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Relevant bibliographies by topics / Semidefinite programming

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Academic literature on the topic 'Semidefinite programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Semidefinite programming"

1

Helmberg, C. "Semidefinite programming." European Journal of Operational Research 137, no. 3 (March 2002): 461–82. http://dx.doi.org/10.1016/s0377-2217(01)00143-6.

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Vandenberghe, Lieven, and Stephen Boyd. "Semidefinite Programming." SIAM Review 38, no. 1 (March 1996): 49–95. http://dx.doi.org/10.1137/1038003.

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Overton, Michael, and Henry Wolkowicz. "Semidefinite programming." Mathematical Programming 77, no. 1 (April 1997): 105–9. http://dx.doi.org/10.1007/bf02614431.

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Yurtsever, Alp, Joel A. Tropp, Olivier Fercoq, Madeleine Udell, and Volkan Cevher. "Scalable Semidefinite Programming." SIAM Journal on Mathematics of Data Science 3, no. 1 (January 2021): 171–200. http://dx.doi.org/10.1137/19m1305045.

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Vandenberghe, Lieven, and Stephen Boyd. "Applications of semidefinite programming." Applied Numerical Mathematics 29, no. 3 (March 1999): 283–99. http://dx.doi.org/10.1016/s0168-9274(98)00098-1.

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Goldfarb, D., and K. Scheinberg. "On parametric semidefinite programming." Applied Numerical Mathematics 29, no. 3 (March 1999): 361–77. http://dx.doi.org/10.1016/s0168-9274(98)00102-0.

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Kalantari, Bahman. "Semidefinite programming and matrix scaling over the semidefinite cone." Linear Algebra and its Applications 375 (December 2003): 221–43. http://dx.doi.org/10.1016/s0024-3795(03)00664-5.

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Lidický, Bernard, and Florian Pfender. "Semidefinite Programming and Ramsey Numbers." SIAM Journal on Discrete Mathematics 35, no. 4 (January 2021): 2328–44. http://dx.doi.org/10.1137/18m1169473.

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Bofill, Walter Gómez, and Juan A. Gómez. "LINEAR AND NONLINEAR SEMIDEFINITE PROGRAMMING." Pesquisa Operacional 34, no. 3 (December 2014): 495–520. http://dx.doi.org/10.1590/0101-7438.2014.034.03.0495.

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Zhang, Tianyu, and Liwei Zhang. "Critical Multipliers in Semidefinite Programming." Asia-Pacific Journal of Operational Research 37, no. 04 (May 19, 2020): 2040012. http://dx.doi.org/10.1142/s0217595920400126.

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It was proved in Izmailov and Solodov (2014). Newton-Type Methods for Optimization and Variational Problems, Springer] that the existence of a noncritical multiplier for a (smooth) nonlinear programming problem is equivalent to an error bound condition for the Karush–Kuhn–Thcker (KKT) system without any assumptions. This paper investigates whether this result still holds true for a (smooth) nonlinear semidefinite programming (SDP) problem. The answer is negative: the existence of noncritical multiplier does not imply the error bound condition for the KKT system without additional conditions, which is illustrated by an example. In this paper, we obtain characterizations, in terms of the problem data, the critical and noncritical multipliers for a SDP problem. We prove that, for the SDP problem, the noncriticality property can be derived from the error bound condition for the KKT system without any assumptions, and we give an example to show that the noncriticality does not imply the error bound for the KKT system. We propose a set of assumptions under which the error bound condition for the KKT system can be derived from the noncriticality property. a Finally, we establish a new error bound for [Formula: see text]-part, which is expressed by both perturbation and the multiplier estimation.

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Dissertations / Theses on the topic "Semidefinite programming"

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Zhu, Yuntao. "Semidefinite programming under uncertainty." Online access for everyone, 2006. http://www.dissertations.wsu.edu/Dissertations/summer2006/y%5Fzhu%5F073106.pdf.

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Jibrin, Shafiu. "Redundancy in semidefinite programming." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0010/NQ32337.pdf.

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Jibrin, Shafiu Carleton University Dissertation Mathematics and Statistics. "Redundancy in semidefinite programming." Ottawa, 1997.

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Wei, Hua. "Numerical Stability in Linear Programming and Semidefinite Programming." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2922.

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We study numerical stability for interior-point methods applied to Linear Programming, LP, and Semidefinite Programming, SDP. We analyze the difficulties inherent in current methods and present robust algorithms.

We start with the error bound analysis of the search directions for the normal equation approach for LP. Our error analysis explains the surprising fact that the ill-conditioning is not a significant problem for the normal equation system. We also explain why most of the popular LP solvers have a default stop tolerance of only 10^-8 when the machine precision on a 32-bit computer is approximately 10^-16.

We then propose a simple alternative approach for the normal equation based interior-point method. This approach has better numerical stability than the normal equation based method. Although, our approach is not competitive in terms of CPU time for the NETLIB problem set, we do obtain higher accuracy. In addition, we obtain significantly smaller CPU times compared to the normal equation based direct solver, when we solve well-conditioned, huge, and sparse problems by using our iterative based linear solver. Additional techniques discussed are: crossover; purification step; and no backtracking.

Finally, we present an algorithm to construct SDP problem instances with prescribed strict complementarity gaps. We then introduce two measures of strict complementarity gaps. We empirically show that: (i) these measures can be evaluated accurately; (ii) the size of the strict complementarity gaps correlate well with the number of iteration for the SDPT3 solver, as well as with the local asymptotic convergence rate; and (iii) large strict complementarity gaps, coupled with the failure of Slater's condition, correlate well with loss of accuracy in the solutions. In addition, the numerical tests show that there is no correlation between the strict complementarity gaps and the geometrical measure used in [31], or with Renegar's condition number.

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Zanjácomo, Paulo Régis. "On weighted paths for nonlinear semidefinite complementarity problems and newton methods for semidefinite programming." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/21680.

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Ye, Kai. "Applications of semidefinite programming in finance." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508489.

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Keuchel, Jens. "Image partitioning based on semidefinite programming." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11513861.

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Qian, Xun. "Continuous methods for convex programming and convex semidefinite programming." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/422.

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In this thesis, we study several interior point continuous trajectories for linearly constrained convex programming (CP) and convex semidefinite programming (SDP). The continuous trajectories are characterized as the solution trajectories of corresponding ordinary differential equation (ODE) systems. All our ODE systems are closely related to interior point methods.. First, we propose and analyze three continuous trajectories, which are the solutions of three ODE systems for linearly constrained convex programming. The three ODE systems are formulated based on an variant of the affine scaling direction, the central path, and the affine scaling direction in interior point methods. The resulting solutions of the first two ODE systems are called generalized affine scaling trajectory and generalized central path, respectively. Under some mild conditions, the properties of the continuous trajectories, the optimality and convergence of the continuous trajectories are all obtained. Furthermore, we show that for the example of Gilbert et al. [Math. Program., { 103}, 63-94 (2005)], where the central path does not converge, our generalized central path converges to an optimal solution of the same example in the limit.. Then we analyze two primal dual continuous trajectories for convex programming. The two continuous trajectories are derived from the primal-dual path-following method and the primal-dual affine scaling method, respectively. Theoretical properties of the two interior point continuous trajectories are fully studied. The optimality and convergence of both interior point continuous trajectories are obtained for any interior feasible point under some mild conditions. In particular, with proper choice of some parameters, the convergence for both continuous trajectories does not require the strict complementarity or the analyticity of the objective function.. For convex semidefinite programming, four interior continuous trajectories defined by matrix differential equations are proposed and analyzed. Optimality and convergence of the continuous trajectories are also obtained under some mild conditions. We also propose a strategy to guarantee the optimality of the affine scaling algorithm for convex SDP.

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Shen, Yijiang. "Binary image restoration by positive semidefinite programming and signomial programming." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39557431.

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沈逸江 and Yijiang Shen. "Binary image restoration by positive semidefinite programming and signomial programming." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39557431.

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Books on the topic "Semidefinite programming"

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Wolkowicz, Henry, Romesh Saigal, and Lieven Vandenberghe, eds. Handbook of Semidefinite Programming. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4381-7.

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de Klerk, Etienne. Aspects of Semidefinite Programming. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/b105286.

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Gärtner, Bernd, and Jiri Matousek. Approximation Algorithms and Semidefinite Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-22015-9.

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Gärtner, Bernd. Approximation algorithms and semidefinite programming. Heidelberg: Springer, 2012.

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5

Polyhedral and semidefinite programming methods in combinatorial optimization. Providence, R.I: American Mathematical Society, 2010.

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Tuncel, Levent. Polyhedral and semidefinite programming methods in combinatorial optimization. Providence, R.I: American Mathematical Society, 2010.

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Henry, Wolkowicz, Saigal Romesh, and Vandenberghe Lieven, eds. Handbook of semidefinite programming: Theory, algorithms, and applications. Boston: Kluwer Academic Publishers, 2000.

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Aspects of semidefinite programming: Interior point algorithms and selected applications. Dordrecht: Kluwer Academic Publishers, 2002.

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Klerk, Etienne de. Aspects of semidefinite programming: Interior point algorithms and selected applications. New York: Springer, 2011.

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Matoušek, Jiří. Approximation Algorithms and Semidefinite Programming. Springer, 2012.

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Book chapters on the topic "Semidefinite programming"

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Ramana, Motakuri V., and Panos M. Pardalos. "Semidefinite Programming." In Applied Optimization, 369–98. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-3449-1_9.

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Jansen, Benjamin. "Semidefinite Programming." In Interior Point Techniques in Optimization, 221–39. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-5561-9_10.

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Du, Ding-Zhu, Panos M. Pardalos, and Weili Wu. "Semidefinite Programming." In Nonconvex Optimization and Its Applications, 201–13. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5795-8_13.

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Du, Ding-Zhu, Ker-I. Ko, and Xiaodong Hu. "Semidefinite Programming." In Design and Analysis of Approximation Algorithms, 339–70. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1701-9_9.

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Vazirani, Vijay V. "Semidefinite Programming." In Approximation Algorithms, 255–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-04565-7_26.

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Shen, Chunhua, and Anton van den Hengel. "Semidefinite Programming." In Computer Vision, 717–19. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_688.

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Shen, Chunhua, and Anton van den Hengel. "Semidefinite Programming." In Computer Vision, 1131–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_688.

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Gärtner, Bernd, and Jiří Matoušek. "Semidefinite Programming." In Approximation Algorithms and Semidefinite Programming, 15–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22015-9_2.

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Floudas, Christodoulos A., Pãnos M. Pardalos, Claire S. Adjiman, William R. Esposito, Zeynep H. Gümüş, Stephen T. Harding, John L. Klepeis, Clifford A. Meyer, and Carl A. Schweiger. "Semidefinite Programming Problems." In Nonconvex Optimization and Its Applications, 251–61. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-3040-1_11.

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van Hoeve, Willem-Jan. "Semidefinite Programming and Constraint Programming." In Handbook on Semidefinite, Conic and Polynomial Optimization, 635–68. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-0769-0_22.

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Conference papers on the topic "Semidefinite programming"

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Krechetov, Mikhail, Jakub Marecek, Yury Maximov, and Martin Takac. "Entropy-Penalized Semidefinite Programming." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/157.

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Low-rank methods for semi-definite programming (SDP) have gained a lot of interest recently, especially in machine learning applications. Their analysis often involves determinant-based or Schatten-norm penalties, which are difficult to implement in practice due to high computational efforts. In this paper, we propose Entropy-Penalized Semi-Definite Programming (EP-SDP), which provides a unified framework for a broad class of penalty functions used in practice to promote a low-rank solution. We show that EP-SDP problems admit an efficient numerical algorithm, having (almost) linear time complexity of the gradient computation; this makes it useful for many machine learning and optimization problems. We illustrate the practical efficiency of our approach on several combinatorial optimization and machine learning problems.

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Le, Tuan Anh, and Mohammad Reza Nakhai. "Coordinated beamforming using semidefinite programming." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6364232.

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Choi, Hyungjin, Peter J. Seiler, and Sairaj V. Dhople. "Uncertainty propagation with Semidefinite Programming." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7403157.

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Berta, Mario, Francesco Borderi, Omar Fawzi, and Volkher B. Scholz. "Quantum Coding via Semidefinite Programming." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849325.

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Bandeira, Afonso S., Moses Charikar, Amit Singer, and Andy Zhu. "Multireference alignment using semidefinite programming." In ITCS'14: Innovations in Theoretical Computer Science. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2554797.2554839.

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Li, Wei, Fangzhou Wang, José M. F. Moura, and R. D. Blanton. "Global Floorplanning via Semidefinite Programming." In 2023 60th ACM/IEEE Design Automation Conference (DAC). IEEE, 2023. http://dx.doi.org/10.1109/dac56929.2023.10247967.

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Primbs, J. A. "Option pricing bounds via semidefinite programming." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1656391.

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Jensen, Tobias Lindstrom, and Lieven Vandenberghe. "Multi-pitch estimation using semidefinite programming." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952946.

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Noureddine, Hadi, Damien Castelain, and Ramesh Pyndiah. "Cooperative network localizability via semidefinite programming." In 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2011). IEEE, 2011. http://dx.doi.org/10.1109/pimrc.2011.6139714.

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Manchester, Zachary R., and Mason A. Peck. "Recursive Inertia Estimation with Semidefinite Programming." In AIAA Guidance, Navigation, and Control Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1902.

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Reports on the topic "Semidefinite programming"

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Ariyawansa, K. A., and Yuntao Zhu. Chance-Constrained Semidefinite Programming. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada530454.

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Ariyawansa, K. A. Stochastic Semidefinite Programming: Applications and Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada573242.

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Benson, S. J., and Y. Ye. DSDP5 user guide - software for semidefinite programming. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/947970.

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Jin, Shengping, K. A. Ariyawansa, and Yuntao Zhu. Homogeneous Self-Dual Algorithms for Stochastic Semidefinite Programming. Fort Belvoir, VA: Defense Technical Information Center, June 2011. http://dx.doi.org/10.21236/ada544763.

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Overton, Michael L. Final report, DOE Grant DE-FG02-98ER25352, Computational semidefinite programming. Office of Scientific and Technical Information (OSTI), September 2002. http://dx.doi.org/10.2172/806634.

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Mazziotti, David A. Parallel Large-scale Semidefinite Programming for Strong Electron Correlation: Using Correlation and Entanglement in the Design of Efficient Energy-Transfer Mechanisms. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada617270.

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