Relevant bibliographies by topics / Methods of nonlinear 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 'Methods of nonlinear programming'

Author: Grafiati
Published: 28 May 2022
Last updated: 30 July 2025

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Journal articles on the topic "Methods of nonlinear programming"

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Nie, Pu-yan. "Sequential penalty quadratic programming filter methods for nonlinear programming." Nonlinear Analysis: Real World Applications 8, no. 1 (2007): 118–29. http://dx.doi.org/10.1016/j.nonrwa.2005.06.003.

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Pshenichnyi, B. N., and E. E. Kirik. "Nonlinear programming methods and network flows." Cybernetics and Systems Analysis 30, no. 6 (1994): 846–54. http://dx.doi.org/10.1007/bf02366443.

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Patriksson, M. "Partial linearization methods in nonlinear programming." Journal of Optimization Theory and Applications 78, no. 2 (1993): 227–46. http://dx.doi.org/10.1007/bf00939668.

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Lin, Ming-Hua, John Gunnar Carlsson, Dongdong Ge, Jianming Shi, and Jung-Fa Tsai. "A Review of Piecewise Linearization Methods." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/101376.

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Various optimization problems in engineering and management are formulated as nonlinear programming problems. Because of the nonconvexity nature of this kind of problems, no efficient approach is available to derive the global optimum of the problems. How to locate a global optimal solution of a nonlinear programming problem is an important issue in optimization theory. In the last few decades, piecewise linearization methods have been widely applied to convert a nonlinear programming problem into a linear programming problem or a mixed-integer convex programming problem for obtaining an appro
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Chen, Zhong. "Parallel Iterative Methods for Nonlinear Programming Problems." Advanced Materials Research 159 (December 2010): 105–10. http://dx.doi.org/10.4028/www.scientific.net/amr.159.105.

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In this paper, we present two parallel multiplicative algorithms for convex programming. If the objective function is differentiable and convex on the positive orthant of , and it has compact level sets and has a locally Lipschitz continuous gradient, we prove these algorithms converge to a solution of minimization problem. For the proofs there are essentially used the results of sequential methods shown by Eggermont[1].
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Ghadimi, Saeed, Guanghui Lan, and Hongchao Zhang. "Generalized Uniformly Optimal Methods for Nonlinear Programming." Journal of Scientific Computing 79, no. 3 (2019): 1854–81. http://dx.doi.org/10.1007/s10915-019-00915-4.

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Byrd, Richard H., Jorge Nocedal, and Richard A. Waltz. "Steering exact penalty methods for nonlinear programming." Optimization Methods and Software 23, no. 2 (2008): 197–213. http://dx.doi.org/10.1080/10556780701394169.

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Nabatova, D. S. "METHODS OF NONLINEAR PROGRAMMING IN SOLVING FINITE GAMES." SOFT MEASUREMENTS AND COMPUTING 7, no. 80 (2024): 10–20. http://dx.doi.org/10.36871/2618-9976.2024.07.002.

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The paper considers the formulation of nonlinear programming problems for finding a Nash equilibrium situation in a bimatric game and in a game with three participants. An overview of existing methods for solving is given: an algorithm for solving bimatric LemkeHowson games, methods for solving quadratic and convex programming problems for a finite game with three participants.
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Aktamovich, Saipnazarov Shaylovbek, Khodjabaeva Dilbar, and Ortiqova Malika. "METHODS FOR SOLVING UN CONDITIONAL AND CONDITIONAL EXTREMUM PROBLEMS." International Journal of Advance Scientific Research 4, no. 6 (2024): 57–65. http://dx.doi.org/10.37547/ijasr-04-06-11.

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The article discusses conditional programming problems. Such problems can in principle, be solved using classical methods. However, along this path there are computational difficulties that make it necessary to search for other solution methods. Therefore, in this article we proposed particular methods for solving nonlinear programming problems.
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Białoń, Paweł. "Large-scale nonlinear programming algorithm using projection methods." Discussiones Mathematicae. Differential Inclusions, Control and Optimization 20, no. 2 (2000): 171. http://dx.doi.org/10.7151/dmdico.1011.

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Dissertations / Theses on the topic "Methods of nonlinear programming"

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Akrotirianakis, Ioannis. "Interior point methods for nonlinear programming and application to mixed integer nonlinear programming." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/7923.

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The main objectives of this thesis are (i) to develop primal-dual interior point algorithms for solving Nonlinear Programming (NLP) problems (ii) to develop a branch and cut algorithm for solving 0-1 Mixed Integer Nonlinear Programming (MINLP) problems and (iii) the application of the interior point algorithms to solve the NLP problems arising during the solution process of a 0-1 MINLP problem. Two primal-dual interior point algorithms are developed for solving NLP problems with both equality and inequality constraints. In the first algorithm the perturbed optimality conditions of the initial
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Nowak, Ivo. "Relaxation and decomposition methods for mixed integer nonlinear programming." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974403326.

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Yamakawa, Yuya. "Studies on Optimization Methods for Nonlinear Semidefinite Programming Problems." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199446.

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Lopez, Negrete de la Fuente Rodrigo. "Nonlinear Programming Sensitivity Based Methods for Constrained State Estimation." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/174.

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Model based control schemes, such as nonlinear model predictive control, assume that the full state vector of the plant is known for feedback control. However, in reality this is not always true. Most times only a set of noisy measurements are available, and thus, the unmeasured states need to be inferred from these measurements. This is done in combination with a detailed model of the system. The most common nonlinear state estimation methods do not have a means to deal with bounds or constraints on the states in an efficient or systematical way. These bounds and constraints are important in
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Wan, Wei. "Advances in Newton-based Barrier Methods for Nonlinear Programming." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/998.

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Nonlinear programming is a very important tool for optimizing many systems in science and engineering. The interior point solver IPOPT has become one of the most popular solvers for NLP because of its high performance. However, certain types of problems are still challenging for IPOPT. This dissertation considers three improvements or extensions to IPOPT to improve performance on several practical classes of problems. Compared to active set solvers that treat inequalities by identifying active constraints and transforming to equalities, the interior point method is less robust in the presence
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Nowak, Ivo. "Relaxation and decomposition methods for mixed integer nonlinear programming." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2005. http://dx.doi.org/10.18452/13962.

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Die Habilitationsschrift beschäftigt sich mit Theorie, Algorithmen und Software zur Lösung von nichtkonvexen, gemischt-ganzzahligen, nichtlinearen Optimierungsproblemen (MINLP). Sie besteht aus 14 Kapiteln, die in zwei Teile gegliedert sind. Im ersten Teil werden grundlegende Optimierungswerkzeuge beschrieben und im zweiten Teil werden Lösungsalgorithmen vorgestellt. Fast alle vorgeschlagenen Algorithmen wurden als Teil der objektorientierten C++ Bibliothek LaGO implementiert. Numerische Experimente mit verschiedenen MINLP-Problemen zeigen die Möglichkeiten und Grenzen dieser Verf
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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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Ou, Rongfu. "Parallel numerical integration methods for nonlinear dynamics." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/18181.

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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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Mitradjieva-Daneva, Maria. "Feasible Direction Methods for Constrained Nonlinear Optimization : Suggestions for Improvements." Doctoral thesis, Linköping : Department of Mathematics, Linköping University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8811.

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Books on the topic "Methods of nonlinear programming"

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Ya-xiang, Yuan, ed. Optimization theory and methods: Nonlinear programming. Springer, 2005.

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Hartley, Roger. Linear and nonlinear programming: An introduction to linear methods in mathematical programming. E. Horwood, 1985.

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Claus, Richter, Hollatz Horst, and Pallaschke Diethard, eds. Numerical methods of nonlinear programming and their implementations. Akademie Verlag, 1991.

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Nowak, Ivo. Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming. Birkhäuser Basel, 2005. http://dx.doi.org/10.1007/3-7643-7374-1.

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Du, Dingzhu. Convergence theory of feasible direction methods. Science Press, 1991.

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Floudas, Christodoulos A. Deterministic global optimization: Theory, methods, and applications. Kluwer Academic Publishers, 2000.

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Betts, John T. Practical methods for optimal control and estimation using nonlinear programming. 2nd ed. Society for Industrial and Applied Mathematics, 2010.

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Betts, John T. Practical methods for optimal control and estimation using nonlinear programming. 2nd ed. Society for Industrial and Applied Mathematics, 2009.

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Betts, John T. Practical methods for optimal control and estimation using nonlinear programming. 2nd ed. Society for Industrial and Applied Mathematics, 2009.

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Franklin, Joel N. Methods of mathematical economics: Linear and nonlinear programming, fixed-point theorems. Society for Industrial and Applied Mathematics, 2002.

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Book chapters on the topic "Methods of nonlinear programming"

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Fletcher, R. "Nonlinear Programming." In Practical Methods of Optimization. John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118723203.ch12.

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Killen, James. "Nonlinear Programming." In Mathematical Programming Methods for Geographers and Planners. Routledge, 2021. http://dx.doi.org/10.4324/9781003179030-8.

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Luenberger, David G., and Yinyu Ye. "Primal Methods." In Linear and Nonlinear Programming. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74503-9_12.

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Luenberger, David G., and Yinyu Ye. "Primal Methods." In Linear and Nonlinear Programming. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18842-3_12.

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Gill, Philip E., and Elizabeth Wong. "Sequential Quadratic Programming Methods." In Mixed Integer Nonlinear Programming. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1927-3_6.

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Luenberger, David G., and Yinyu Ye. "Quasi-Newton Methods." In Linear and Nonlinear Programming. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74503-9_10.

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Luenberger, David G., and Yinyu Ye. "Primal-Dual Methods." In Linear and Nonlinear Programming. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74503-9_15.

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Luenberger, David G., and Yinyu Ye. "Interior-Point Methods." In Linear and Nonlinear Programming. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74503-9_5.

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Luenberger, David G., and Yinyu Ye. "Basic Descent Methods." In Linear and Nonlinear Programming. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74503-9_8.

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Luenberger, David G., and Yinyu Ye. "Conjugate Direction Methods." In Linear and Nonlinear Programming. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74503-9_9.

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Conference papers on the topic "Methods of nonlinear programming"

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Durdán, Milan, Marek Laciak, Ján Kačur, Patrik Flegner, Ján Terpák, and Kristína Hobl̆áková. "Nonlinear Programming Methods Application in the Annealing Process." In 2025 26th International Carpathian Control Conference (ICCC). IEEE, 2025. https://doi.org/10.1109/iccc65605.2025.11022841.

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Kalra, Monika, Bavish Kumar, Bankush Kumar, and Tarun Arora. "A Comparative Analysis of Root-Finding Methods for Nonlinear Functions Using Python Programming Language." In 2024 International Conference on Computing, Sciences and Communications (ICCSC). IEEE, 2024. https://doi.org/10.1109/iccsc62048.2024.10830432.

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Wang, Qin, Bin Cheng, and Yi Huang. "Enhanced Pattern Search Combined with Filter Method for Solving Nonlinear Bilevel Programming Problems." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10868539.

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Lu, Peng, and James N. Siddall. "Nonlinear Programming Using Logic Methods." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0066.

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Abstract The algorithm presented here combines Boolean logic analysis with heuristic search to solve the general 0-1 polynomial programming problem. It can also be extended to solve continuous variable problems, after the problem has been converted to discrete polynomial form. Logic relations embedded in 0-1 variables are explored by a simple consensus operation to serve as the main minimization strategy. The procedure for sequential generation of prime implicants avoids rapidly increasing the number of prime implicants when problems become large. The computational results indicate that this a
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Zahara, Erwie, Yi-Tung Kao, and Chia-Hsin Hu. "A Hybrid Optimization Methods for Nonlinear Programming." In 2007 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2007. http://dx.doi.org/10.1109/ieem.2007.4419330.

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Briskin, Evgeniy S., Tatiana A. Tarasova, Victor V. Zhoga, Yaroslav V. Kalinin, Alexey P. Fedin, and Evgeniy A. Marchuk. "Walking machines movement optimization with fuzzy nonlinear programming methods." In 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR). IEEE, 2020. http://dx.doi.org/10.1109/dcnair50402.2020.9216938.

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Parthasarathy, Dinesh, Jonas Schmitt, and Harald Köstler. "Evolving Nonlinear Multigrid Methods With Grammar-Guided Genetic Programming." In GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation. ACM, 2023. http://dx.doi.org/10.1145/3583133.3590734.

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Matei, Ion, and John S. Baras. "Distributed nonlinear programming methods for optimization problems with inequality constraints." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402615.

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SKALECKI, LISA, and MARC MARTIN. "General adaptive guidance using nonlinear programming constraint solving methods (FAST)." In Navigation and Control Conference. American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-2820.

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Luo, Zhijun, Guohua Chen, and Lirong Wang. "A Modified Sequence Quadratic Programming Method for Nonlinear Programming." In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.42.

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Reports on the topic "Methods of nonlinear programming"

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Cai, Yongyang, Kenneth Judd, Thomas Lontzek, Valentina Michelangeli, and Che-Lin Su. Nonlinear Programming Method for Dynamic Programming. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19034.

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Martinez, H. J., Z. Parada, and R. A. Tapia. On the Characterization of Q-Superlinear Convergence of Quasi-Newton Interior-Point Methods for Nonlinear Programming. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada444979.

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El-Bakry, A. S., Richard A. Tapia, Y. Zhang, and T. Tsuchiya. On the Formulation and Theory of the Newton Interior-Point Method for Nonlinear Programming. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada453865.

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Brinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor-Mirghani. Adapting to Learn and Learning to Adapt: Practical Insights from International Development Projects. RTI Press, 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.

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Adaptive programming and management principles focused on learning, experimentation, and evidence-based decision making are gaining traction with donor agencies and implementing partners in international development. Adaptation calls for using learning to inform adjustments during project implementation. This requires information gathering methods that promote reflection, learning, and adaption, beyond reporting on pre-specified data. A focus on adaptation changes traditional thinking about program cycle. It both erases the boundaries between design, implementation, and evaluation and reframes
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Eslami, Keyvan, and Thomas M. Phelan. The Art of Temporal Approximation: An Investigation into Numerical Solutions to Discrete and Continuous-Time Problems in Economics. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202310.

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A recent literature within quantitative macroeconomics has advocated the use of continuous-time methods for dynamic programming problems. In this paper we explore the relative merits of continuous-time and discrete-time methods within the context of stationary and nonstationary income fluctuation problems. For stationary problems in two dimensions, the continuous-time approach is both more stable and typically faster than the discrete-time approach for any given level of accuracy. In contrast, for convex lifecycle problems (in which age or time enters explicitly), simply iterating backwards fr
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Goldfarb, Donald. Algorithms for Nonlinear Programming. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada161746.

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Murray, Walter. Research on Algorithms for Nonlinear Programming. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada188326.

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Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Yingjie Feng. Nonlinear Binscatter Methods. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1110.

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Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to primarily construct flexible yet interpretable visualization methods, but they can also be used to estimate treatment effects, assess uncertainty, and test substantive domain-specific hypotheses. This paper introduces novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation methods, covering generalized linear, robust, and quantile regressio
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Abramson, Mark A., Charles Audet, Jr Dennis, and J.E. Nonlinear Programming by Mesh Adaptive Direct Searches. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada444692.

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Murray, W., and M. A. Saunders. New approaches to linear and nonlinear programming. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/5254075.

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