierarchical Combinatorial Design and Optimization of Quasi-Periodic Metamaterial Structures." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85914.

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As advanced manufacturing techniques such as additive manufacturing become widely available, it is of interest to investigate the potential advantages that arise when designing periodic metamaterials to achieve a specific desired behavior or physical property. Designing the fine scale detailed geometry of periodic metamaterials to achieve a specified behavior falls under the category of notoriously intractable inverse problems. To simplify solving the inverse problem, most relevant works represent metamaterials as periodic single unit cell structures repeated in regular lattices. Such representation simplifies modeling and simulation task but at the cost of possibly limiting the range of physical behaviors that can be achieved through the use of more than one unit cell structures. This article outlines a quasi-periodic representation that utilizes more than a single unit cell to generate periodic metamaterials. Additionally, a hierarchical optimization scheme to optimize the generating function for a quasi-periodic structure using the genetic algorithm (GA) and a barrier function interior point method is also sketched to solve the inverse problem. To demonstrate the utility of the proposed hierarchical optimization framework to solve quasi-periodic metamaterial inverse problem, a problem in which the objective is to minimize the total strain in the structure while subjected to weight and the total-size constraint is considered. We detail the overall computational approach in which geometric representation, optimization algorithms, and finite element analysis are coupled and report preliminary numerical experiments.
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10

Shellshear, Evan, Johan S. Carlson, and Robert Bohlin. "A Combinatorial Packing Algorithm and Standard Trunk Geometry for ISO Luggage Packing." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70778.

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Automated packing algorithms for luggage compartments in automobiles are of great interest. The difficulty of automatically computing the volume of a mesh representation of a boot according to the ISO 3832 standard restricts the design of vehicles required to meet minimal trunk volume specifications and also increases the cost of physical and virtual verification of the original design specifications. In our paper we present a new heuristic combinatorial packing algorithm for the ISO luggage packing standard. The algorithm presents numerous advantages over previous algorithms in terms of its simplicity and speed as well as producing high density of packed objects. The algorithm also solves the problem of requiring a fixed grid structure to position discrete objects in the boot and can also be used as an additional optimization on existing algorithms. In addition, we also provide the first comparison of state of the art packing algorithms for a simplified trunk geometry and propose a standard trunk geometry to enable future researchers to compare their results with other packing algorithms.
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Reports on the topic "Combinatorial optimization Computer algorithms"

1

Plotkin, Serge. Research in Graph Algorithms and Combinatorial Optimization. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada292630.

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2

Shepherd, F. B. Fundamentals of Combinatorial Optimization and Algorithms Design: December Report. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada429923.

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3

Kennington, Jeffrey L. Optimization Algorithms for New Computer Architectures with Applications to Routing and Scheduling. Fort Belvoir, VA: Defense Technical Information Center, February 1992. http://dx.doi.org/10.21236/ada251959.

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