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Relevant bibliographies by topics / Combinatorial and linear optimization

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Academic literature on the topic 'Combinatorial and linear optimization'

Author: Grafiati

Published: 30 November 2024

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Journal articles on the topic "Combinatorial and linear optimization"

1

Yannakakis, Mihalis. "Expressing combinatorial optimization problems by Linear Programs." Journal of Computer and System Sciences 43, no. 3 (1991): 441–66. http://dx.doi.org/10.1016/0022-0000(91)90024-y.

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Donets, Georgy, and Vasyl Biletskyi. "On Some Optimization Problems on Permutations." Cybernetics and Computer Technologies, no. 1 (June 30, 2022): 5–10. http://dx.doi.org/10.34229/2707-451x.22.1.1.

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Numerous studies consider combinatorial optimization problems and their solution methods, since a large number of practical problems are described by means of combinatorial optimization models. Among these problems, the most prominent ones are function optimization problems on combinatorial configurations. Many of the studies mentioned above propose approaches and describe methods to solve combinatorial optimization problems for linear and fractionally linear functions on combinatorial sets such as permutations and arrangements. This work describes new approaches and methods to solve some maxi

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DE FARIAS, I. R., E. L. JOHNSON, and G. L. NEMHAUSER. "Branch-and-cut for combinatorial optimization problems without auxiliary binary variables." Knowledge Engineering Review 16, no. 1 (2001): 25–39. http://dx.doi.org/10.1017/s0269888901000030.

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Many optimisation problems involve combinatorial constraints on continuous variables. An example of a combinatorial constraint is that at most one variable in a group of nonnegative variables may be positive. Traditionally, in the mathematical programming community, such problems have been modeled as mixed-integer programs by introducing auxiliary binary variables and additional constraints. Because the number of variables and constraints becomes larger and the combinatorial structure is not used to advantage, these mixed-integer programming models may not be solved satisfactorily, except for

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Barbolina, Tetiana. "Estimates of objective function minimum for solving linear fractional unconstrained combinatorial optimization problems on arrangements." Physico-mathematical modelling and informational technologies, no. 32 (July 6, 2021): 32–36. http://dx.doi.org/10.15407/fmmit2021.32.055.

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The paper is devoted to the study of one class of Euclidean combinatorial optimization problems — combinatorial optimization problems on the general set of arrangements with linear fractional objective function and without additional (non-combinatorial) constraints. The paper substantiates the improvement of the polynomial algorithm for solving the specified class of problems. This algorithm foresees solving a finite sequence of linear unconstrained problems of combinatorial optimization on arrangements. The modification of the algorithm is based on the use of estimates of the objective functi

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Pichugina, Oksana, and Liudmyla Koliechkina. "Linear constrained combinatorial optimization on well-described sets." IOP Conference Series: Materials Science and Engineering 1099, no. 1 (2021): 012064. http://dx.doi.org/10.1088/1757-899x/1099/1/012064.

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Donets, G. A., and V. I. Biletskyi. "On the Problem of a Linear Function Localization on Permutations." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 14–18. http://dx.doi.org/10.34229/2707-451x.20.2.2.

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Combinatorial optimization problems and methods of their solution have been a subject of numerous studies, since a large number of practical problems are described by combinatorial optimization models. Many studies consider approaches to and describe methods of solution for combinatorial optimization problems with linear or fractionally linear target functions on combinatorial sets such as permutations and arrangements. Studies consider solving combinatorial problems by means of well-known methods, as well as developing new methods and algorithms of searching a solution. We describe a method o

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Engau, Alexander, Miguel F. Anjos, and Anthony Vannelli. "On Interior-Point Warmstarts for Linear and Combinatorial Optimization." SIAM Journal on Optimization 20, no. 4 (2010): 1828–61. http://dx.doi.org/10.1137/080742786.

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Chung, Sung-Jin, Horst W. Hamacher, Francesco Maffioli, and Katta G. Murty. "Note on combinatorial optimization with max-linear objective functions." Discrete Applied Mathematics 42, no. 2-3 (1993): 139–45. http://dx.doi.org/10.1016/0166-218x(93)90043-n.

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Borissova, Daniela, Ivan Mustakerov, and Lyubka Doukovska. "Predictive Maintenance Sensors Placement by Combinatorial Optimization." International Journal of Electronics and Telecommunications 58, no. 2 (2012): 153–58. http://dx.doi.org/10.2478/v10177-012-0022-6.

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Predictive Maintenance Sensors Placement by Combinatorial Optimization The strategy of predictive maintenance monitoring is important for successful system damage detection. Maintenance monitoring utilizes dynamic response information to identify the possibility of damage. The basic factors of faults detection analysis are related to properties of the structure under inspection, collect the signals and appropriate signals processing. In vibration control, structures response sensing is limited by the number of sensors or the number of input channels of the data acquisition system. An essential

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Mandi, Jayanta, Emir Demirovi?, Peter J. Stuckey, and Tias Guns. "Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1603–10. http://dx.doi.org/10.1609/aaai.v34i02.5521.

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Combinatorial optimization assumes that all parameters of the optimization problem, e.g. the weights in the objective function, are fixed. Often, these weights are mere estimates and increasingly machine learning techniques are used to for their estimation. Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems. It takes the regret of the predictions on the linear problem into account, by repeatedly solving it during learning. We investigate the use of SPO to solve more reali

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Dissertations / Theses on the topic "Combinatorial and linear optimization"

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Salazar-Neumann, Martha. "Advances in robust combinatorial optimization and linear programming." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210192.

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La construction de modèles qui protègent contre les incertitudes dans les données, telles que la variabilité de l'information et l'imprécision est une des principales préoccupations en optimisation sous incertitude. L'incertitude peut affecter différentes domaines, comme le transport, les télécommunications, la finance, etc. ainsi que les différentes parts d'un problème d'optimisation, comme les coefficients de la fonction objectif et /ou les contraintes. De plus, l'ensemble des données incertaines peut être modélisé de différentes façons, comme sous ensembles compactes et convexes de l´espace

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Iemets, O. O., and T. M. Barbolina. "Linear-fractional combinatorial optimization problems: model and solving." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46962.

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Euclidean problems of linear-fractional combinatorial optimization on arrangements are discussed. Authors propose the model of practical problem. Also the polynomial algorithm for solving linear-fractional combinatorial optimization problems on arrangements is discussed.

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Cheng, Jianqiang. "Stochastic Combinatorial Optimization." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112261.

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Dans cette thèse, nous étudions trois types de problèmes stochastiques : les problèmes avec contraintes probabilistes, les problèmes distributionnellement robustes et les problèmes avec recours. Les difficultés des problèmes stochastiques sont essentiellement liées aux problèmes de convexité du domaine des solutions, et du calcul de l’espérance mathématique ou des probabilités qui nécessitent le calcul complexe d’intégrales multiples. A cause de ces difficultés majeures, nous avons résolu les problèmes étudiées à l’aide d’approximations efficaces.Nous avons étudié deux types de problèmes stoch

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Burer, Samuel A. "New algorithmic approaches for semidefinite programming with applications to combinatorial optimization." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/30268.

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Sidford, Aaron Daniel. "Iterative methods, combinatorial optimization, and linear programming beyond the universal barrier." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99848.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 256-266).<br>In this thesis we consider fundamental problems in continuous and combinatorial optimization that occur pervasively in practice and show how to improve upon the best known theoretical running times for solving these problems across a broad range of parameters. Using and improving techniques from diverse disciplines including spectral graph theory, numerical analysis, data struc

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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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<p>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.</p><p>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.</p><p>We focus on solving graph games by using iterative stra

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Ferroni, Nicola. "Exact Combinatorial Optimization with Graph Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17502/.

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Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly lar

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Weltge, Stefan [Verfasser], and Volker [Akademischer Betreuer] Kaibel. "Sizes of linear descriptions in combinatorial optimization / Stefan Weltge. Betreuer: Volker Kaibel." Magdeburg : Universitätsbibliothek, 2015. http://d-nb.info/1082625868/34.

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Wang, Xia. "Applications of genetic algorithms, dynamic programming, and linear programming to combinatorial optimization problems." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8778.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Applied Mathematics & Statistics, and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Chakrabarty, Deeparnab. "Algorithmic aspects of connectivity, allocation and design problems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24659.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2008.<br>Committee Chair: Vazirani, Vijay; Committee Member: Cook, William; Committee Member: Kalai, Adam; Committee Member: Tetali, Prasad; Committee Member: Thomas, Robin

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Books on the topic "Combinatorial and linear optimization"

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Pardalos, P. M. Handbook of combinatorial optimization. Springer, 2013.

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Dingzhu, Du, and Pardalos P. M. 1954-, eds. Handbook of combinatorial optimization. Kluwer Academic Publishers, 1998.

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MacGregor Smith, J. Combinatorial, Linear, Integer and Nonlinear Optimization Apps. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75801-1.

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Diaby, Moustapha. Advances in combinatorial optimization: Linear programming formulation of the traveling salesman and other hard combinatorial optimization problems. World Scientific, 2015.

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Padberg, Manfred. Linear Optimization and Extensions. Springer Berlin Heidelberg, 1999.

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Gerhard, Reinelt, and SpringerLink (Online service), eds. The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization. Springer-Verlag Berlin Heidelberg, 2011.

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Strobach, Peter. Linear Prediction Theory: A Mathematical Basis for Adaptive Systems. Springer Berlin Heidelberg, 1990.

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Jonas, Mockus, ed. Bayesian heuristic approach to discrete and global optimization: Algorithms, visualization, software, and applications. Kluwer Academic Publishers, 1997.

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Mahjoub, A. Ridha, Vangelis Markakis, Ioannis Milis, and Vangelis Th Paschos, eds. Combinatorial Optimization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32147-4.

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Korte, Bernhard, and Jens Vygen. Combinatorial Optimization. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56039-6.

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Book chapters on the topic "Combinatorial and linear optimization"

1

Akgül, Mustafa. "The Linear Assignment Problem." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_5.

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Yang, Kai, and Katta G. Murty. "Surrogate Constraint Methods for Linear Inequalities." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_2.

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Dongarra, Jack, and Jerzy Waśniewski. "High Performance Linear Algebra Package - LAPACK90." In Combinatorial Optimization. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3282-4_11.

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Nemhauser, George, and Laurence Wolsey. "Linear Programming." In Integer and Combinatorial Optimization. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118627372.ch2.

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Du, Ding-Zhu, Panos Pardalos, Xiaodong Hu, and Weili Wu. "Linear Programming." In Introduction to Combinatorial Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10596-8_6.

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Padberg, Manfred. "Combinatorial Optimization: An Introduction." In Linear Optimization and Extensions. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-12273-0_10.

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Alevras, Dimitres, and Manfred W.Padberg. "Combinatorial Optimization: An Introduction." In Linear Optimization and Extensions. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56628-8_10.

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Pinar, Mustafa Ç., and Stavros A. Zenios. "Solving Large Scale Multicommodity Networks Using Linear—Quadratic Penalty Functions." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_12.

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Bilbao, Jesús Mario. "Linear optimization methods." In Cooperative Games on Combinatorial Structures. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4393-0_2.

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den Hertog, D., C. Roos, and T. Terlaky. "The Linear Complementary Problem, Sufficient Matrices and the Criss-Cross Method." In Combinatorial Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_18.

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Conference papers on the topic "Combinatorial and linear optimization"

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Ma, Hyunjun, and Q.-Han Park. "Constraint-Driven Method for Combinatorial Optimization." In 2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR). IEEE, 2024. http://dx.doi.org/10.1109/cleo-pr60912.2024.10676554.

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Yannakakis, Mihalis. "Expressing combinatorial optimization problems by linear programs." In the twentieth annual ACM symposium. ACM Press, 1988. http://dx.doi.org/10.1145/62212.62232.

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Baioletti, Marco, Alfredo Milani, and Valentino Santucci. "Linear Ordering Optimization with a Combinatorial Differential Evolution." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.373.

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Jin, Chen, Qiang Fu, Huahua Wang, et al. "Solving combinatorial optimization problems using relaxed linear programming." In the 2nd International Workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2501221.2501227.

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Louchet, J., R. Mathurin, and B. Rottembourg. "Combinatorial optimization and linear prediction approaches to rain cell tracking." In 26th AIPR Workshop: Exploiting New Image Sources and Sensors, edited by J. Michael Selander. SPIE, 1998. http://dx.doi.org/10.1117/12.300045.

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Gadallah, M. H., and H. A. ElMaraghy. "A New Algorithm for Combinatorial Optimization." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0059.

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Abstract A new algorithm for combinatorial search optimization is developed. This algorithm is based on orthogonal arrays as planning schemes and search graph techniques as representation schemes. Based on the algorithm, a discrete formulation is given to model two search domains. As an application, the algorithm is used to deal with the problem of least cost tolerance allocation with optimum process selection. Studies are performed to compare between different orthogonal array and column assignment and number of design levels with respect to optimum. The proposed algorithm is capable of deali

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Beniwal, Gautam, and Mohammad Rizwanullah. "Combinatorial Optimization of Non-linear Multicommodity Network Flow Using Pseudo Quasi-Newton Method." In 2022 International Conference on Computational Modelling, Simulation and Optimization (ICCMSO). IEEE, 2022. http://dx.doi.org/10.1109/iccmso58359.2022.00041.

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Drori, Iddo, Anant Kharkar, William R. Sickinger, et al. "Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00013.

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Quan, Ning, and Harrison Kim. "A Tight Upper Bound for Grid-Based Wind Farm Layout Optimization." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59712.

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This paper uses the method developed by Billionnet et al. (1999) to obtain tight upper bounds on the optimal values of mixed integer linear programming (MILP) formulations in grid-based wind farm layout optimization. The MILP formulations in grid-based wind farm layout optimization can be seen as linearized versions of the 0-1 quadratic knapsack problem (QKP) in combinatorial optimization. The QKP is NP-hard, which means the MILP formulations remain difficult problems to solve, especially for large problems with grid sizes of more than 500 points. The upper bound method proposed by Billionnet

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Mitra, Mainak, Alparslan Emrah Bayrak, Stefano Zucca, and Bogdan I. Epureanu. "A Sensitivity Based Heuristic for Optimal Blade Arrangement in a Linear Mistuned Rotor." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75542.

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This paper investigates methodologies for finding optimal or near-optimal blade arrangements in a bladed disk with inserted blades for minimizing or maximizing blade response amplification due to mistuning in material properties of the blades. The mistuning in the blades is considered to be known, and only their arrangement is modifiable. Hence, this is a problem in discrete optimization, particularly combinatorial optimization where the objective of response amplification is a nonlinear function of the blade arrangement. Previous studies have treated mistuning as a continuous parameter to ana

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Reports on the topic "Combinatorial and linear optimization"

1

Bixby, Robert E. Notes on Combinatorial Optimization. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada455247.

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Coffrin, Carleton James. Combinatorial Optimization on D-Wave. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1454977.

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Radzik, Thomas. Newton's Method for Fractional Combinatorial Optimization,. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada323687.

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GEORGE MASON UNIV FAIRFAX VA. Solving Large-Scale Combinatorial Optimization Problems. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada327597.

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Hoffman, Karla L. Solution Procedures for Large-Scale Combinatorial Optimization. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada278242.

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

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Wets, Roger D. Parametric and Combinatorial Problems in Constrained Optimization. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada264229.

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Shepherd, Bruce, Peter Winkler, and Chandra Chekuri. Fundamentals of Combinatorial Optimization and Algorithm Design. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada423042.

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Parekh, Ojas, Robert D. Carr, and David Pritchard. LDRD final report : combinatorial optimization with demands. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1055603.

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Jaillet, Patrick. Data-Driven Online and Real-Time Combinatorial Optimization. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada592939.

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