Gotowe bibliografie tematyczne / Convex optimization

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  2. Rozprawy doktorskie
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Gotowa bibliografia na temat „Convex optimization”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Convex optimization"

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Luethi, Hans-Jakob. "Convex Optimization." Journal of the American Statistical Association 100, no. 471 (2005): 1097. http://dx.doi.org/10.1198/jasa.2005.s41.

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Ceria, Sebastián, and João Soares. "Convex programming for disjunctive convex optimization." Mathematical Programming 86, no. 3 (1999): 595–614. http://dx.doi.org/10.1007/s101070050106.

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Lasserre, Jean B. "On convex optimization without convex representation." Optimization Letters 5, no. 4 (2011): 549–56. http://dx.doi.org/10.1007/s11590-011-0323-1.

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Ben-Tal, A., and A. Nemirovski. "Robust Convex Optimization." Mathematics of Operations Research 23, no. 4 (1998): 769–805. http://dx.doi.org/10.1287/moor.23.4.769.

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Tilahun, Surafel Luleseged. "Convex Grey Optimization." RAIRO - Operations Research 53, no. 1 (2019): 339–49. http://dx.doi.org/10.1051/ro/2018088.

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Many optimization problems are formulated from a real scenario involving incomplete information due to uncertainty in reality. The uncertainties can be expressed with appropriate probability distributions or fuzzy numbers with a membership function, if enough information can be accessed for the construction of either the probability density function or the membership of the fuzzy numbers. However, in some cases there may not be enough information for that and grey numbers need to be used. A grey number is an interval number to represent the value of a quantity. Its exact value or the likelihoo
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Ubhaya, Vasant A. "Quasi-convex optimization." Journal of Mathematical Analysis and Applications 116, no. 2 (1986): 439–49. http://dx.doi.org/10.1016/s0022-247x(86)80008-7.

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Onn, Shmuel. "Convex Matroid Optimization." SIAM Journal on Discrete Mathematics 17, no. 2 (2003): 249–53. http://dx.doi.org/10.1137/s0895480102408559.

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Pardalos, Panos M. "Convex optimization theory." Optimization Methods and Software 25, no. 3 (2010): 487. http://dx.doi.org/10.1080/10556781003625177.

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Onn, Shmuel, and Uriel G. Rothblum. "Convex Combinatorial Optimization." Discrete & Computational Geometry 32, no. 4 (2004): 549–66. http://dx.doi.org/10.1007/s00454-004-1138-y.

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Mayeli, Azita. "Non-convex Optimization via Strongly Convex Majorization-minimization." Canadian Mathematical Bulletin 63, no. 4 (2019): 726–37. http://dx.doi.org/10.4153/s0008439519000730.

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AbstractIn this paper, we introduce a class of nonsmooth nonconvex optimization problems, and we propose to use a local iterative minimization-majorization (MM) algorithm to find an optimal solution for the optimization problem. The cost functions in our optimization problems are an extension of convex functions with MC separable penalty, which were previously introduced by Ivan Selesnick. These functions are not convex; therefore, convex optimization methods cannot be applied here to prove the existence of optimal minimum point for these functions. For our purpose, we use convex analysis tool
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Rozprawy doktorskie na temat "Convex optimization"

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Joulin, Armand. "Convex optimization for cosegmentation." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00826236.

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La simplicité apparente avec laquelle un humain perçoit ce qui l'entoure suggère que le processus impliqué est en partie mécanique, donc ne nécessite pas un haut degré de réflexion. Cette observation suggère que notre perception visuelle du monde peut être simulée sur un ordinateur. La vision par ordinateur est le domaine de recherche consacré au problème de la création d'une forme de perception visuelle pour des ordinateurs. La puissance de calcul des ordinateurs des années 50 ne permettait pas de traiter et d'analyser les données visuelles nécessaires à l'élaboration d'une perception visuell
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Rätsch, Gunnar. "Robust boosting via convex optimization." Phd thesis, Universität Potsdam, 2001. http://opus.kobv.de/ubp/volltexte/2005/39/.

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In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, bei dem die resultierende Klassifikations- oder Regressionsregel aus einfachen Regeln - den Basishypothesen - zusammengesetzt ist. Die sogenannten Boosting Algorithmen erzeugen iterativ eine gewichtete Summe von Basishypothesen, die gut auf ungesehenen Mustern vorhersagen. <br /> Die Arbeit behandelt
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Nekooie, Batool. "Convex optimization involving matrix inequalities." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/13880.

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Jangam, Ravindra nath vijay kumar. "BEAMFORMING TECHNIQUES USING CONVEX OPTIMIZATION." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-33934.

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The thesis analyses and validates Beamforming methods using Convex Optimization.  CVX which is a Matlab supported tool for convex optimization has been used to develop this concept. An algorithm is designed by which an appropriate system has been identified by varying parameters such as number of antennas, passband width, and stopbands widths of a beamformer. We have observed the beamformer by minimizing the error for Least-square and Infinity norms. A graph obtained by the optimum values between least-square and infinity norms shows us a trade-off between these two norms. We have observed con
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Saunderson, James (James Francis). "Subspace identification via convex optimization." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66475.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 88-92).<br>In this thesis we consider convex optimization-based approaches to the classical problem of identifying a subspace from noisy measurements of a random process taking values in the subspace. We focus on the case where the measurement noise is component-wise independent, known as the factor analysis model in statistics. We develop a new analysis of an existing convex optimization-based heur
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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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Boţ, Radu Ioan. "Conjugate duality in convex optimization." Berlin [u.a.] Springer, 2010. http://dx.doi.org/10.1007/978-3-642-04900-2.

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Aggarwal, Varun. "Analog circuit optimization using evolutionary algorithms and convex optimization." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40525.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Includes bibliographical references (p. 83-88).<br>In this thesis, we analyze state-of-art techniques for analog circuit sizing and compare them on various metrics. We ascertain that a methodology which improves the accuracy of sizing without increasing the run time or the designer effort is a contribution. We argue that the accuracy of geometric programming can be improved without adversely influencing the run time or increasing the designer's effort. This is facilitated by dec
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van, den Berg Ewout. "Convex optimization for generalized sparse recovery." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/16646.

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The past decade has witnessed the emergence of compressed sensing as a way of acquiring sparsely representable signals in a compressed form. These developments have greatly motivated research in sparse signal recovery, which lies at the heart of compressed sensing, and which has recently found its use in altogether new applications. In the first part of this thesis we study the theoretical aspects of joint-sparse recovery by means of sum-of-norms minimization, and the ReMBo-l₁ algorithm, which combines boosting techniques with l₁-minimization. For the sum-of-norms approach we derive necessary
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Lin, Chin-Yee. "Interior point methods for convex optimization." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/15044.

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Książki na temat "Convex optimization"

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Lieven, Vandenberghe, ed. Convex optimization. Cambridge University Press, 2006.

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Pennanen, Teemu, and Ari-Pekka Perkkiö. Convex Stochastic Optimization. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76432-5.

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Brinkhuis, Jan. Convex Analysis for Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41804-5.

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Nesterov, Yurii. Lectures on Convex Optimization. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91578-4.

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Bonnans, J. Frédéric. Convex and Stochastic Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14977-2.

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Zaslavski, Alexander J. Convex Optimization with Computational Errors. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37822-6.

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Pardalos, Panos M., Antanas Žilinskas, and Julius Žilinskas. Non-Convex Multi-Objective Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61007-8.

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Borwein, Jonathan M., and Adrian S. Lewis. Convex Analysis and Nonlinear Optimization. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4757-9859-3.

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Li, Li. Selected Applications of Convex Optimization. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46356-7.

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Peypouquet, Juan. Convex Optimization in Normed Spaces. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13710-0.

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Części książek na temat "Convex optimization"

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Stefanov, Stefan M. "Preliminaries: Convex Analysis and Convex Programming." In Applied Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3417-1_1.

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Stefanov, Stefan M. "Preliminaries: Convex Analysis and Convex Programming." In Separable Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78401-0_1.

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Nesterov, Yurii. "Convex Optimization." In Encyclopedia of Operations Research and Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1171.

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Allgöwer, Frank, Jan Hasenauer, and Steffen Waldherr. "Convex Optimization." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1449.

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Hult, Henrik, Filip Lindskog, Ola Hammarlid, and Carl Johan Rehn. "Convex Optimization." In Risk and Portfolio Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4103-8_2.

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Zaslavski, Alexander J. "Convex Optimization." In SpringerBriefs in Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12644-4_2.

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Borkar, Vivek S., and K. S. Mallikarjuna Rao. "Convex Optimization." In Texts and Readings in Mathematics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1652-8_5.

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Wheeler, Jeffrey Paul. "Convex Optimization." In An Introduction to Optimization with Applications in Machine Learning and Data Analytics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780367425517-19.

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Royset, Johannes O., and Roger J.-B. Wets. "CONVEX OPTIMIZATION." In An Optimization Primer. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76275-9_2.

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Çınlar, Erhan, and Robert J. Vanderbei. "Convex Optimization." In Undergraduate Texts in Mathematics. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5257-7_6.

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Streszczenia konferencji na temat "Convex optimization"

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Khatana, Vivek, and Murti V. Salapaka. "Distributed Difference of Convex Optimization." In 2024 60th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2024. http://dx.doi.org/10.1109/allerton63246.2024.10735298.

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Nayak, Tanvi S., and B. N. Bharath. "Improved Bounds For Online Convex Optimization." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887754.

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Boyd, Stephen. "Convex optimization." In the 17th ACM SIGKDD international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020410.

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Szu, Harold H. "Non-Convex Optimization." In 30th Annual Technical Symposium, edited by William J. Miceli. SPIE, 1986. http://dx.doi.org/10.1117/12.976247.

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Udell, Madeleine, Karanveer Mohan, David Zeng, Jenny Hong, Steven Diamond, and Stephen Boyd. "Convex Optimization in Julia." In 2014 First Workshop for High Performance Technical Computing in Dynamic Languages (HPTCDL). IEEE, 2014. http://dx.doi.org/10.1109/hptcdl.2014.5.

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Tsianos, Konstantinos I., and Michael G. Rabbat. "Distributed strongly convex optimization." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483272.

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Boyd, Stephen, Lieven Vandenberghe, and Michael Grant. "Advances in Convex Optimization." In 2006 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.280567.

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Ramirez, Lennin Mallma, Alexandre Belfort de Almeida Chiacchio, Nelson Maculan Filho, Rodrigo de Souza Couto, Adilson Xavier, and Vinicius Layter Xavier. "HALA in Convex Optimization." In ANAIS DO SIMPóSIO BRASILEIRO DE PESQUISA OPERACIONAL. Galoa, 2023. http://dx.doi.org/10.59254/sbpo-2023-175132.

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Liu, Xinfu, and Ping Lu. "Solving Non-Convex Optimal Control Problems by Convex Optimization." In AIAA Guidance, Navigation, and Control (GNC) Conference. American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-4725.

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Tsitsiklis, John N., and Zhi-quan Luo. "Communication complexity of convex optimization." In 1986 25th IEEE Conference on Decision and Control. IEEE, 1986. http://dx.doi.org/10.1109/cdc.1986.267379.

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Raporty organizacyjne na temat "Convex optimization"

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Coffrin, Carleton James, and Line Alnaes Roald. Convex Relaxations in Power System Optimization, A Brief Introduction. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1461380.

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Tran, Tuyen. Convex and Nonconvex Optimization Techniques for Multifacility Location and Clustering. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7356.

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Giles, Daniel. The Majorization Minimization Principle and Some Applications in Convex Optimization. Portland State University Library, 2015. http://dx.doi.org/10.15760/honors.175.

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Deits, Robin, and Russ Tedrake. Footstep Planning on Uneven Terrain with Mixed-Integer Convex Optimization. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609276.

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Wen, Zaiwen, and Donald Goldfarb. A Line Search Multigrid Method for Large-Scale Convex Optimization. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada478093.

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Lawrence, Nathan. Convex and Nonconvex Optimization Techniques for the Constrained Fermat-Torricelli Problem. Portland State University Library, 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. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612792.

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Knapp, Adam C., and Kevin J. Johnson. Using Fisher Information Criteria for Chemical Sensor Selection via Convex Optimization Methods. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ada640843.

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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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