Literatura académica sobre el tema "Linear programming"
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Artículos de revistas sobre el tema "Linear programming"
S. Mohan, S. Mohan y Dr S. Sekar Dr. S. Sekar. "Linear Programming Problem with Homogeneous Constraints". Indian Journal of Applied Research 4, n.º 3 (1 de octubre de 2011): 298–307. http://dx.doi.org/10.15373/2249555x/mar2014/90.
Texto completoD, Sempavazhaam, Jothi K y Kamali S. Nandhini A. Princy Rebekah J. "A Study on Linear Programming Problem". International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (28 de febrero de 2019): 903–4. http://dx.doi.org/10.31142/ijtsrd21539.
Texto completoMahjoub Mohammed Hussein, Elfarazdag. "Application of Linear Programming (Transportation Problem)". International Journal of Science and Research (IJSR) 12, n.º 3 (5 de marzo de 2023): 452–54. http://dx.doi.org/10.21275/sr21222020051.
Texto completoAsquith, J. y Vasek Chvatal. "Linear Programming". Mathematical Gazette 69, n.º 448 (junio de 1985): 151. http://dx.doi.org/10.2307/3616957.
Texto completoCaoimh, C. C. O. y Howard Karloff. "Linear Programming". Mathematical Gazette 79, n.º 484 (marzo de 1995): 245. http://dx.doi.org/10.2307/3620128.
Texto completoDantzig, George B. "Linear Programming". Operations Research 50, n.º 1 (febrero de 2002): 42–47. http://dx.doi.org/10.1287/opre.50.1.42.17798.
Texto completoŘímánek, Josef. "Linear programming". European Journal of Operational Research 21, n.º 2 (agosto de 1985): 277–78. http://dx.doi.org/10.1016/0377-2217(85)90046-3.
Texto completoQi, Zhiquan, Yingjie Tian y Yong Shi. "Regularized Multiple Criteria Linear Programming via Linear Programming". Procedia Computer Science 9 (2012): 1234–39. http://dx.doi.org/10.1016/j.procs.2012.04.134.
Texto completoDangerfield, Janet y B. D. Bunday. "Basic Linear Programming". Mathematical Gazette 69, n.º 450 (diciembre de 1985): 317. http://dx.doi.org/10.2307/3617607.
Texto completoMegiddo, N. "Linear Programming (1986)". Annual Review of Computer Science 2, n.º 1 (junio de 1987): 119–45. http://dx.doi.org/10.1146/annurev.cs.02.060187.001003.
Texto completoTesis sobre el tema "Linear programming"
Andreotti, Sandro [Verfasser]. "Linear Programming and Integer Linear Programming in Bioinformatics / Sandro Andreotti". Berlin : Freie Universität Berlin, 2015. http://d-nb.info/1066645213/34.
Texto completoSarrabezolles, Pauline. "Colourful linear programming". Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1033/document.
Texto completoThe colorful Carathéodory theorem, proved by Bárány in 1982, states the following. Given d Å1 sets of points S1, . . . ,SdÅ1 µ Rd , each of them containing 0 in its convex hull, there exists a colorful set T containing 0 in its convex hull, i.e. a set T µ SdÅ1 iÆ1 Si such that jT \Si j • 1 for all i and such that 0 2 conv(T ). This result gave birth to several questions, some algorithmic and some more combinatorial. This thesis provides answers on both aspects. The algorithmic questions raised by the colorful Carathéodory theorem concern, among other things, the complexity of finding a colorful set under the condition of the theorem, and more generally of deciding whether there exists such a colorful set when the condition is not satisfied. In 1997, Bárány and Onn defined colorful linear programming as algorithmic questions related to the colorful Carathéodory theorem. The two questions we just mentioned come under colorful linear programming. This thesis aims at determining which are the polynomial cases of colorful linear programming and which are the harder ones. New complexity results are obtained, refining the sets of undetermined cases. In particular, we discuss some combinatorial versions of the colorful Carathéodory theorem from an algorithmic point of view. Furthermore, we show that computing a Nash equilibrium in a bimatrix game is polynomially reducible to a colorful linear programming problem. On our track, we found a new way to prove that a complementarity problem belongs to the PPAD class with the help of Sperner’s lemma. Finally, we present a variant of the “Bárány-Onn” algorithm, which is an algorithmcomputing a colorful set T containing 0 in its convex hull whose existence is ensured by the colorful Carathéodory theorem. Our algorithm makes a clear connection with the simplex algorithm. After a slight modification, it also coincides with the Lemke method, which computes a Nash equilibriumin a bimatrix game. The combinatorial question raised by the colorful Carathéodory theorem concerns the number of positively dependent colorful sets. Deza, Huang, Stephen, and Terlaky (Colourful simplicial depth, Discrete Comput. Geom., 35, 597–604 (2006)) conjectured that, when jSi j Æ d Å1 for all i 2 {1, . . . ,d Å1}, there are always at least d2Å1 colourful sets containing 0 in their convex hulls. We prove this conjecture with the help of combinatorial objects, known as the octahedral systems. Moreover, we provide a thorough study of these objects
Espinoza, Daniel G. "On Linear Programming, Integer Programming and Cutting Planes". Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10482.
Texto completoWei, Hua. "Numerical Stability in Linear Programming and Semidefinite Programming". Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2922.
Texto completoWe start with the error bound analysis of the search directions for the normal equation approach for LP. Our error analysis explains the surprising fact that the ill-conditioning is not a significant problem for the normal equation system. We also explain why most of the popular LP solvers have a default stop tolerance of only 10-8 when the machine precision on a 32-bit computer is approximately 10-16.
We then propose a simple alternative approach for the normal equation based interior-point method. This approach has better numerical stability than the normal equation based method. Although, our approach is not competitive in terms of CPU time for the NETLIB problem set, we do obtain higher accuracy. In addition, we obtain significantly smaller CPU times compared to the normal equation based direct solver, when we solve well-conditioned, huge, and sparse problems by using our iterative based linear solver. Additional techniques discussed are: crossover; purification step; and no backtracking.
Finally, we present an algorithm to construct SDP problem instances with prescribed strict complementarity gaps. We then introduce two measures of strict complementarity gaps. We empirically show that: (i) these measures can be evaluated accurately; (ii) the size of the strict complementarity gaps correlate well with the number of iteration for the SDPT3 solver, as well as with the local asymptotic convergence rate; and (iii) large strict complementarity gaps, coupled with the failure of Slater's condition, correlate well with loss of accuracy in the solutions. In addition, the numerical tests show that there is no correlation between the strict complementarity gaps and the geometrical measure used in [31], or with Renegar's condition number.
Wolf, Jan [Verfasser]. "Quantified Linear Programming / Jan Wolf". Aachen : Shaker, 2015. http://d-nb.info/1074087275/34.
Texto completoSjöström, Henrik. "Pivoting methods for linear programming". Thesis, KTH, Matematik (Inst.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98977.
Texto completoPrice, C. J. "Non-linear semi-infinite programming". Thesis, University of Canterbury. Mathematics and Statistics, 1992. http://hdl.handle.net/10092/7920.
Texto completoWu, S. Y. "Linear programming on measure spaces". Thesis, University of Cambridge, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372925.
Texto completoDIAS, DOUGLAS MOTA. "QUANTUM-INSPIRED LINEAR GENETIC PROGRAMMING". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17544@1.
Texto completoA superioridade de desempenho dos algoritmos quânticos, em alguns problemas específicos, reside no uso direto de fenômenos da mecânica quântica para realizar operações com dados em computadores quânticos. Esta característica fez surgir uma nova abordagem, denominada Computação com Inspiração Quântica, cujo objetivo é criar algoritmos clássicos (executados em computadores clássicos) que tirem proveito de princípios da mecânica quântica para melhorar seu desempenho. Neste sentido, alguns algoritmos evolutivos com inspiração quântica tem sido propostos e aplicados com sucesso em problemas de otimização combinatória e numérica, apresentando desempenho superior àquele dos algoritmos evolutivos convencionais, quanto à melhoria da qualidade das soluções e à redução do número de avaliações necessárias para alcançá-las. Até o presente momento, no entanto, este novo paradigma de inspiração quântica ainda não havia sido aplicado à Programação Genética (PG), uma classe de algoritmos evolutivos que visa à síntese automática de programas de computador. Esta tese propõe, desenvolve e testa um novo modelo de algoritmo evolutivo com inspiração quântica, denominado Programação Genética Linear com Inspiração Quântica (PGLIQ), para a evolução de programas em código de máquina. A Programação Genética Linear é assim denominada porque cada um dos seus indivíduos é representado por uma lista de instruções (estruturas lineares), as quais são executadas sequencialmente. As contribuições deste trabalho são o estudo e a formulação inédita do uso do paradigma da inspiração quântica na síntese evolutiva de programas de computador. Uma das motivações para a opção pela evolução de programas em código de máquina é que esta é a abordagem de PG que, por oferecer a maior velocidade de execução, viabiliza experimentos em larga escala. O modelo proposto é inspirado em sistemas quânticos multiníveis e utiliza o qudit como unidade básica de informação quântica, o qual representa a superposição dos estados de um sistema deste tipo. O funcionamento do modelo se baseia em indivíduos quânticos, que representam a superposição de todos os programas do espaço de busca, cuja observação gera indivíduos clássicos e os programas (soluções). Nos testes são utilizados problemas de regressão simbólica e de classificação binária para se avaliar o desempenho da PGLIQ e compará-lo com o do modelo AIMGP (Automatic Induction of Machine Code by Genetic Programming), considerado atualmente o modelo de PG mais eficiente na evolução de código de máquina, conforme citado em inúmeras referências bibliográficas na área. Os resultados mostram que a Programação Genética Linear com Inspiração Quântica (PGLIQ) apresenta desempenho geral superior nestas classes de problemas, ao encontrar melhores soluções (menores erros) a partir de um número menor de avaliações, com a vantagem adicional de utilizar um número menor de parâmetros e operadores que o modelo de referência. Nos testes comparativos, o modelo mostra desempenho médio superior ao do modelo de referência para todos os estudos de caso, obtendo erros de 3 a 31% menores nos problemas de regressão simbólica, e de 36 a 39% nos problemas de classificação binária. Esta pesquisa conclui que o paradigma da inspiração quântica pode ser uma abordagem competitiva para se evoluir programas eficientemente, encorajando o aprimoramento e a extensão do modelo aqui apresentado, assim como a criação de outros modelos de programação genética com inspiração quântica.
The superior performance of quantum algorithms in some specific problems lies in the direct use of quantum mechanics phenomena to perform operations with data on quantum computers. This feature has originated a new approach, named Quantum-Inspired Computing, whose goal is to create classic algorithms (running on classical computers) that take advantage of quantum mechanics principles to improve their performance. In this sense, some quantum-inspired evolutionary algorithms have been proposed and successfully applied in combinatorial and numerical optimization problems, presenting a superior performance to that of conventional evolutionary algorithms, by improving the quality of solutions and reducing the number of evaluations needed to achieve them. To date, however, this new paradigm of quantum inspiration had not yet been applied to Genetic Programming (GP), a class of evolutionary algorithms that aims the automatic synthesis of computer programs. This thesis proposes, develops and tests a novel model of quantum-inspired evolutionary algorithm named Quantum-Inspired Linear Genetic Programming (QILGP) for the evolution of machine code programs. Linear Genetic Programming is so named because each of its individuals is represented by a list of instructions (linear structures), which are sequentially executed. The contributions of this work are the study and formulation of the novel use of quantum inspiration paradigm on evolutionary synthesis of computer programs. One of the motivations for choosing by the evolution of machine code programs is because this is the GP approach that, by offering the highest speed of execution, makes feasible large-scale experiments. The proposed model is inspired on multi-level quantum systems and uses the qudit as the basic unit of quantum information, which represents the superposition of states of such a system. The model’s operation is based on quantum individuals, which represent a superposition of all programs of the search space, whose observation leads to classical individuals and programs (solutions). The tests use symbolic regression and binary classification problems to evaluate the performance of QILGP and compare it with the AIMGP model (Automatic Induction of Machine Code by Genetic Programming), which is currently considered the most efficient GP model to evolve machine code, as cited in numerous references in this field. The results show that Quantum-Inspired Linear Genetic Programming (QILGP) presents superior overall performance in these classes of problems, by achieving better solutions (smallest error) from a smaller number of evaluations, with the additional advantage of using a smaller number of parameters and operators that the reference model. In comparative tests, the model shows average performance higher than that of the reference model for all case studies, achieving errors 3-31% lower in the problems of symbolic regression, and 36-39% in the binary classification problems. This research concludes that the quantum inspiration paradigm can be a competitive approach to efficiently evolve programs, encouraging the improvement and extension of the model presented here, as well as the creation of other models of quantum-inspired genetic programming.
Ramadan, Khaled Carleton University Dissertation Mathematics and Statistics. "Linear programming with interval coefficients". Ottawa, 1996.
Buscar texto completoLibros sobre el tema "Linear programming"
Darst, Richard B. Introduction to linear programming: Applications and extensions. New York: M. Dekker, 1991.
Buscar texto completoKarloff, Howard. Linear Programming. Boston, MA: Birkhäuser Boston, 1991. http://dx.doi.org/10.1007/978-0-8176-4844-2.
Texto completoSaigal, Romesh. Linear Programming. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2311-6.
Texto completoVanderbei, Robert J. Linear Programming. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39415-8.
Texto completoVanderbei, Robert J. Linear Programming. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4614-7630-6.
Texto completoVanderbei, Robert J. Linear Programming. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5662-3.
Texto completoVanderbei, Robert J. Linear Programming. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74388-2.
Texto completoIgnizio, James P. Linear programming. Englewood Cliffs, N.J: Prentice Hall, 1994.
Buscar texto completoWisniewski, Mik. Linear programming. Basingstoke: Palgrave, 2001.
Buscar texto completoFeiring, Bruce. Linear Programming. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1986. http://dx.doi.org/10.4135/9781412984751.
Texto completoCapítulos de libros sobre el tema "Linear programming"
Luc, Dinh The. "Linear Programming". En Multiobjective Linear Programming, 49–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21091-9_3.
Texto completoBhatti, M. Asghar. "Linear Programming". En Practical Optimization Methods, 315–436. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-0501-2_6.
Texto completoBlum, Lenore, Felipe Cucker, Michael Shub y Steve Smale. "Linear Programming". En Complexity and Real Computation, 275–96. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0701-6_15.
Texto completoPoler, Raúl, Josefa Mula y Manuel Díaz-Madroñero. "Linear Programming". En Operations Research Problems, 1–48. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5577-5_1.
Texto completoHolden, K. y A. W. Pearson. "Linear Programming". En Introductory Mathematics for Economics and Business, 297–318. London: Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-22357-2_8.
Texto completoČepin, Marko. "Linear Programming". En Assessment of Power System Reliability, 249–52. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-688-7_16.
Texto completoShimizu, Kiyotaka, Yo Ishizuka y Jonathan F. Bard. "Linear Programming". En Nondifferentiable and Two-Level Mathematical Programming, 128–87. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6305-1_5.
Texto completoDiwekar, Urmila. "Linear Programming". En Introduction to Applied Optimization, 1–29. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76635-5_2.
Texto completoFlorenzano, Monique y Cuong Le Van. "Linear Programming". En Studies in Economic Theory, 51–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56522-9_4.
Texto completoWoodford, C. y C. Phillips. "Linear Programming". En Numerical Methods with Worked Examples: Matlab Edition, 135–67. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1366-6_7.
Texto completoActas de conferencias sobre el tema "Linear programming"
Flanagan, Mark F. "Linear-programming receivers". En 2008 46th Annual Allerton Conference on Communication, Control, and Computing. IEEE, 2008. http://dx.doi.org/10.1109/allerton.2008.4797568.
Texto completoXu, Zhiming, Yu Bai y Shuning Wang. "Sequential Global Linear Programming Algorithm for Continuous Piecewise Linear Programming*". En 2018 13th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2018. http://dx.doi.org/10.1109/wcica.2018.8630336.
Texto completoPaolini, Luca y Mauro Piccolo. "Semantically linear programming languages". En the 10th international ACM SIGPLAN symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389449.1389462.
Texto completoVaidya, Jaideep. "Privacy-preserving linear programming". En the 2009 ACM symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1529282.1529729.
Texto completoTaghavi, Mohammad y Paul Siegel. "Adaptive Linear Programming Decoding". En 2006 IEEE International Symposium on Information Theory. IEEE, 2006. http://dx.doi.org/10.1109/isit.2006.262071.
Texto completoRamos, Edgar A. "Linear programming queries revisited". En the sixteenth annual symposium. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/336154.336198.
Texto completoYang, Jihui. "Fuzzy relation linear programming". En 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255037.
Texto completoGhosh, Arka, Piotr Hofman y Sławomir Lasota. "Orbit-finite linear programming". En 2023 38th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS). IEEE, 2023. http://dx.doi.org/10.1109/lics56636.2023.10175799.
Texto completoPapadimitriou, Christos H. y Mihalis Yannakakis. "Linear programming without the matrix". En the twenty-fifth annual ACM symposium. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/167088.167127.
Texto completoSyed, Umar, Michael Bowling y Robert E. Schapire. "Apprenticeship learning using linear programming". En the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390286.
Texto completoInformes sobre el tema "Linear programming"
Bixby, Robert E. Linear Programming Tools for Integer Programming. Fort Belvoir, VA: Defense Technical Information Center, octubre de 1989. http://dx.doi.org/10.21236/ada219013.
Texto completoBixby, Robert. Linear-Programming Tools in Integer Programming: The Traveling Salesman. Fort Belvoir, VA: Defense Technical Information Center, octubre de 1992. http://dx.doi.org/10.21236/ada261398.
Texto completoKlotz, Edward S. Dynamic Pricing Criteria in Linear Programming. Fort Belvoir, VA: Defense Technical Information Center, julio de 1988. http://dx.doi.org/10.21236/ada198945.
Texto completoQuinn, J. J., R. L. Johnson y L. A. Durham. Optimized groundwater containment using linear programming. Office of Scientific and Technical Information (OSTI), julio de 1998. http://dx.doi.org/10.2172/656456.
Texto completoGill, Philip E., Walter Murray, Dulce B. Ponceleon y Michael A. Saunders. Primal-Dual Methods for Linear Programming. Fort Belvoir, VA: Defense Technical Information Center, mayo de 1991. http://dx.doi.org/10.21236/ada237418.
Texto completoEbeida, Mohamed, Ahmed Abdelkader, Nina Amenta, Drew Kouri, Ojas Parekh, Cynthia Phillips y Nickolas Winovich. Novel Geometric Operations for Linear Programming. Office of Scientific and Technical Information (OSTI), noviembre de 2020. http://dx.doi.org/10.2172/1813669.
Texto completoGearhart, Jared Lee, Kristin Lynn Adair, Justin David Durfee, Katherine A. Jones, Nathaniel Martin y Richard Joseph Detry. Comparison of open-source linear programming solvers. Office of Scientific and Technical Information (OSTI), octubre de 2013. http://dx.doi.org/10.2172/1104761.
Texto completoMurray, W. y M. A. Saunders. New approaches to linear and nonlinear programming. Office of Scientific and Technical Information (OSTI), marzo de 1990. http://dx.doi.org/10.2172/5254075.
Texto completoTodd, Michael J. y Yinyu Ye. A Centered Projective Algorithm for Linear Programming. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1988. http://dx.doi.org/10.21236/ada192100.
Texto completoDomich, Paul D., Paul T. Boggs, Janet R. Donaldson y Christoph Witzgall. Optimal 3-dimensional methods for linear programming. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4225.
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