Gotowe bibliografie tematyczne / Nonlinear optimization

Gotowa bibliografia na temat „Nonlinear optimization”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 maja 2024

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Artykuły w czasopismach na temat "Nonlinear optimization"

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Moulard, Thomas, Benjamin Chr^|^eacute;tien i Eiichi Yoshida. "Software Tools for Nonlinear Optimization". Journal of the Robotics Society of Japan 32, nr 6 (2014): 536–41. http://dx.doi.org/10.7210/jrsj.32.536.

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YUGE, Kohei, Susumu Ejima i Junichi ABE. "Nonlinear Optimization". Reference Collection of Annual Meeting VIII.03.1 (2003): 61–62. http://dx.doi.org/10.1299/jsmemecjsm.viii.03.1.0_61.

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Salman, Abbas Musleh, i Ahmed Sabah Al-Jilawi. "Combinatorial Optimization and Nonlinear Optimization". Journal of Physics: Conference Series 1818, nr 1 (1.03.2021): 012134. http://dx.doi.org/10.1088/1742-6596/1818/1/012134.

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NASSERI, S. H. "FUZZY NONLINEAR OPTIMIZATION". Journal of Nonlinear Sciences and Applications 01, nr 04 (21.12.2008): 230–35. http://dx.doi.org/10.22436/jnsa.001.04.05.

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Mardle, S., i K. M. Miettinen. "Nonlinear Multiobjective Optimization". Journal of the Operational Research Society 51, nr 2 (luty 2000): 246. http://dx.doi.org/10.2307/254267.

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Yabe, Hiroshi, i Naoki Sakaiwa. "A NEW NONLINEAR CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION". Journal of the Operations Research Society of Japan 48, nr 4 (2005): 284–96. http://dx.doi.org/10.15807/jorsj.48.284.

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AMIR, Hossain M., i Takashi HASEGAWA. "Nonlinear discrete structural optimization." Doboku Gakkai Ronbunshu, nr 392 (1988): 61–71. http://dx.doi.org/10.2208/jscej.1988.392_61.

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Pardalos, Panos, i Stephen A. Vavasis. "Nonlinear Optimization: Complexity Issues." Mathematics of Computation 60, nr 201 (styczeń 1993): 440. http://dx.doi.org/10.2307/2153188.

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Belotti, Pietro, Christian Kirches, Sven Leyffer, Jeff Linderoth, James Luedtke i Ashutosh Mahajan. "Mixed-integer nonlinear optimization". Acta Numerica 22 (2.04.2013): 1–131. http://dx.doi.org/10.1017/s0962492913000032.

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Many optimal decision problems in scientific, engineering, and public sector applications involve both discrete decisions and nonlinear system dynamics that affect the quality of the final design or plan. These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling nonlinear functions. We review models and applications of MINLP, and survey the state of the art in methods for solving this challenging class of problems.Most solution methods for MINLP apply some form of tree search. We distinguish two broad classes of methods: single-tree and multitree methods. We discuss these two classes of methods first in the case where the underlying problem functions are convex. Classical single-tree methods include nonlinear branch-and-bound and branch-and-cut methods, while classical multitree methods include outer approximation and Benders decomposition. The most efficient class of methods for convex MINLP are hybrid methods that combine the strengths of both classes of classical techniques.Non-convex MINLPs pose additional challenges, because they contain non-convex functions in the objective function or the constraints; hence even when the integer variables are relaxed to be continuous, the feasible region is generally non-convex, resulting in many local minima. We discuss a range of approaches for tackling this challenging class of problems, including piecewise linear approximations, generic strategies for obtaining convex relaxations for non-convex functions, spatial branch-and-bound methods, and a small sample of techniques that exploit particular types of non-convex structures to obtain improved convex relaxations.We finish our survey with a brief discussion of three important aspects of MINLP. First, we review heuristic techniques that can obtain good feasible solution in situations where the search-tree has grown too large or we require real-time solutions. Second, we describe an emerging area of mixed-integer optimal control that adds systems of ordinary differential equations to MINLP. Third, we survey the state of the art in software for MINLP.
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Levy, Robert, i Huei-Shiang Perng. "Optimization for nonlinear stability". Computers & Structures 30, nr 3 (styczeń 1988): 529–35. http://dx.doi.org/10.1016/0045-7949(88)90286-6.

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Rozprawy doktorskie na temat "Nonlinear optimization"

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Skrobanski, Jerzy Jan. "Optimization subject to nonlinear constraints". Thesis, Imperial College London, 1986. http://hdl.handle.net/10044/1/7331.

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Strandberg, Mattias. "Portfolio Optimization with NonLinear Instruments". Thesis, Umeå universitet, Institutionen för fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-137233.

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Denton, Trip Shokoufandeh Ali. "Subset selection using nonlinear optimization /". Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/1763.

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Robinson, Daniel P. "Primal-dual methods for nonlinear optimization". Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3274512.

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Thesis (Ph. D.)--University of California, San Diego, 2007.
Title from first page of PDF file (viewed October 4, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 173-175).
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Raj, Ashish. "Evolutionary Optimization Algorithms for Nonlinear Systems". DigitalCommons@USU, 2013. http://digitalcommons.usu.edu/etd/1520.

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Many real world problems in science and engineering can be treated as optimization problems with multiple objectives or criteria. The demand for fast and robust stochastic algorithms to cater to the optimization needs is very high. When the cost function for the problem is nonlinear and non-differentiable, direct search approaches are the methods of choice. Many such approaches use the greedy criterion, which is based on accepting the new parameter vector only if it reduces the value of the cost function. This could result in fast convergence, but also in misconvergence where it could lead the vectors to get trapped in local minima. Inherently, parallel search techniques have more exploratory power. These techniques discourage premature convergence and consequently, there are some candidate solution vectors which do not converge to the global minimum solution at any point of time. Rather, they constantly explore the whole search space for other possible solutions. In this thesis, we concentrate on benchmarking three popular algorithms: Real-valued Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). The DE algorithm is found to out-perform the other algorithms in fast convergence and in attaining low-cost function values. The DE algorithm is selected and used to build a model for forecasting auroral oval boundaries during a solar storm event. This is compared against an established model by Feldstein and Starkov. As an extended study, the ability of the DE is further put into test in another example of a nonlinear system study, by using it to study and design phase-locked loop circuits. In particular, the algorithm is used to obtain circuit parameters when frequency steps are applied at the input at particular instances.
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Chryssochoos, Ioannis. "Optimization based control of nonlinear systems". Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399165.

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Wilson, Simon Paul. "Aircraft routing using nonlinear global optimization". Thesis, University of Hertfordshire, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275117.

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Soto, Jonathan. "Nonlinear contraction tools for constrained optimization". Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62538.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 77-78).
This thesis derives new results linking nonlinear contraction analysis, a recent stability theory for nonlinear systems, and constrained optimization theory. Although dynamic systems and optimization are both areas that have been extensively studied [21], few results have been achieved in this direction because strong enough tools for dynamic systems were not available. Contraction analysis provides the necessary mathematical background. Based on an operator that projects the speed of the system on the tangent space of the constraints, we derive generalizations of Lagrange parameters. After presenting some initial examples that show the relations between contraction and optimization, we derive a contraction theorem for nonlinear systems with equality constraints. The method is applied to examples in differential geometry and biological systems. A new physical interpretation of Lagrange parameters is provided. In the autonomous case, we derive a new algorithm to solve minimization problems. Next, we state a contraction theorem for nonlinear systems with inequality constraints. In the autonomous case, the algorithm solves minimization problems very fast compared to standard algorithms. Finally, we state another contraction theorem for nonlinear systems with time-varying equality constraints. A new generalization of time varying Lagrange parameters is given. In the autonomous case, we provide a solution for a new class of optimization problems, minimization with time-varying constraints.
by Jonathan Soto.
S.M.
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Prokopyev, Oleg A. "Nonlinear integer optimization and applications in biomedicine". [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015226.

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Zhang, Hongchao. "Gradient methods for large-scale nonlinear optimization". [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0013703.

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Książki na temat "Nonlinear optimization"

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Aragón, Francisco J., Miguel A. Goberna, Marco A. López i Margarita M. L. Rodríguez. Nonlinear Optimization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11184-7.

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Eiselt, H. A., i Carl-Louis Sandblom. Nonlinear Optimization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19462-8.

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Bomze, Immanuel M., Vladimir F. Demyanov, Roger Fletcher i Tamás Terlaky. Nonlinear Optimization. Redaktorzy Gianni Di Pillo i Fabio Schoen. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11339-0.

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Ruszczyński, Andrzej P. Nonlinear optimization. Princeton, NJ: Princeton University Press, 2006.

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Nonlinear optimization. Princeton, N.J: Princeton University Press, 2006.

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1958-, Bomze Immanuel M., Di Pillo G i Schoen Fabio, red. Nonlinear optimization. Heidelberg: Springer, 2010.

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Hillermeier, Claus. Nonlinear Multiobjective Optimization. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8280-4.

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Miettinen, Kaisa. Nonlinear Multiobjective Optimization. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5563-6.

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Du, Ding-Zhu, Panos M. Pardalos i Zhao Zhang, red. Nonlinear Combinatorial Optimization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16194-1.

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Miettinen, Kaisa. Nonlinear multiobjective optimization. Boston: Kluwer Academic Publishers, 1999.

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Części książek na temat "Nonlinear optimization"

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Nesterov, Yurii. "Nonlinear Optimization". W Applied Optimization, 1–50. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-8853-9_1.

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Sioshansi, Ramteen, i Antonio J. Conejo. "Nonlinear Optimization". W Springer Optimization and Its Applications, 197–285. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56769-3_4.

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Nesterov, Yurii. "Nonlinear Optimization". W Lectures on Convex Optimization, 3–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91578-4_1.

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Willis, Robert, i Brad A. Finney. "Nonlinear Optimization". W Environmental Systems Engineering and Economics, 297–434. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4615-0479-5_7.

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Beigi, Homayoon. "Nonlinear Optimization". W Fundamentals of Speaker Recognition, 773–839. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-77592-0_25.

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Goodarzi, Ehsan, Mina Ziaei i Edward Zia Hosseinipour. "Nonlinear Optimization". W Topics in Safety, Risk, Reliability and Quality, 55–109. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04400-2_3.

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Weik, Martin H. "nonlinear optimization". W Computer Science and Communications Dictionary, 1108. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_12435.

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Gao, Xiang, i Tao Zhang. "Nonlinear Optimization". W Introduction to Visual SLAM, 109–39. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4939-4_5.

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Leader, Jeffery J. "Nonlinear Optimization". W Numerical Analysis and Scientific Computation, 483–548. Wyd. 2. New York: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003042273-7.

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Eichfelder, Gabriele. "Nonlinear Scalarizations". W Vector Optimization, 89–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54283-1_5.

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Streszczenia konferencji na temat "Nonlinear optimization"

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McMahon, Peter L., Alireza Marandi, Yoshitaka Haribara, Ryan Hamerly, Carsten Langrock, Shuhei Tamate, Takahiro Inagaki i in. "Combinatorial optimization using networks of optical parametric oscillators". W Nonlinear Optics. Washington, D.C.: OSA, 2017. http://dx.doi.org/10.1364/nlo.2017.nm2b.2.

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Burger, Miloš, Jon Murphy, Lauren Finney, Nicholas Peskosky, John Nees, Karl Krushelnick i Igor Jovanovic. "Wavefront uniformity optimization of Laguerre-Gaussian ultrafast beams". W Nonlinear Optics. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/nlo.2023.m2b.2.

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We report the genetic algorithm-driven wavefront optimization of ultrafast Laguerre-Gaussian beams. Wavefront manipulation was performed using a deformable mirror. The results show that the intensity fluctuations along the perimeter of t he target ring-shaped profile can be reduced up to ~15%.
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Hoang, Van Thuy, Yassin Boussafa, Lynn Sader, Sébastien Février, Vincent Couderc i Benjamin Wetzel. "Machine learning optimization of supercontinuum properties towards multiphoton microscopy". W Nonlinear Photonics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/np.2022.nptu1g.3.

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We numerically study how the suitable adjustment of femtosecond pulse patterns in combination with machine learning can be leveraged to maximize the output spectral intensities and temporal waveforms at wavelengths relevant for multi-photon imaging.
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Pauliat, G., P. Mathey, G. Roosen, H. Rajbenbach i J. P. Huignard. "Signal to noise ratio optimization of photorefractive image amplifiers". W Nonlinear Optics. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nlo.1992.thb4.

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Jinno, Kenya. "Nonlinear Map Optimization". W 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018. http://dx.doi.org/10.1109/cec.2018.8477914.

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Ding, Edwin, i J. Nathan Kutz. "Operating Regimes and Performance Optimization in Mode-Locked Fiber Lasers". W Nonlinear Photonics. Washington, D.C.: OSA, 2010. http://dx.doi.org/10.1364/np.2010.ntuc15.

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Rosa, Paweł, Giuseppe Rizzelli i Juan Diego Ania-Castañón. "Signal Power Symmetry Optimization for Optical Phase Conjugation Using Raman Amplification". W Nonlinear Optics. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/nlo.2015.nw4a.36.

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Surya, Joshua B., Alexander W. Bruch, Juanjuan Lu, Zheng Gong, Yuntao Xu, Risheng Cheng, Sihao Wang i Hong X. Tang. "Optimization of Second Order Nonlinear Frequency Conversion in Lithium Niobate Microrings". W Nonlinear Optics. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/nlo.2019.nw3a.3.

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JONES, JR., JOHN. "Nonlinear control theory". W 4th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4740.

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BOGDAN, Constantin. "NUMERICAL NONLINEAR GLOBAL OPTIMIZATION". W 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/21/s07.061.

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Raporty organizacyjne na temat "Nonlinear optimization"

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Johnson, Michael M., Ann S. Yoshimura, Patricia Diane Hough i Heidi R. Ammerlahn. Nonlinear optimization for stochastic simulations. Office of Scientific and Technical Information (OSTI), grudzień 2003. http://dx.doi.org/10.2172/918225.

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Teel, Andrew R. Optimization-Based Robust Nonlinear Control. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2006. http://dx.doi.org/10.21236/ada452020.

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Robinson, Stephen M. Computation and Theory in Nonlinear Optimization. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 1996. http://dx.doi.org/10.21236/ada311415.

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Shanno, David F. Numerical Methods for Linear and Nonlinear Optimization. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1987. http://dx.doi.org/10.21236/ada190029.

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Rosen, J. B. Parallel Solution of Large-Scale Nonlinear Optimization. Fort Belvoir, VA: Defense Technical Information Center, listopad 1994. http://dx.doi.org/10.21236/ada294372.

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Shanno, David F. Numerical Methods for Linear and Nonlinear Optimization. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 1995. http://dx.doi.org/10.21236/ada299989.

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Nocedal, Jorge. Nonlinear Optimization Methods for Large-Scale Learning. Office of Scientific and Technical Information (OSTI), październik 2019. http://dx.doi.org/10.2172/1571768.

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He, An, i O. Chubar. Nonlinear Optimization in "Synchrotron Radiation Workshop" Code. Office of Scientific and Technical Information (OSTI), lipiec 2019. http://dx.doi.org/10.2172/1573467.

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Shanno, David. Numerical Methods for Linear and Nonlinear Optimization. Fort Belvoir, VA: Defense Technical Information Center, marzec 1998. http://dx.doi.org/10.21236/ada343437.

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P. D. Hough, T. G. Kolda i V. J. Torczon. Asynchronous parallel pattern search for nonlinear optimization. Office of Scientific and Technical Information (OSTI), styczeń 2000. http://dx.doi.org/10.2172/751003.

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