Relevant bibliographies by topics / Programming (Mathematics) Convex programming

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Programming (Mathematics) Convex programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Programming (Mathematics) Convex programming"

1

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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Xu, Z. K., and S. C. Fang. "Unconstrained convex programming approach to linear programming." Journal of Optimization Theory and Applications 86, no. 3 (1995): 745–52. http://dx.doi.org/10.1007/bf02192167.

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fang, S. C., and H. S. J. Tsao. "An unconstrained convex programming approach to solving convex quadratic programming problems." Optimization 27, no. 3 (1993): 235–43. http://dx.doi.org/10.1080/02331939308843884.

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Fang, S. C. "An unconstrained convex programming view of linear programming." ZOR Zeitschrift f� Operations Research Methods and Models of Operations Research 36, no. 2 (1992): 149–61. http://dx.doi.org/10.1007/bf01417214.

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Kutateladze, S. S. "Variant of nontandard convex programming." Siberian Mathematical Journal 27, no. 4 (1987): 537–44. http://dx.doi.org/10.1007/bf00969166.

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Jeyakumar, V., and B. Mond. "On generalised convex mathematical programming." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 34, no. 1 (1992): 43–53. http://dx.doi.org/10.1017/s0334270000007372.

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AbstractThe sufficient optimality conditions and duality results have recently been given for the following generalised convex programming problem:where the funtion f and g satisfyfor some η: X0 × X0 → ℝnIt is shown here that a relaxation defining the above generalised convexity leads to a new class of multi-objective problems which preserves the sufficient optimality and duality results in the scalar case, and avoids the major difficulty of verifying that the inequality holds for the same function η(. , .). Further, this relaxation allows one to treat certain nonlinear multi-objective fractio
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Rajasekera, J. R., and S. C. Fang. "On the convex programming approach to linear programming." Operations Research Letters 10, no. 6 (1991): 309–12. http://dx.doi.org/10.1016/0167-6377(91)90001-6.

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Trujillo-Cortez, R., and S. Zlobec. "Bilevel convex programming models." Optimization 58, no. 8 (2009): 1009–28. http://dx.doi.org/10.1080/02331930701763330.

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Weir, T. "Programming with semilocally convex functions." Journal of Mathematical Analysis and Applications 168, no. 1 (1992): 1–12. http://dx.doi.org/10.1016/0022-247x(92)90185-g.

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Champion, T. "Duality gap in convex programming." Mathematical Programming 99, no. 3 (2004): 487–98. http://dx.doi.org/10.1007/s10107-003-0461-z.

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Dissertations / Theses on the topic "Programming (Mathematics) Convex programming"

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Trujillo-Cortez, Refugio. "Stable convex parametric programming and applications." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=37856.

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This thesis is a study of stable perturbations in convex programming models. Stability of a general model is introduced as lower semicontinuity of the feasible set mapping. This stability is shown to be equivalent to the Robinson notion of stability and regularity. In the convex case, it is also equivalent to the full-rank Slater condition. Then, the relationships between various point-to-set mappings are studied for convex models and new implications between these mappings are established. Also, local and global optimality of parameters is studied. A new result here is a characterization of l
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Yue, Hongwei. "First-order affine scaling continuous method for convex quadratic programming." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/39.

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We develop several continuous method models for convex quadratic programming (CQP) problems with di.erent types of constraints. The essence of the continuous method is to construct one ordinary di.erential equation (ODE) system such that its limiting equilibrium point corresponds to an optimal solution of the underlying optimization problem. All our continuous method models share the main feature of the interior point methods, i.e., starting from any interior point, all the solution trajectories remain in the interior of the feasible regions. First, we present an a.ne scaling continuous metho
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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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Dong, Hongbo. "Copositive programming: separation and relaxations." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2692.

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A large portion of research in science and engineering, as well as in business, concerns one similar problem: how to make things "better”? Once properly modeled (although usually a highly nontrivial task), this kind of questions can be approached via a mathematical optimization problem. Optimal solution to a mathematical optimization problem, when interpreted properly, might corresponds to new knowledge, effective methodology or good decisions in corresponding application area. As already proved in many success stories, research in mathematical optimization has a significant impact on numerous
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Dadush, Daniel Nicolas. "Integer programming, lattice algorithms, and deterministic volume estimation." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44807.

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The main subject of this thesis is the development of new geometric tools and techniques for solving classic problems within the geometry of numbers and convex geometry. At a high level, the problems considered in this thesis concern the varied interplay between the continuous and the discrete, an important theme within computer science and operations research. The first subject we consider is the study of cutting planes for non-linear integer programs. Cutting planes have been implemented to great effect for linear integer programs, and so understanding their properties in more general settin
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Potaptchik, Marina. "Portfolio Selection Under Nonsmooth Convex Transaction Costs." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2940.

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We consider a portfolio selection problem in the presence of transaction costs. Transaction costs on each asset are assumed to be a convex function of the amount sold or bought. This function can be nondifferentiable in a finite number of points. The objective function of this problem is a sum of a convex twice differentiable function and a separable convex nondifferentiable function. We first consider the problem in the presence of linear constraints and later generalize the results to the case when the constraints are given by the convex piece-wise linear functions. <br /><br /
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Lehmann, Sonja [Verfasser], and Klaus [Akademischer Betreuer] Schittkowski. "A strictly feasible sequential convex programming method / Sonja Lehmann. Betreuer: Klaus Schittkowski." Bayreuth : Universitätsbibliothek Bayreuth, 2011. http://d-nb.info/1018017712/34.

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Li, Xinxin. "Some operator splitting methods for convex optimization." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/43.

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Many applications arising in various areas can be well modeled as convex optimization models with separable objective functions and linear coupling constraints. Such areas include signal processing, image processing, statistical learning, wireless networks, etc. If these well-structured convex models are treated as generic models and their separable structures are ignored in algorithmic design, then it is hard to effectively exploit the favorable properties that the objective functions possibly have. Therefore, some operator splitting methods have regained much attention from different areas for
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Theußl, Stefan, Florian Schwendinger, and Kurt Hornik. "ROI: An extensible R Optimization Infrastructure." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/5858/1/ROI_StatReport.pdf.

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Optimization plays an important role in many methods routinely used in statistics, machine learning and data science. Often, implementations of these methods rely on highly specialized optimization algorithms, designed to be only applicable within a specific application. However, in many instances recent advances, in particular in the field of convex optimization, make it possible to conveniently and straightforwardly use modern solvers instead with the advantage of enabling broader usage scenarios and thus promoting reusability. This paper introduces the R Optimization Infrastructure which p
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Wright, Stephen E. "Convergence and approximation for primal-dual methods in large-scale optimization /." Thesis, Connect to this title online; UW restricted, 1990. http://hdl.handle.net/1773/5751.

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Books on the topic "Programming (Mathematics) Convex programming"

1

Neil, Cameron. Introduction to linear and convex programming. Cambridge University Press, 1985.

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Șandru, Ovidiu-Ilie. Noneuclidean convexity: Applications in the programming theory. Editura Tehnică, 1998.

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Hiriart-Urruty, Jean-Baptiste. Fundamentals of convex analysis. Springer, 2001.

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1944-, Lemaréchal Claude, ed. Fundamentals of convex analysis. Springer, 2001.

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

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Foundations of optimization. Springer, 2010.

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Gao, David Yang. Duality principles in nonconvex systems: Theory, methods, and applications. Kluwer Academic Publishers, 2000.

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Convex analysis and global optimization. Kluwer Academic Publishers, 1998.

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Hiriart-Urruty, Jean-Baptiste. Convex analysis and minimization algorithms. 2nd ed. Springer-Verlag, 1996.

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Hiriart-Urruty, Jean-Baptiste. Convex analysis and minimization algorithms. Springer-Verlag, 1993.

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Book chapters on the topic "Programming (Mathematics) Convex programming"

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Barbu, Viorel, and Teodor Precupanu. "Convex Programming." In Springer Monographs in Mathematics. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2247-7_3.

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Peressini, Anthony L., J. J. Uhl, and Francis E. Sullivan. "Convex Sets and Convex Functions." In The Mathematics of Nonlinear Programming. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-1025-2_2.

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Borwein, Jonathan M., and D. Russell Luke. "Duality and Convex Programming." In Handbook of Mathematical Methods in Imaging. Springer New York, 2014. http://dx.doi.org/10.1007/978-3-642-27795-5_7-4.

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Borwein, Jonathan M., and D. Russell Luke. "Duality and Convex Programming." In Handbook of Mathematical Methods in Imaging. Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-92920-0_7.

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Borwein, Jonathan M., and D. Russell Luke. "Duality and Convex Programming." In Handbook of Mathematical Methods in Imaging. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-0790-8_7.

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Schittkowski, K., and C. Zillober. "Sequential Convex Programming Methods." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-88272-2_8.

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Auslender, Alfred. "Numerical methods for nondifferentiable convex optimization." In Mathematical Programming Studies. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/bfb0121157.

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Peressini, Anthony L., J. J. Uhl, and Francis E. Sullivan. "Convex Programming and the Karush-Kuhn-Tucker Conditions." In The Mathematics of Nonlinear Programming. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-1025-2_5.

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Ponstein, J. "From Convex to Mixed Programming." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-45610-7_4.

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Courtillot, M. "A METHOD FOR CONVEX PROGRAMMING." In Proceedings of the Princeton Symposium on Mathematical Programming. Princeton University Press, 2015. http://dx.doi.org/10.1515/9781400869930-042.

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Conference papers on the topic "Programming (Mathematics) Convex programming"

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Jiang, Tao, and Mehran Chirehdast. "A Systems Approach to Structural Topology Optimization: Designing Optimal Connections." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/dac-1474.

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Abstract In this paper, structural topology optimization is extended to systems design. Locations and patterns of connections in a structural system that consists of multiple components strongly affect its performance. Topology of connections is defined, and a new classification for structural optimization is introduced that includes the topology optimization problem for connections. A mathematical programming problem is formulated that addresses this design problem. A convex approximation method using analytical gradients is used to solve the optimization problem. This solution method is read
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Shen, Xinyue, Steven Diamond, Yuantao Gu, and Stephen Boyd. "Disciplined convex-concave programming." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798400.

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Shen, Xinyue, Steven Diamond, Madeleine Udell, Yuantao Gu, and Stephen Boyd. "Disciplined multi-convex programming." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978647.

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Shi, Yingying, Zhihui Li, and Jiannan Wang. "E-convex Bilevel Programming." In Its Applications and Embedded Sys (CDEE). IEEE, 2010. http://dx.doi.org/10.1109/cdee.2010.17.

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Hsu, Justin, Zhiyi Huang, Aaron Roth, and Zhiwei Steven Wu. "Jointly Private Convex Programming." In Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974331.ch43.

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Pacheco, Ana, Anabela Gomes, Joana Henriques, Ana Maria de Almeida, and António José Mendes. "Mathematics and programming." In the 9th International Conference. ACM Press, 2008. http://dx.doi.org/10.1145/1500879.1500963.

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Luo, Xiaomei, K. Wong, Yiqiang Wu, and Chisheng Li. "Optimal Transceiver Design via Convex Programming." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.344532.

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Rivera, Mariano, Oscar Dalmau, and Josue Tago. "Image segmentation by convex quadratic programming." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761385.

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Peng Du, Shih-Hung Weng, Xiang Hu, and Chung-Kuan Cheng. "Power grid sizing via convex programming." In 2011 IEEE 9th International Conference on ASIC (ASICON 2011). IEEE, 2011. http://dx.doi.org/10.1109/asicon.2011.6157190.

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Xiong, Dapeng, Guoxiang Gu, and Kemin Zhou. "Identification in hH∞ via convex programming." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4793129.

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Reports on the topic "Programming (Mathematics) Convex programming"

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Jarre, Florian. Interior-Point Methods for Convex Programming. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada231372.

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McCormick, Garth P., and Christoph Witzgall. On weakly analytic and faithfully convex functions in convex programming. National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6426.

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McCormick, Garth P. Limits of SUMT trajectories in convex programming. National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.ir.6074.

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Jarre, Florian, and Michael A. Saunders. Practical Aspects of an Interior-Point Method for Convex Programming. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada239457.

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