Relevant bibliographies by topics / Convex constrained optimization

Academic literature on the topic 'Convex constrained optimization'

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Published: 14 March 2023

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Journal articles on the topic "Convex constrained optimization"

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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.

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In practical active noise control (ANC) applications, various types of constraints may need to be satisfied, e.g., robust stability, disturbance enhancement, and filter output power constraint. Some adaptive filters such as leaky LMS have been developed to apply required constraints indirectly. However, when multiple constraints are required simultaneously, satisfactory noise performance is difficult to achieve by tuning only one leaky factor. Another filter design approach that may achieve better noise control performance is to solve a constrained optimization problem. But the computational complexity of solving such a constrained optimization problem for ANC applications is usually too high even for offline design. Recently, a convex optimization reformulation is proposed which significantly reduces the required computational effort in solving constrained optimization problems for active noise control applications. In the current work, a constrained adaptive ANC filter design method is proposed. The previously proposed convex formulation is improved so that it can be implemented in real-time. The optimal filter coefficients are then redesigned continuously using online convex optimization when the environment is time-varying.
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Yu, 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.

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A novel sequential convex (SCvx) optimization scheme via the Chebyshev pseudospectral method is proposed for efficiently solving the hypersonic reentry trajectory optimization problem which is highly constrained by heat flux, dynamic pressure, normal load, and multiple no-fly zones. The Chebyshev-Gauss Legend (CGL) node points are used to transcribe the original dynamic constraint into algebraic equality constraint; therefore, a nonlinear programming (NLP) problem is concave and time-consuming to be solved. The iterative linearization and convexification techniques are proposed to convert the concave constraints into convex constraints; therefore, a sequential convex programming problem can be efficiently solved by convex algorithms. Numerical results and a comparison study reveal that the proposed method is efficient and effective to solve the problem of reentry trajectory optimization with multiple constraints.
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Iiduka, 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.

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Owens, 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.

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Cvitanic, 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.

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Donato, 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.

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In this paper a non-convex vector optimization problem among infinite-dimensional spaces is presented. In particular, a generalized Lagrange multiplier rule is formulated as a necessary and sufficient optimality condition for weakly minimal solutions of a constrained vector optimization problem, without requiring that the ordering cone that defines the inequality constraints has non-empty interior. This paper extends the result of Donato (J. Funct. Analysis261 (2011), 2083–2093) to the general setting of vector optimization by introducing a constraint qualification assumption that involves the Fréchet differentiability of the maps and the tangent cone to the image set. Moreover, the constraint qualification is a necessary and sufficient condition for the Lagrange multiplier rule to hold.
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Zhang, 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.

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An off-line optimization approach of high precision minimum time feedrate for CNC machining is proposed. Besides the ordinary considered velocity, acceleration, and jerk constraints, dynamic performance constraint of each servo drive is also considered in this optimization problem to improve the tracking precision along the optimized feedrate trajectory. Tracking error is applied to indicate the servo dynamic performance of each axis. By using variable substitution, the tracking error constrained minimum time trajectory planning problem is formulated as a nonlinear path constrained optimal control problem. Bang-bang constraints structure of the optimal trajectory is proved in this paper; then a novel constraint handling method is proposed to realize a convex optimization based solution of the nonlinear constrained optimal control problem. A simple ellipse feedrate planning test is presented to demonstrate the effectiveness of the approach. Then the practicability and robustness of the trajectory generated by the proposed approach are demonstrated by a butterfly contour machining example.
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Liu, 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.

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Ahmadi, 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.

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We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a Lagrangian framework. Under the assumption that the risk objectives and constraints can be represented by a Markov risk transition mapping, we propose an optimization-based method to synthesize Markovian policies that lower-bound the constrained risk-averse problem. We demonstrate that the formulated optimization problems are in the form of difference convex programs (DCPs) and can be solved by the disciplined convex-concave programming (DCCP) framework. We show that these results generalize linear programs for constrained MDPs with total discounted expected costs and constraints. Finally, we illustrate the effectiveness of the proposed method with numerical experiments on a rover navigation problem involving conditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR) coherent risk measures.
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Kallio, 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.

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Dissertations / Theses on the topic "Convex constrained optimization"

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Shewchun, John Marc 1972. "Constrained control using convex optimization." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/46471.

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Yang, 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.

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Lintereur, 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.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1998.
In 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.
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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.

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Oliveira, 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/.

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In this doctoral thesis, we propose new iterative methods for solving a class of convex optimization problems. In general, we consider problems in which the objective function is composed of a finite sum of convex functions and the set of constraints is, at least, convex and closed. The iterative methods we propose are basically designed through the combination of incremental subgradient methods and string-averaging algorithms. Furthermore, in order to obtain methods able to solve optimization problems with many constraints (and possibly in high dimensions), generally given by convex functions, our analysis includes an operator that calculates approximate projections onto the feasible set, instead of the Euclidean projection. This feature is employed in the two methods we propose; one deterministic and the other stochastic. A convergence analysis is proposed for both methods and numerical experiments are performed in order to verify their applicability, especially in large scale problems.
Nesta 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.
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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.

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This thesis seeks to gain further insight into the connection between stochastic optimal control and forward and backward stochastic differential equations and its applications in solving continuous-time constrained portfolio optimization problems. Three topics are studied in this thesis. In the first part of the thesis, we focus on stochastic maximum principle, which seeks to establish the connection between stochastic optimal control and backward stochastic differential differential equations coupled with static optimality condition on the Hamiltonian. We prove a weak neccessary and sufficient maximum principle for Markovian regime switching stochastic optimal control problems. Instead of insisting on the maxi- mum condition of the Hamiltonian, we show that 0 belongs to the sum of Clarkes generalized gradient of the Hamiltonian and Clarkes normal cone of the control constraint set at the optimal control. Under a joint concavity condition on the Hamiltonian and a convexity condition on the terminal objective function, the necessary condition becomes sufficient. We give four examples to demonstrate the weak stochastic maximum principle. In the second part of the thesis, we study a continuous-time stochastic linear quadratic control problem arising from mathematical finance. We model the asset dynamics with random market coefficients and portfolio strategies with convex constraints. Following the convex duality approach,we show that the necessary and sufficient optimality conditions for both the primal and dual problems can be written in terms of processes satisfying a system of FBSDEs together with other conditions. We characterise explicitly the optimal wealth and portfolio processes as functions of adjoint processes from the dual FBSDEs in a dynamic fashion and vice versa. We apply the results to solve quadratic risk minimization problems with cone-constraints and derive the explicit representations of solutions to the extended stochastic Riccati equations for such problems. In the final section of the thesis, we extend the previous result to utility maximization problems. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of FBSDEs plus additional conditions. Such formulation then allows us to explicitly characterize the primal optimal control as a function of the adjoint processes coming from the dual FBSDEs in a dynamic fashion and vice versa. Moreover, we also find that the optimal primal wealth process coincides with the optimal adjoint process of the dual problem and vice versa. Finally we solve three constrained utility maximization problems and contrasts the simplicity of the duality approach we propose with the technical complexity in solving the primal problem directly.
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Kůdela, 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.

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Při práci s úlohami stochastického programování se často setkáváme s optimalizačními problémy, které jsou příliš rozsáhlé na to, aby byly zpracovány pomocí rutinních metod matematického programování. Nicméně, v některých případech mají tyto problémy vhodnou strukturu, umožňující použití specializovaných dekompozičních metod, které lze použít při řešení rozsáhlých optimalizačních problémů. Tato práce se zabývá dvěma třídami úloh stochastického programování, které mají speciální strukturu, a to dvoustupňovými stochastickými úlohami a úlohami s pravděpodobnostním omezením, a pokročilými dekompozičními metodami, které lze použít k řešení problému v těchto dvou třídách. V práci popisujeme novou metodu pro tvorbu “warm-start” řezů pro metodu zvanou “Generalized Benders Decomposition”, která se používá při řešení dvoustupňových stochastických problémů. Pro třídu úloh s pravděpodobnostním omezením zde uvádíme originální dekompoziční metodu, kterou jsme nazvali “Pool & Discard algoritmus”. Užitečnost popsaných dekompozičních metod je ukázána na několika příkladech a inženýrských aplikacích.
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Günther, Christian [Verfasser]. "On generalized-convex constrained multi-objective optimization and application in location theory / Christian Günther." Halle, 2018. http://d-nb.info/1175950602/34.

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Wang, Guanglei. "Relaxations in mixed-integer quadratically constrained programming and robust programming." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0026/document.

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De nombreux problèmes de la vie réelle sont exprimés sous la forme de décisions à prendre à l’aide de l’information accessible dans le but d’atteindre certains objectifs. La programmation numérique a prouvé être un outil efficace pour modéliser et résoudre une grande variété de problèmes de ce type. Cependant, de nombreux problèmes en apparence faciles sont encore durs à résoudre. Et même des problèmes faciles de programmation linéaire deviennent durs avec l’incertitude de l’information disponible. Motivés par un problème de télécommunication où l’on doit associer des machines virtuelles à des serveurs tout en minimisant les coûts, nous avons employé plusieurs outils de programmation mathématique dans le but de résoudre efficacement le problème, et développé de nouveaux outils pour des problèmes plus généraux. Dans l’ensemble, résumons les principaux résultats de cette thèse comme suit. Une formulation exacte et plusieurs reformulations pour le problème d’affectation de machines virtuelles dans le cloud sont données. Nous utilisons plusieurs inégalités valides pour renforcer la formulation exacte, accélérant ainsi l’algorithme de résolution de manière significative. Nous donnons en outre un résultat géométrique sur la qualité de la borne lagrangienne montrant qu’elle est généralement beaucoup plus forte que la borne de la relaxation continue. Une hiérarchie de relaxation est également proposée en considérant une séquence de couverture de l’ensemble de la demande. Ensuite, nous introduisons une nouvelle formulation induite par les symétries du problème. Cette formulation permet de réduire considérablement le nombre de termes bilinéaires dans le modèle, et comme prévu, semble plus efficace que les modèles précédents. Deux approches sont développées pour la construction d’enveloppes convexes et concaves pour l’optimisation bilinéaire sur un hypercube. Nous établissons plusieurs connexions théoriques entre différentes techniques et nous discutons d’autres extensions possibles. Nous montrons que deux variantes de formulations pour approcher l’enveloppe convexe des fonctions bilinéaires sont équivalentes. Nous introduisons un nouveau paradigme sur les problèmes linéaires généraux avec des paramètres incertains. Nous proposons une hiérarchie convergente de problèmes d’optimisation robuste – approche robuste multipolaire, qui généralise les notions de robustesse statique, de robustesse d’affinement ajustable, et de robustesse entièrement ajustable. En outre, nous montrons que l’approche multipolaire peut générer une séquence de bornes supérieures et une séquence de bornes inférieures en même temps et les deux séquences convergent vers la valeur robuste des FARC sous certaines hypothèses modérées
Many 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
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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.

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Books on the topic "Convex constrained optimization"

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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.

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Xiaoqi, Yang, ed. Lagrange-type functions in constrained non-convex optimization. Boston: Kluwer Academic Publishers, 2003.

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Strongin, 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.

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Rubinov, A., and Xiao-qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization (Applied Optimization). Springer, 2003.

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Yang, Xiao-qi, and Alexander M. Rubinov. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.

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Rubinov, Alexander M., and Xiao-Qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.

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Yang, Xiao-qi Xiao-qi, and Alexander M. Rubinov. Lagrange-Type Functions in Constrained Non-Convex Optimization. Springer London, Limited, 2013.

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Sergeyev, Yaroslav D., and Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2014.

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Sergeyev, Yaroslav D., and Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2013.

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Sergeyev, Yaroslav D., and Roman G. Strongin. Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Springer, 2013.

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Book chapters on the topic "Convex constrained optimization"

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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.

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Lin, 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.

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Stefanov, 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.

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Kiwiel, 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.

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Mai, 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.

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Kiwiel, 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.

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Uderzo, 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.

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Tran-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.

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Rubinov, 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.

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Rubinov, 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.

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Conference papers on the topic "Convex constrained optimization"

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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.

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Doan, 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.

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Nock, 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.

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Vaze, 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.

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Yu, 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.

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Cao, 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.

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Kim, 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.

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Li, 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.

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Zarghamy, 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.

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Nutalapati, 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.

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Reports on the topic "Convex constrained optimization"

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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.

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Chen, 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.

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Scholnik, 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.

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