Academic literature on the topic 'Global Optimization'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Global Optimization.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Global Optimization"
Sahinidis, Nikolaos V. "Global optimization." Optimization Methods and Software 24, no. 4-5 (October 2009): 479–82. http://dx.doi.org/10.1080/10556780903135287.
Full textHartke, Bernd. "Global optimization." Wiley Interdisciplinary Reviews: Computational Molecular Science 1, no. 6 (May 12, 2011): 879–87. http://dx.doi.org/10.1002/wcms.70.
Full textStephens, C. P., and W. Baritompa. "Global Optimization Requires Global Information." Journal of Optimization Theory and Applications 96, no. 3 (March 1998): 575–88. http://dx.doi.org/10.1023/a:1022612511618.
Full textBlack, Fischer, and Robert Litterman. "Global Portfolio Optimization." Financial Analysts Journal 48, no. 5 (September 1992): 28–43. http://dx.doi.org/10.2469/faj.v48.n5.28.
Full textSellis, Timos K. "Global query optimization." ACM SIGMOD Record 15, no. 2 (June 15, 1986): 191–205. http://dx.doi.org/10.1145/16856.16874.
Full textOdili, Julius Beneoluchi, and A. Noraziah. "African Buffalo Optimization for Global Optimization." Current Science 114, no. 03 (February 10, 2018): 627. http://dx.doi.org/10.18520/cs/v114/i03/627-636.
Full textNg, Chi-Kong, Duan Li, and Lian-Sheng Zhang. "Global Descent Method for Global Optimization." SIAM Journal on Optimization 20, no. 6 (January 2010): 3161–84. http://dx.doi.org/10.1137/090749815.
Full textBeheshti, Zahra, Siti Mariyam Shamsuddin, and Sarina Sulaiman. "Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems." Mathematical Problems in Engineering 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/907386.
Full textOuyang, Hai-bin, Li-qun Gao, Xiang-yong Kong, De-xuan Zou, and Steven Li. "Teaching-learning based optimization with global crossover for global optimization problems." Applied Mathematics and Computation 265 (August 2015): 533–56. http://dx.doi.org/10.1016/j.amc.2015.05.012.
Full textKassoul, Khelil, Nicolas Zufferey, Naoufel Cheikhrouhou, and Samir Brahim Belhaouari. "Exponential Particle Swarm Optimization for Global Optimization." IEEE Access 10 (2022): 78320–44. http://dx.doi.org/10.1109/access.2022.3193396.
Full textDissertations / Theses on the topic "Global Optimization"
Singer, Adam B. "Global dynamic optimization." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28662.
Full textIncludes bibliographical references (p. 247-256).
(cont.) on a set composed of the Cartesian product between the parameter bounds and the state bounds. Furthermore, I show that the solution of the differential equations is affine in the parameters. Because the feasible set is convex pointwise in time, the standard result that a convex function composed with an affine function remains convex yields the desired result that the integrand is convex under composition. Additionally, methods are developed using interval arithmetic to derive the exact state bounds for the solution of a linear dynamic system. Given a nonzero tolerance, the method is rigorously shown to converge to the global solution in a finite time. An implementation is developed, and via a collection of case studies, the technique is shown to be very efficient in computing the global solutions. For problems with embedded nonlinear dynamic systems, the analysis requires a more sophisticated composition technique attributed to McCormick. McCormick's composition technique provides a method for computing a convex underestimator for for the integrand given an arbitrary nonlinear dynamic system provided that convex underestimators and concave overestimators can be given for the states. Because the states are known only implicitly via the solution of the nonlinear differential equations, deriving these convex underestimators and concave overestimators is a highly nontrivial task. Based on standard optimization results, outer approximation, the affine solution to linear dynamic systems, and differential inequalities, I present a novel method for constructing convex underestimators and concave overestimators for arbitrary nonlinear dynamic systems ...
My thesis focuses on global optimization of nonconvex integral objective functions subject to parameter dependent ordinary differential equations. In particular, efficient, deterministic algorithms are developed for solving problems with both linear and nonlinear dynamics embedded. The techniques utilized for each problem classification are unified by an underlying composition principle transferring the nonconvexity of the embedded dynamics into the integral objective function. This composition, in conjunction with control parameterization, effectively transforms the problem into a finite dimensional optimization problem where the objective function is given implicitly via the solution of a dynamic system. A standard branch-and-bound algorithm is employed to converge to the global solution by systematically eliminating portions of the feasible space by solving an upper bounding problem and convex lower bounding problem at each node. The novel contributions of this work lie in the derivation and solution of these convex lower bounding relaxations. Separate algorithms exist for deriving convex relaxations for problems with linear dynamic systems embedded and problems with nonlinear dynamic systems embedded. However, the two techniques are unified by the method for relaxing the integral in the objective function. I show that integrating a pointwise in time convex relaxation of the original integrand yields a convex underestimator for the integral. Separate composition techniques, however, are required to derive relaxations for the integrand depending upon the nature of the embedded dynamics; each case is addressed separately. For problems with embedded linear dynamic systems, the nonconvex integrand is relaxed pointwise in time
by Adam Benjamin Singer.
Ph.D.
Ruan, Ning. "Global optimization for nonconvex optimization problems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1936.
Full textMoore, Roxanne Adele. "Value-based global optimization." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44750.
Full textBirbil, Sevket Ilker. "Stochastic Global Optimization Techniques." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020403-171452.
Full textIn this research, a novel population-based global optimization method has been studied. The method is called Electromagnetism-like Mechanism or in short EM. The proposed method mimicks the behavior of electrically charged particles. In other words, a set of points is sampled from the feasible region and these points imitate the role of the charged particles in basic electromagnetism. The underlying idea of the method is directing sample points toward local optimizers, which point out attractive regions of the feasible space.The proposed method has been applied to different test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature. Without using the higher order information, EM has converged rapidly (in terms of the number of function evaluations) to the global optimum and produced highly efficient results for problems of varying degree of difficulty.After a systematic study of the underlying stochastic process, the proof of convergence to the global optimum has been given for the proposed method. The thrust of the proof has been to show that in the limit, at least one of the points in the population moves to the neighborhood of the global optimum with probability one.The structure of the proposed method is very flexible permitting the easy development of variations. Capitalizing on this, several variants of the proposed method has been developed and compared with the other methods from the literature. These variants of EM have been able to provide accurate answers to selected problems and in many cases have been able to outperform other well-known methods.
Gattupalli, Rajeswar R. "Advances in global optimization /." View online ; access limited to URI, 2008. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3314454.
Full textZehnder, Nino. "Global optimization of laminated structures /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17573.
Full textQuttineh, Nils-Hassan. "Algorithms for Costly Global Optimization." Licentiate thesis, Mälardalen University, School of Education, Culture and Communication, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-5970.
Full textThere exists many applications with so-called costly problems, which means that the objective function you want to maximize or minimize cannot be described using standard functions and expressions. Instead one considers these objective functions as ``black box'' where the parameter values are sent in and a function value is returned. This implies in particular that no derivative information is available.The reason for describing these problems as expensive is that it may take a long time to calculate a single function value. The black box could, for example, solve a large system of differential equations or carrying out a heavy simulation, which can take anywhere from several minutes to several hours!These very special conditions therefore requires customized algorithms. Common optimization algorithms are based on calculating function values every now and then, which usually can be done instantly. But with an expensive problem, it may take several hours to compute a single function value. Our main objective is therefore to create algorithms that exploit all available information to the limit before a new function value is calculated. Or in other words, we want to find the optimal solution using as few function evaluations as possible.A good example of real life applications comes from the automotive industry, where on the development of new engines utilize advanced models that are governed by a dozen key parameters. The goal is to optimize the model by changing the parameters in such a way that the engine becomes as energy efficient as possible, but still meets all sorts of demands on strength and external constraints.
Schonlau, Matthias. "Computer experiments and global optimization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22234.pdf.
Full textAl-Mharmah, Hisham. "Global optimization of stochastic functions." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25665.
Full textOulmane, Mourad. "On-Chip global interconnect optimization." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33985.
Full textAccurate moment matching techniques for computing the RC delays and transition times are used in addition to an accurate CMOS inverter/repeater delay model that takes into account short channel effects that are prevalent in deep submicron (DSM) technologies. In particular, a new delay metric, based on the first two moments of the impulse response of the interconnect RC circuit, is derived. Also, a new empirical ramp approximation that takes into account the inherent asymmetry of signals in signal distribution networks in DSM technologies is presented.
Books on the topic "Global Optimization"
Horst, Reiner, and Hoang Tuy. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3.
Full textHorst, Reiner, and Hoang Tuy. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5.
Full textHorst, Reiner, and Hoang Tuy. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7.
Full textTörn, Aimo, and Antanas Žilinskas, eds. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-50871-6.
Full textPintér, János D., ed. Global Optimization. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-30927-6.
Full textLiberti, Leo, and Nelson Maculan, eds. Global Optimization. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-30528-9.
Full textSchäffler, Stefan. Global Optimization. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1.
Full textA, Zhilinskas, ed. Global optimization. Berlin: Springer-Verlag, 1989.
Find full textFloudas, Christodoulos A. Deterministic Global Optimization. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-4949-6.
Full textChew, Soo Hong, and Quan Zheng. Integral Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-46623-6.
Full textBook chapters on the topic "Global Optimization"
Tuy, Hoang, Steffen Rebennack, and Panos M. Pardalos. "Global Optimization." In Encyclopedia of Operations Research and Management Science, 650–58. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1142.
Full textKulisch, Ulrich, Rolf Hammer, Matthias Hocks, and Dietmar Ratz. "Global Optimization." In C++ Toolbox for Verified Computing I, 312–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79651-7_14.
Full textKulisch, Ulrich, Rolf Hammer, Matthias Hocks, and Dietmar Ratz. "Global Optimization." In C++ Toolbox for Verified Computing I, 113–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79651-7_7.
Full textForst, Wilhelm, and Dieter Hoffmann. "Global Optimization." In Optimization—Theory and Practice, 341–63. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-78977-4_8.
Full textHeitzinger, Clemens. "Global Optimization." In Algorithms with JULIA, 307–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16560-3_11.
Full textKulisch, Ulrich, Rolf Hammer, Dietmar Ratz, and Matthias Hocks. "Global Optimization." In Springer Series in Computational Mathematics, 282–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78423-1_14.
Full textKulisch, Ulrich, Rolf Hammer, Dietmar Ratz, and Matthias Hocks. "Global Optimization." In Springer Series in Computational Mathematics, 105–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78423-1_7.
Full textSchäffler, Stefan. "Unconstrained Global Optimization." In Global Optimization, 21–55. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1_3.
Full textSchäffler, Stefan. "Constrained Global Optimization." In Global Optimization, 75–103. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1_5.
Full textSchäffler, Stefan. "Vector Optimization." In Global Optimization, 105–17. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1_6.
Full textConference papers on the topic "Global Optimization"
Sellis, Timos K. "Global query optimization." In the 1986 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/16894.16874.
Full textNAN, Marin Silviu. "EXACT GLOBAL OPTIMIZATION." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/21/s07.039.
Full textDaboul, Siad, Stephan Held, Bento Natura, and Daniel Rotter. "Global Interconnect Optimization." In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2019. http://dx.doi.org/10.1109/iccad45719.2019.8942155.
Full textBánhelyi, Balázs, Tibor Csendes, Balázs Lévai, Dániel Zombori, and László Pál. "Improved versions of the GLOBAL optimization algorithm and the globalJ modularized toolbox." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089989.
Full textCalvin, James M., Craig J. Gotsman, and Cuicui Zheng. "Global optimization for image registration." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089975.
Full textKosolap, Anatolii. "Quadratic regularization for global optimization." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5090001.
Full textBOGDAN, Constantin. "NUMERICAL NONLINEAR GLOBAL OPTIMIZATION." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/21/s07.061.
Full textBilbro, Griff L. "Fast stochastic global optimization." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Su-Shing Chen. SPIE, 1993. http://dx.doi.org/10.1117/12.162050.
Full textRogers, John. "Global optimization and desensitization." In SPIE Optifab, edited by Julie L. Bentley and Sebastian Stoebenau. SPIE, 2015. http://dx.doi.org/10.1117/12.2196010.
Full textJagadish, H. V., Hui Jin, Beng Chin Ooi, and Kian-Lee Tan. "Global optimization of histograms." In the 2001 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375663.375687.
Full textReports on the topic "Global Optimization"
Dunlavy, Daniel M., and Dianne P. O'Leary. Homotopy optimization methods for global optimization. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/876373.
Full textHaftka, Raphael T. Theory and Algorithms for Global/Local Design Optimization. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada437353.
Full textMalony, Allen D., and Sameer S. Shende. MOGO: Model-Oriented Global Optimization of Petascale Applications. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1096588.
Full textEskow, Elizabeth, and Robert B. Schnabel. Mathematical Modeling of a Parallel Global Optimization Algorithm. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada446514.
Full textGlover, Fred. Probabilistic Methods for Global Optimization in Continuous Variables. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada304297.
Full textGlover, Fred. Probabilistic Methods or Global Optimization in Continuous Variables. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada311405.
Full textMarcus, Steven I., Michael C. Fu, and Jiaqiao Hu. Simulation-Based Methodologies for Global Optimization and Planning. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada591505.
Full textOblow, E. M. STP: A Stochastic Tunneling Algorithm for Global Optimization. Office of Scientific and Technical Information (OSTI), May 1999. http://dx.doi.org/10.2172/814395.
Full textHART, WILLIAM E. LDRD Final Report: Global Optimization for Engineering Science Problems. Office of Scientific and Technical Information (OSTI), December 1999. http://dx.doi.org/10.2172/15153.
Full textAnderson, P. B., D. W. Norton, and M. A. Young. CM-5 Kernel Optimization of a Global Weather Model. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada295796.
Full text