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Статті в журналах з теми "Quadratically Constrained Linear Programming"
Klomp, M. "Longitudinal force distribution using quadratically constrained linear programming." Vehicle System Dynamics 49, no. 12 (December 2011): 1823–36. http://dx.doi.org/10.1080/00423114.2010.545131.
Повний текст джерелаHu, Chenyang, Yuelin Gao, Fuping Tian, and Suxia Ma. "A Relaxed and Bound Algorithm Based on Auxiliary Variables for Quadratically Constrained Quadratic Programming Problem." Mathematics 10, no. 2 (January 16, 2022): 270. http://dx.doi.org/10.3390/math10020270.
Повний текст джерелаAlkhalifa, Loay, and Hans Mittelmann. "New Algorithm to Solve Mixed Integer Quadratically Constrained Quadratic Programming Problems Using Piecewise Linear Approximation." Mathematics 10, no. 2 (January 9, 2022): 198. http://dx.doi.org/10.3390/math10020198.
Повний текст джерелаLara, Hugo José, Abel Soares Siqueira, and Jinyun Yuan. "A Reduced Semidefinite Programming Formulation for HA Assignment Problems in Sport Scheduling." TEMA (São Carlos) 19, no. 3 (December 17, 2018): 471. http://dx.doi.org/10.5540/tema.2018.019.03.471.
Повний текст джерелаJain, Pallavi, Gur Saran, and Kamal Srivastava. "A new Integer Linear Programming and Quadratically Constrained Quadratic Programming Formulation for Vertex Bisection Minimization Problem." Journal of Automation, Mobile Robotics & Intelligent Systems 10, no. 1 (February 18, 2016): 69–73. http://dx.doi.org/10.14313/jamris_1-2016/9.
Повний текст джерелаFogarty, Colin B., and Dylan S. Small. "Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies Through Quadratically Constrained Linear Programming." Journal of the American Statistical Association 111, no. 516 (October 1, 2016): 1820–30. http://dx.doi.org/10.1080/01621459.2015.1120675.
Повний текст джерелаMesserer, Florian, Katrin Baumgärtner, and Moritz Diehl. "Survey of sequential convex programming and generalized Gauss-Newton methods." ESAIM: Proceedings and Surveys 71 (August 2021): 64–88. http://dx.doi.org/10.1051/proc/202171107.
Повний текст джерелаPopkov, Alexander S. "Optimal program control in the class of quadratic splines for linear systems." Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes 16, no. 4 (2020): 462–70. http://dx.doi.org/10.21638/11701/spbu10.2020.411.
Повний текст джерелаMaddaloni, Alessandro, Ruben Matino, Ismael Matino, Stefano Dettori, Antonella Zaccara, and Valentina Colla. "A quadratic programming model for the optimization of off-gas networks in integrated steelworks." Matériaux & Techniques 107, no. 5 (2019): 502. http://dx.doi.org/10.1051/mattech/2019025.
Повний текст джерелаXu, Yangyang. "First-Order Methods for Constrained Convex Programming Based on Linearized Augmented Lagrangian Function." INFORMS Journal on Optimization 3, no. 1 (January 2021): 89–117. http://dx.doi.org/10.1287/ijoo.2019.0033.
Повний текст джерелаДисертації з теми "Quadratically Constrained Linear Programming"
P, Van Voorhis Timothy. "The quadratically constrained quadratic program." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/23379.
Повний текст джерелаWang, Guanglei. "Relaxations in mixed-integer quadratically constrained programming and robust programming." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0026/document.
Повний текст джерелаMany real life problems are characterized by making decisions with current information to achieve certain objectives. Mathematical programming has been developed as a successful tool to model and solve a wide range of such problems. However, many seemingly easy problems remain challenging. And some easy problems such as linear programs can be difficult in the face of uncertainty. Motivated by a telecommunication problem where assignment decisions have to be made such that the cloud virtual machines are assigned to servers in a minimum-cost way, we employ several mathematical programming tools to solve the problem efficiently and develop new tools for general theoretical problems. In brief, our work can be summarized as follows. We provide an exact formulation and several reformulations on the cloud virtual machine assignment problem. Then several valid inequalities are used to strengthen the exact formulation, thereby accelerating the solution procedure significantly. In addition, an effective Lagrangian decomposition is proposed. We show that, the bounds providedby the proposed Lagrangian decomposition is strong, both theoretically and numerically. Finally, a symmetry-induced model is proposed which may reduce a large number of bilinear terms in some special cases. Motivated by the virtual machine assignment problem, we also investigate a couple of general methods on the approximation of convex and concave envelopes for bilinear optimization over a hypercube. We establish several theoretical connections between different techniques and prove the equivalence of two seeming different relaxed formulations. An interesting research direction is also discussed. To address issues of uncertainty, a novel paradigm on general linear problems with uncertain parameters are proposed. This paradigm, termed as multipolar robust optimization, generalizes notions of static robustness, affinely adjustable robustness, fully adjustable robustness and fills the gaps in-between. As consequences of this new paradigms, several known results are implied. Further, we prove that the multipolar approach can generate a sequence of upper bounds and a sequence of lower bounds at the same time and both sequences converge to the robust value of fully adjustable robust counterpart under some mild assumptions
Crowe, Mitch. "Nonlinearly constrained optimization via sequential regularized linear programming." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/29648.
Повний текст джерелаZhao, Jianmin. "Optimal Clustering: Genetic Constrained K-Means and Linear Programming Algorithms." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1583.
Повний текст джерелаHardin, Jill Renea. "Resource-constrained scheduling and production planning : linear programming-based studies." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/24857.
Повний текст джерелаWang, Yanhui. "Affine scaling algorithms for linear programs and linearly constrained convex and concave programs." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/24919.
Повний текст джерелаViana, Luiz Alberto do Carmo. "Dependency constrained minimum spanning tree." Universidade Federal do CearÃ, 2016. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=17302.
Повний текст джерелаIntroduzimos o problema de Ãrvore Geradora com DependÃncias MÃnima, AGDM(G,D,w), definido sobre um grafo G(V,E) e um digrafo D(E,A), cujos vÃrtices sÃo as arestas de G e cujos arcos definem dependÃncias entre tais arestas. O problema consiste em encontrar, dentre as Ãrvores geradoras do grafo G(V,E) que satisfaÃam as restriÃÃes de dependÃncia impostas pelo digrafo de entrada D(E,A), uma que tenha custo mÃnimo, segundo a ponderaÃÃo w das arestas de G. As restriÃÃes de dependÃncia exigem que uma aresta e de G sà pode fazer parte de uma soluÃÃo se for uma fonte em D ou se fizer parte da soluÃÃo alguma outra aresta à tal que o arco (e′, e) esteja em D. Provamos que decidir se hà soluÃÃo viÃvel para AGDM(G,D,w) à um problema NP-completo, mesmo quando G à um cacto cordal e D à a uniÃo de arborescÃncias de altura no mÃximo 2. Sua NP-completude tambÃm à mostrada ainda que G seja bipartido, as restriÃÃes de dependÃncia ocorram apenas entre arestas adjacentes de G e formem arborescÃncias de altura no mÃximo 2. Resultados idÃnticos sÃo obtidos para as variantes do problema onde, nas restriÃÃes de dependÃncia, substitui-se o requisito âalgumaâ por âexatamente umaâ ou âtodaâ. Para resolver o problema, apresentamos algumas formulaÃÃes de programaÃÃo inteira e desigualdades vÃlidas. Propomos uma estratÃgia para reduzir a dimensÃo do problema, excluindo arestas de G com base na estrutura de D. Avaliamos os modelos e algoritmos propostos usando instÃncias geradas aleatoriamente. Resultados computacionais sÃo reportados.
We introduce the Dependency Constrained Minimum Spanning Tree Problem, DCMST(G,D,w), defined over a graph G(V,E) and a digraph D(E,A), whose vertices are the edges of G and whose arcs describe dependency relations between these edges. Such problem consists of finding, among the spanning trees of G(V,E) satisfying the dependency constraints imposed by D(E,A), that one whose cost is minimum, according to a edgeweight function w. The dependency constraints impose that an edge e of G can be part of a solution either if it is a source in D or if some other edge e′, such that the arc (e′, e) is in D, is part of it as well. We prove that deciding whether there is a feasible solution to DCMST(G,D,w) is an NP-complete problem, even if G is a chordal cactus and D is a union of arborescences of height at most 2. NP-completeness also applies if G is bipartite, the dependency constraints occur only between adjacent edges of G and their related arcs describe arborescences whose height is at most 2. The same results are obtained for the problem variants which demand that, instead of âsomeâ, âexactly oneâor âallâdependencies be part of a solution. To solve the problem, we introduce some integer programming formulations and some valid inequalities. We propose a strategy to reduce the problem dimension by excluding some edges of G according to the structure of D. We evaluate the introduced models and algorithms using randomly generated instances. Computational results are reported.
Aslan, Murat. "The Cardinality Constrained Multiple Knapsack Problem." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610131/index.pdf.
Повний текст джерелаPedroso, Lucas Garcia. "Programação não linear sem derivadas." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307473.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
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Resumo: Neste trabalho propomos um algoritmo Lagrangiano Aumentado sem derivadas para o problema geral de otimização. Consideramos o método introduzido por Andreani, Birgin, Martínez e Schuverdt, eliminando os cálculos de derivadas inerentes ao algoritmo através de modificações adequadas no critério de parada. Foram mantidos os bons resultados teóricos do método, como convergência sob a condição de qualificação CPLD e a limitação do parâmetro de penalidade. Experimentos numéricos são apresentados, entre os quais destacamos um exemplo de problema sem derivadas baseado na simulação de áreas de figuras no plano.
Abstract: We propose in this work a derivative-free Augmented Lagrangian algorithm for the general problem of optimization. We consider the method due to Andreani, Birgin, Martínez and Schuverdt, eliminating the derivative computations in the algorithm by making suitable modifications on the stopping criterion. The good theoretical results of the method were mantained, as convergence under the CPLD constraint qualification and the limitation of the penalty parameter. Numerical experiments are presented, and the most relevant of them is an example of derivative-free problem based on the simulation of areas of figures on the plane.
Doutorado
Otimização Matematica
Doutor em Matemática Aplicada
Vanden, Berghen Frank. "Constrained, non-linear, derivative-free, parallel optimization of continuous, high computing load, noisy objective functions." Doctoral thesis, Universite Libre de Bruxelles, 2004. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211177.
Повний текст джерелаDoctorat en sciences appliquées
info:eu-repo/semantics/nonPublished
Книги з теми "Quadratically Constrained Linear Programming"
Xiaoqi, Yang, ed. Lagrange-type functions in constrained non-convex optimization. Boston: Kluwer Academic Publishers, 2003.
Знайти повний текст джерелаRyan, Michael J. A Model building approach to horticultural applications of linear programming and constrained games. Hull: University of Hull, Department of Economics, 1997.
Знайти повний текст джерелаYang, Xiao-qi, and Alexander M. Rubinov. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.
Знайти повний текст джерелаRubinov, A., and Xiao-qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization (Applied Optimization). Springer, 2003.
Знайти повний текст джерелаMichel, Bierlaire. Optimization: Principles and Algorithms. EPFL Press, 2015. http://dx.doi.org/10.55430/6116v1mb.
Повний текст джерелаЧастини книг з теми "Quadratically Constrained Linear Programming"
Qualizza, Andrea, Pietro Belotti, and François Margot. "Linear Programming Relaxations of Quadratically Constrained Quadratic Programs." In Mixed Integer Nonlinear Programming, 407–26. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1927-3_14.
Повний текст джерелаShah, Nita H., and Poonam Prakash Mishra. "Constrained Multivariable Optimization." In Non-Linear Programming, 31–50. First edition. | Boca Raton, FL: CRC Press, an imprint of Taylor & Francis Group, LLC, 2021. | Series: Mathematical engineering, manufacturing, and management sciences: CRC Press, 2020. http://dx.doi.org/10.1201/9781003105213-3.
Повний текст джерелаShah, Nita H., and Poonam Prakash Mishra. "Constrained Multivariable Optimization." In Non-Linear Programming, 31–50. First edition. | Boca Raton, FL: CRC Press, an imprint of Taylor & Francis Group, LLC, 2021. | Series: Mathematical engineering, manufacturing, and management sciences: CRC Press, 2020. http://dx.doi.org/10.4324/9781003105213-3.
Повний текст джерелаLuenberger, David G., and Yinyu Ye. "Constrained Minimization Conditions." In Linear and Nonlinear Programming, 321–57. New York, NY: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74503-9_11.
Повний текст джерелаLuenberger, David G., and Yinyu Ye. "Constrained Minimization Conditions." In Linear and Nonlinear Programming, 321–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18842-3_11.
Повний текст джерелаJiang, Rujun, and Duan Li. "Semidefinite Programming Based Convex Relaxation for Nonconvex Quadratically Constrained Quadratic Programming." In Advances in Intelligent Systems and Computing, 213–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21803-4_22.
Повний текст джерелаKu, Wen-Yang, and J. Christopher Beck. "Constraint Programming for Strictly Convex Integer Quadratically-Constrained Problems." In Lecture Notes in Computer Science, 316–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44953-1_21.
Повний текст джерелаAltman, Eitan. "The total cost: Dynamic and Linear Programming." In Constrained Markov Decision Processes, 117–35. Boca Raton: Routledge, 2021. http://dx.doi.org/10.1201/9781315140223-11.
Повний текст джерелаMészáros, Csaba. "The Bpmpd Interior Point Solver for Convex Quadratically Constrained Quadratic Programming Problems." In Large-Scale Scientific Computing, 813–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_97.
Повний текст джерелаMueller, Marianne, and Stefan Kramer. "Integer Linear Programming Models for Constrained Clustering." In Discovery Science, 159–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16184-1_12.
Повний текст джерелаТези доповідей конференцій з теми "Quadratically Constrained Linear Programming"
Olkin, Julia A., and Paul J. Titterton, Jr. "Semidefinite programming for quadratically constrained quadratic programs." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Franklin T. Luk. SPIE, 1995. http://dx.doi.org/10.1117/12.211397.
Повний текст джерелаAmato, Christopher, Daniel S. Bernstein, and Shlomo Zilberstein. "Solving POMDPs using quadratically constrained linear programs." In the fifth international joint conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1160633.1160694.
Повний текст джерелаKhabbazibasmenj, Arash, and Sergiy A. Vorobyov. "Generalized quadratically constrained quadratic programming for signal processing." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855084.
Повний текст джерелаSun, Chuangchuang, and Ran Dai. "Spacecraft Attitude Control under Constrained Zones via Quadratically Constrained Quadratic Programming." In AIAA Guidance, Navigation, and Control Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-2010.
Повний текст джерелаFrison, Gianluca, Jonathan Frey, Florian Messerer, Andrea Zanelli, and Moritz Diehl. "Introducing the quadratically-constrained quadratic programming framework in HPIPM." In 2022 European Control Conference (ECC). IEEE, 2022. http://dx.doi.org/10.23919/ecc55457.2022.9838499.
Повний текст джерелаCheng, Yongfang, Yin Wang, Mario Sznaier, and Octavia Camps. "Subspace Clustering with Priors via Sparse Quadratically Constrained Quadratic Programming." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.562.
Повний текст джерелаSun, Chuangchuang, Ran Dai, and Ping Lu. "Multi-Phase Spacecraft Mission Optimization by Quadratically Constrained Quadratic Programming." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-1667.
Повний текст джерелаYou, Sixiong, and Ran Dai. "Local Optimization of Nonconvex Mixed-Integer Quadratically Constrained Quadratic Programming Problems." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9304368.
Повний текст джерелаFakhry, R., Yasmine Abouelseoud, and Emtethal Negm. "Mixed-integer quadratically constrained programming with application to distribution networks reconfiguration." In 2016 Eighteenth International Middle East Power Systems Conference (MEPCON). IEEE, 2016. http://dx.doi.org/10.1109/mepcon.2016.7836950.
Повний текст джерелаYe, Jieping, Shuiwang Ji, and Jianhui Chen. "Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming." In the 13th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281283.
Повний текст джерелаЗвіти організацій з теми "Quadratically Constrained Linear Programming"
Ye, Y., O. Gueler, R. A. Tapia, and Y. Zhang. A Quadratically Convergent O(square root of nL-Iteration Algorithm for Linear Programming. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada455490.
Повний текст джерелаVAN BLOEMEN WAANDERS, BART G., ROSCOE A. BARTLETT, KEVIN R. LONG, PAUL T. BOGGS, and ANDREW G. SALINGER. Large Scale Non-Linear Programming for PDE Constrained Optimization. Office of Scientific and Technical Information (OSTI), October 2002. http://dx.doi.org/10.2172/805833.
Повний текст джерела