Literatura académica sobre el tema "Convex constrained optimization"
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Artículos de revistas sobre el tema "Convex constrained optimization"
Zhuang, Yongjie y Yangfan Liu. "A constrained adaptive active noise control filter design method via online convex optimization". Journal of the Acoustical Society of America 152, n.º 4 (octubre de 2022): A98. http://dx.doi.org/10.1121/10.0015669.
Texto completoYu, Chun-Mei, Dang-Jun Zhao y Ye Yang. "Efficient Convex Optimization of Reentry Trajectory via the Chebyshev Pseudospectral Method". International Journal of Aerospace Engineering 2019 (2 de mayo de 2019): 1–9. http://dx.doi.org/10.1155/2019/1414279.
Texto completoIiduka, Hideaki. "Decentralized hierarchical constrained convex optimization". Optimization and Engineering 21, n.º 1 (1 de junio de 2019): 181–213. http://dx.doi.org/10.1007/s11081-019-09440-7.
Texto completoOwens, R. W. y V. P. Sreedharan. "An algorithm for constrained convex optimization". Numerical Functional Analysis and Optimization 8, n.º 1-2 (enero de 1985): 137–52. http://dx.doi.org/10.1080/01630568508816207.
Texto completoCvitanic, Jaksa y Ioannis Karatzas. "Convex Duality in Constrained Portfolio Optimization". Annals of Applied Probability 2, n.º 4 (noviembre de 1992): 767–818. http://dx.doi.org/10.1214/aoap/1177005576.
Texto completoDonato, Maria Bernadette. "Generalized Lagrange multiplier rule for non-convex vector optimization problems". Proceedings of the Royal Society of Edinburgh: Section A Mathematics 146, n.º 2 (3 de marzo de 2016): 297–308. http://dx.doi.org/10.1017/s0308210515000463.
Texto completoZhang, Qiang, Shurong Li y Jianxin Guo. "Minimum Time Trajectory Optimization of CNC Machining with Tracking Error Constraints". Abstract and Applied Analysis 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/835098.
Texto completoLiu, An, Vincent K. N. Lau y Borna Kananian. "Stochastic Successive Convex Approximation for Non-Convex Constrained Stochastic Optimization". IEEE Transactions on Signal Processing 67, n.º 16 (15 de agosto de 2019): 4189–203. http://dx.doi.org/10.1109/tsp.2019.2925601.
Texto completoAhmadi, Mohamadreza, Ugo Rosolia, Michel D. Ingham, Richard M. Murray y Aaron D. Ames. "Constrained Risk-Averse Markov Decision Processes". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 11718–25. http://dx.doi.org/10.1609/aaai.v35i13.17393.
Texto completoKallio, Markku y Seppo Salo. "Tatonnement Procedures for Linearly Constrained Convex Optimization". Management Science 40, n.º 6 (junio de 1994): 788–97. http://dx.doi.org/10.1287/mnsc.40.6.788.
Texto completoTesis sobre el tema "Convex constrained optimization"
Shewchun, John Marc 1972. "Constrained control using convex optimization". Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/46471.
Texto completoYang, Yi. "Sequential convex approximations of chance constrained programming /". View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?IELM%202008%20YANG.
Texto completoLintereur, Beau V. (Beau Vincent) 1973. "Constrained H̳₂ design via convex optimization with applications". Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50628.
Texto completoIn title on t.p., double-underscored "H" appears in script.
Includes bibliographical references (p. 133-138).
A convex optimization controller design method is presented which minimizes the closed-loop H2 norm, subject to constraints on the magnitude of closed-loop transfer functions and transient responses due to specified inputs. This method uses direct parameter optimization of the closed-loop Youla or Q-parameter where the variables are the coefficients of a stable orthogonal basis. The basis is constructed using the recently rediscovered Generalized Orthonormal Basis Functions (GOBF) that have found application in system identification. It is proposed that many typical control specifications including robustness to modeling error and gain and phase margins can be posed with two simple constraints in the frequency and time domain. With some approximation, this formulation allows the controller design problem to be cast as a quadratic program. Two example applications demonstrate the practical utility of this method for real systems. First this method is applied to the roll axis of the EOS-AM1 spacecraft attitude control system, with a set of performance and robustness specifications. The constrained H2 controller simultaneously meets the specifications where previous model-based control studies failed. Then a constrained H2 controller is designed for an active vibration isolation system for a spaceborne optical technology demonstration test stand. Mixed specifications are successfully incorporated into the design and the results are verified with experimental frequency data.
by Beau V. Lintereur.
S.M.
Roese-Koerner, Lutz [Verfasser]. "Convex Optimization for Inequality Constrained Adjustment Problems / Lutz Roese-Koerner". Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1078728534/34.
Texto completoOliveira, Rafael Massambone de. "String-averaging incremental subgradient methods for constrained convex optimization problems". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14112017-150512/.
Texto completoNesta tese de doutorado, propomos novos métodos iterativos para a solução de uma classe de problemas de otimização convexa. Em geral, consideramos problemas nos quais a função objetivo é composta por uma soma finita de funções convexas e o conjunto de restrições é, pelo menos, convexo e fechado. Os métodos iterativos que propomos são criados, basicamente, através da junção de métodos de subgradientes incrementais e do algoritmo de média das sequências. Além disso, visando obter métodos flexíveis para soluções de problemas de otimização com muitas restrições (e possivelmente em altas dimensões), dadas em geral por funções convexas, a nossa análise inclui um operador que calcula projeções aproximadas sobre o conjunto viável, no lugar da projeção Euclideana. Essa característica é empregada nos dois métodos que propomos; um determinístico e o outro estocástico. Uma análise de convergência é proposta para ambos os métodos e experimentos numéricos são realizados a fim de verificar a sua aplicabilidade, principalmente em problemas de grande escala.
Li, Yusong. "Stochastic maximum principle and dynamic convex duality in continuous-time constrained portfolio optimization". Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45536.
Texto completoKůdela, Jakub. "Advanced Decomposition Methods in Stochastic Convex Optimization". Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-403864.
Texto completoGünther, Christian [Verfasser]. "On generalized-convex constrained multi-objective optimization and application in location theory / Christian Günther". Halle, 2018. http://d-nb.info/1175950602/34.
Texto completoWang, Guanglei. "Relaxations in mixed-integer quadratically constrained programming and robust programming". Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0026/document.
Texto completoMany 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
Blomqvist, Anders. "A convex optimization approach to complexity constrained analytic interpolation with applications to ARMA estimation and robust control". Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-117.
Texto completoLibros sobre el tema "Convex constrained optimization"
Rubinov, Alexander y Xiaoqi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9172-0.
Texto completoXiaoqi, Yang, ed. Lagrange-type functions in constrained non-convex optimization. Boston: Kluwer Academic Publishers, 2003.
Buscar texto completoStrongin, Roman G. y Yaroslav D. Sergeyev. Global Optimization with Non-Convex Constraints. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4677-1.
Texto completoRubinov, A. y Xiao-qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization (Applied Optimization). Springer, 2003.
Buscar texto completoYang, Xiao-qi y Alexander M. Rubinov. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.
Buscar texto completoRubinov, Alexander M. y Xiao-Qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.
Buscar texto completoYang, Xiao-qi Xiao-qi y Alexander M. Rubinov. Lagrange-Type Functions in Constrained Non-Convex Optimization. Springer London, Limited, 2013.
Buscar texto completoSergeyev, Yaroslav D. y Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2014.
Buscar texto completoSergeyev, Yaroslav D. y Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2013.
Buscar texto completoSergeyev, Yaroslav D. y Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2013.
Buscar texto completoCapítulos de libros sobre el tema "Convex constrained optimization"
Pshenichnyj, Boris N. "Convex and Quadratic Programming". En The Linearization Method for Constrained Optimization, 1–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-57918-9_1.
Texto completoLin, Zhouchen, Huan Li y Cong Fang. "Accelerated Algorithms for Constrained Convex Optimization". En Accelerated Optimization for Machine Learning, 57–108. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2910-8_3.
Texto completoStefanov, Stefan M. "Relaxation of the Equality Constrained Convex Continuous Knapsack Problem". En Separable Optimization, 281–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78401-0_15.
Texto completoKiwiel, K. C. "Descent Methods for Nonsmooth Convex Constrained Minimization". En Nondifferentiable Optimization: Motivations and Applications, 203–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-662-12603-5_19.
Texto completoMai, Van Sy, Dipankar Maity, Bhaskar Ramasubramanian y Michael C. Rotkowitz. "Convex Methods for Rank-Constrained Optimization Problems". En 2015 Proceedings of the Conference on Control and its Applications, 123–30. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974072.18.
Texto completoKiwiel, Krzysztof C. "Feasible point methods for convex constrained minimization problems". En Methods of Descent for Nondifferentiable Optimization, 190–228. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0074505.
Texto completoUderzo, Amos. "Convex Approximators, Convexificators and Exhausters: Applications to Constrained Extremum Problems". En Nonconvex Optimization and Its Applications, 297–327. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3137-8_12.
Texto completoTran-Dinh, Quoc y Volkan Cevher. "Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization". En Large-Scale and Distributed Optimization, 57–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97478-1_4.
Texto completoRubinov, Alexander y Xiaoqi Yang. "Introduction". En Lagrange-type Functions in Constrained Non-Convex Optimization, 1–14. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9172-0_1.
Texto completoRubinov, Alexander y Xiaoqi Yang. "Abstract Convexity". En Lagrange-type Functions in Constrained Non-Convex Optimization, 15–48. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9172-0_2.
Texto completoActas de conferencias sobre el tema "Convex constrained optimization"
Santamaria, I., J. Via, M. Kirby, T. Marrinan, C. Peterson y L. Scharf. "Constrained subspace estimation via convex optimization". En 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081398.
Texto completoDoan, Thinh Thanh y Choon Yik Tang. "Continuous-time constrained distributed convex optimization". En 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483394.
Texto completoNock, R. y F. Nielsen. "Improving clustering algorithms through constrained convex optimization". En Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333833.
Texto completoVaze, Rahul. "On Dynamic Regret and Constraint Violations in Constrained Online Convex Optimization". En 2022 20th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt). IEEE, 2022. http://dx.doi.org/10.23919/wiopt56218.2022.9930613.
Texto completoYu, Hao y Vincent K. N. Lau. "Rank Constrained Schur-Convex Optimization with Multiple Trace/Log-Det Constraints". En GLOBECOM 2010 - 2010 IEEE Global Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/glocom.2010.5684357.
Texto completoCao, Xuanyu, Junshan Zhang y H. Vincent Poor. "Impact of Delays on Constrained Online Convex Optimization". En 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048958.
Texto completoKim, Dong Sik. "Quantization constrained convex optimization for the compressive sensing reconstructions". En 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495809.
Texto completoLi, Xiuxian, Lihua Xie y Yiguang Hong. "Distributed Continuous-Time Constrained Convex Optimization via Nonsmooth Analysis". En 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2018. http://dx.doi.org/10.1109/rcar.2018.8621707.
Texto completoZarghamy, Michael, Alejandro Ribeiroy y Ali Jadbabaiey. "Accelerated dual descent for constrained convex network flow optimization". En 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760019.
Texto completoNutalapati, Mohan Krishna, Muppavaram Sai Krishna, Atanu Samanta y Ketan Rajawat. "Constrained Non-convex Optimization via Stochastic Variance Reduced Approximations". En 2019 Sixth Indian Control Conference (ICC). IEEE, 2019. http://dx.doi.org/10.1109/icc47138.2019.9123192.
Texto completoInformes sobre el tema "Convex constrained optimization"
Lawrence, Nathan. Convex and Nonconvex Optimization Techniques for the Constrained Fermat-Torricelli Problem. Portland State University Library, enero de 2016. http://dx.doi.org/10.15760/honors.319.
Texto completoChen, Yunmei, Guanghui Lan, Yuyuan Ouyang y Wei Zhang. Fast Bundle-Level Type Methods for Unconstrained and Ball-Constrained Convex Optimization. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2014. http://dx.doi.org/10.21236/ada612792.
Texto completoScholnik, Dan P. y Jeffrey O. Coleman. Second-Order Cone Formulations of Mixed-Norm Error Constraints for FIR Filter Optimization. Fort Belvoir, VA: Defense Technical Information Center, junio de 2010. http://dx.doi.org/10.21236/ada523252.
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