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Статті в журналах з теми "Convex constrained optimization"
Zhuang, Yongjie, and Yangfan Liu. "A constrained adaptive active noise control filter design method via online convex optimization." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A98. http://dx.doi.org/10.1121/10.0015669.
Повний текст джерелаYu, Chun-Mei, Dang-Jun Zhao, and Ye Yang. "Efficient Convex Optimization of Reentry Trajectory via the Chebyshev Pseudospectral Method." International Journal of Aerospace Engineering 2019 (May 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/1414279.
Повний текст джерелаIiduka, Hideaki. "Decentralized hierarchical constrained convex optimization." Optimization and Engineering 21, no. 1 (June 1, 2019): 181–213. http://dx.doi.org/10.1007/s11081-019-09440-7.
Повний текст джерелаOwens, R. W., and V. P. Sreedharan. "An algorithm for constrained convex optimization." Numerical Functional Analysis and Optimization 8, no. 1-2 (January 1985): 137–52. http://dx.doi.org/10.1080/01630568508816207.
Повний текст джерелаCvitanic, Jaksa, and Ioannis Karatzas. "Convex Duality in Constrained Portfolio Optimization." Annals of Applied Probability 2, no. 4 (November 1992): 767–818. http://dx.doi.org/10.1214/aoap/1177005576.
Повний текст джерелаDonato, Maria Bernadette. "Generalized Lagrange multiplier rule for non-convex vector optimization problems." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 146, no. 2 (March 3, 2016): 297–308. http://dx.doi.org/10.1017/s0308210515000463.
Повний текст джерелаZhang, Qiang, Shurong Li, and 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.
Повний текст джерелаLiu, An, Vincent K. N. Lau, and Borna Kananian. "Stochastic Successive Convex Approximation for Non-Convex Constrained Stochastic Optimization." IEEE Transactions on Signal Processing 67, no. 16 (August 15, 2019): 4189–203. http://dx.doi.org/10.1109/tsp.2019.2925601.
Повний текст джерелаAhmadi, Mohamadreza, Ugo Rosolia, Michel D. Ingham, Richard M. Murray, and Aaron D. Ames. "Constrained Risk-Averse Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11718–25. http://dx.doi.org/10.1609/aaai.v35i13.17393.
Повний текст джерелаKallio, Markku, and Seppo Salo. "Tatonnement Procedures for Linearly Constrained Convex Optimization." Management Science 40, no. 6 (June 1994): 788–97. http://dx.doi.org/10.1287/mnsc.40.6.788.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаYang, 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.
Повний текст джерелаLintereur, 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.
Повний текст джерелаIn 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.
Повний текст джерелаOliveira, 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/.
Повний текст джерелаNesta 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.
Повний текст джерелаKůdela, 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.
Повний текст джерелаGünther, Christian [Verfasser]. "On generalized-convex constrained multi-objective optimization and application in location theory / Christian Günther." Halle, 2018. http://d-nb.info/1175950602/34.
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
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.
Повний текст джерелаКниги з теми "Convex constrained optimization"
Rubinov, Alexander, and 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.
Повний текст джерелаXiaoqi, Yang, ed. Lagrange-type functions in constrained non-convex optimization. Boston: Kluwer Academic Publishers, 2003.
Знайти повний текст джерелаStrongin, Roman G., and 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.
Повний текст джерелаRubinov, A., and Xiao-qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization (Applied Optimization). Springer, 2003.
Знайти повний текст джерелаYang, Xiao-qi, and Alexander M. Rubinov. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.
Знайти повний текст джерелаRubinov, Alexander M., and Xiao-Qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.
Знайти повний текст джерелаYang, Xiao-qi Xiao-qi, and Alexander M. Rubinov. Lagrange-Type Functions in Constrained Non-Convex Optimization. Springer London, Limited, 2013.
Знайти повний текст джерелаSergeyev, Yaroslav D., and Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2014.
Знайти повний текст джерелаSergeyev, Yaroslav D., and Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2013.
Знайти повний текст джерелаSergeyev, Yaroslav D., and Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2013.
Знайти повний текст джерелаЧастини книг з теми "Convex constrained optimization"
Pshenichnyj, Boris N. "Convex and Quadratic Programming." In 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.
Повний текст джерелаLin, Zhouchen, Huan Li, and Cong Fang. "Accelerated Algorithms for Constrained Convex Optimization." In Accelerated Optimization for Machine Learning, 57–108. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2910-8_3.
Повний текст джерелаStefanov, Stefan M. "Relaxation of the Equality Constrained Convex Continuous Knapsack Problem." In Separable Optimization, 281–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78401-0_15.
Повний текст джерелаKiwiel, K. C. "Descent Methods for Nonsmooth Convex Constrained Minimization." In 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.
Повний текст джерелаMai, Van Sy, Dipankar Maity, Bhaskar Ramasubramanian, and Michael C. Rotkowitz. "Convex Methods for Rank-Constrained Optimization Problems." In 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.
Повний текст джерелаKiwiel, Krzysztof C. "Feasible point methods for convex constrained minimization problems." In Methods of Descent for Nondifferentiable Optimization, 190–228. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0074505.
Повний текст джерелаUderzo, Amos. "Convex Approximators, Convexificators and Exhausters: Applications to Constrained Extremum Problems." In Nonconvex Optimization and Its Applications, 297–327. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3137-8_12.
Повний текст джерелаTran-Dinh, Quoc, and Volkan Cevher. "Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization." In Large-Scale and Distributed Optimization, 57–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97478-1_4.
Повний текст джерелаRubinov, Alexander, and Xiaoqi Yang. "Introduction." In 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.
Повний текст джерелаRubinov, Alexander, and Xiaoqi Yang. "Abstract Convexity." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Convex constrained optimization"
Santamaria, I., J. Via, M. Kirby, T. Marrinan, C. Peterson, and L. Scharf. "Constrained subspace estimation via convex optimization." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081398.
Повний текст джерелаDoan, Thinh Thanh, and Choon Yik Tang. "Continuous-time constrained distributed convex optimization." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483394.
Повний текст джерелаNock, R., and F. Nielsen. "Improving clustering algorithms through constrained convex optimization." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333833.
Повний текст джерелаVaze, Rahul. "On Dynamic Regret and Constraint Violations in Constrained Online Convex Optimization." In 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.
Повний текст джерелаYu, Hao, and Vincent K. N. Lau. "Rank Constrained Schur-Convex Optimization with Multiple Trace/Log-Det Constraints." In GLOBECOM 2010 - 2010 IEEE Global Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/glocom.2010.5684357.
Повний текст джерелаCao, Xuanyu, Junshan Zhang, and H. Vincent Poor. "Impact of Delays on Constrained Online Convex Optimization." In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048958.
Повний текст джерелаKim, Dong Sik. "Quantization constrained convex optimization for the compressive sensing reconstructions." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495809.
Повний текст джерелаLi, Xiuxian, Lihua Xie, and Yiguang Hong. "Distributed Continuous-Time Constrained Convex Optimization via Nonsmooth Analysis." In 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2018. http://dx.doi.org/10.1109/rcar.2018.8621707.
Повний текст джерелаZarghamy, Michael, Alejandro Ribeiroy, and Ali Jadbabaiey. "Accelerated dual descent for constrained convex network flow optimization." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760019.
Повний текст джерелаNutalapati, Mohan Krishna, Muppavaram Sai Krishna, Atanu Samanta, and Ketan Rajawat. "Constrained Non-convex Optimization via Stochastic Variance Reduced Approximations." In 2019 Sixth Indian Control Conference (ICC). IEEE, 2019. http://dx.doi.org/10.1109/icc47138.2019.9123192.
Повний текст джерелаЗвіти організацій з теми "Convex constrained optimization"
Lawrence, Nathan. Convex and Nonconvex Optimization Techniques for the Constrained Fermat-Torricelli Problem. Portland State University Library, January 2016. http://dx.doi.org/10.15760/honors.319.
Повний текст джерелаChen, Yunmei, Guanghui Lan, Yuyuan Ouyang, and Wei Zhang. Fast Bundle-Level Type Methods for Unconstrained and Ball-Constrained Convex Optimization. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada612792.
Повний текст джерелаScholnik, Dan P., and Jeffrey O. Coleman. Second-Order Cone Formulations of Mixed-Norm Error Constraints for FIR Filter Optimization. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada523252.
Повний текст джерела