Relevant bibliographies by topics / Global function optimization

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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 'Global function optimization'

Author: Grafiati
Published: 16 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Global function optimization"

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Şahiner, Ahmet, Temel Ermiş, and Muhammad Wasim Awan. "A New Filled Function for Global Optimization." Analele Universitatii "Ovidius" Constanta - Seria Matematica 31, no. 3 (2023): 207–20. https://doi.org/10.2478/auom-2023-0039.

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Abstract The filled function method has recently become very popular in optimization theory, as it is an e cient and e ective method for finding the global minimizer of multimodal functions. However, the fact that the existing filled functions in the literature generally have exponential or logarithmic terms and/or parameter sensitivity reduces the e ectiveness of this method. In this study, we propose a new non parameter and without exponential/logarithmic terms filled function, which is numerically stable, and is successfully used to solve global optimization problems. Furthermore, we have d
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Liu, Y., and K. L. Teo. "A bridging method for global optimization." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 41, no. 1 (1999): 41–57. http://dx.doi.org/10.1017/s0334270000011024.

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AbstractIn this paper a bridging method is introduced for numerical solutions of one-dimensional global optimization problems where a continuously differentiable function is to be minimized over a finite interval which can be given either explicitly or by constraints involving continuously differentiable functions. The concept of a bridged function is introduced. Some properties of the bridged function are given. On this basis, several bridging algorithm are developed for the computation of global optimal solutions. The algorithms are demonstrated by solving several numerical examples.
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Norkin, Vladimir. "A Stochastic Smoothing Method for Nonsmooth Global Optimization." Cybernetics and Computer Technologies, no. 1 (March 31, 2020): 5–14. http://dx.doi.org/10.34229/2707-451x.20.1.1.

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The paper presents the results of testing the stochastic smoothing method for global optimization of a multiextremal function in a convex feasible subset of Euclidean space. Preliminarily, the objective function is extended outside the admissible region so that its global minimum does not change, and it becomes coercive. The smoothing of a function at any point is carried out by averaging the values of the function over some neighborhood of this point. The size of the neighborhood is a smoothing parameter. Smoothing eliminates small local extrema of the original function. With a sufficiently l
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Zhu, Jinghao, Jiani Zhou, and David Gao. "Global optimization by canonical dual function." Journal of Computational and Applied Mathematics 234, no. 2 (2010): 538–44. http://dx.doi.org/10.1016/j.cam.2009.12.045.

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Wang, Wei, Xiaoshan Zhang, and Min Li. "A Filled Function Method Dominated by Filter for Nonlinearly Global Optimization." Journal of Applied Mathematics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/245427.

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This work presents a filled function method based on the filter technique for global optimization. Filled function method is one of the effective methods for nonlinear global optimization, since it can effectively find a better minimizer. Filter technique is applied to local optimization methods for its excellent numerical results. In order to optimize the filled function method, the filter method is employed for global optimizations in this method. A new filled function is proposed first, and then the algorithm and its properties are proved. The numerical results are listed at the end.
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Satapathy, Suresh Chandra. "Improved teaching learning based optimization for global function optimization." Decision Science Letters 2, no. 1 (2013): 23–34. http://dx.doi.org/10.5267/j.dsl.2012.10.005.

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Satapathy, Suresh Chandra, Anima Naik, and K. Parvathi. "Weighted Teaching-Learning-Based Optimization for Global Function Optimization." Applied Mathematics 04, no. 03 (2013): 429–39. http://dx.doi.org/10.4236/am.2013.43064.

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DJALIL, BOUDJEHEM, BOUDJEHEM BADREDDINE, and BOUKAACHE ABDENOUR. "REDUCING DIMENSION IN GLOBAL OPTIMIZATION." International Journal of Computational Methods 08, no. 03 (2011): 535–44. http://dx.doi.org/10.1142/s0219876211002460.

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In this paper, we propose a very interesting idea in global optimization making it easer and a low-cost task. The main idea is to reduce the dimension of the optimization problem in hand to a mono-dimensional one using variables coding. At this level, the algorithm will look for the global optimum of a mono-dimensional cost function. The new algorithm has the ability to avoid local optima, reduces the number of evaluations, and improves the speed of the algorithm convergence. This method is suitable for functions that have many extremes. Our algorithm can determine a narrow space around the gl
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Bermudez, Dalton H. "Plane Based Optimization Method: A Method for Better Global Minimum Cost Function Localization." Journal of Biomedical and Engineering Research 3, no. 1 (2025): 01–05. https://doi.org/10.64030/3065-8780.03.01.03.

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Background: As a popular optimization algorithm, gradient descent is more prone to converge at a local minimum rather than a global one since it is highly dependent on how initial values are instantiated and an iterative process of updates, reducing its efficiency for non-convex cost functions. This limitation is of utmost importance in domains such as medical physics, where there exist many treatment parameters that need to be optimized. Purpose: We introduce the flat plane with maximum perpendicular distance method, a new optimization algorithm which identifies global minima robustly and eff
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Bemporad, Alberto. "Global optimization via inverse distance weighting and radial basis functions." Computational Optimization and Applications 77, no. 2 (2020): 571–95. http://dx.doi.org/10.1007/s10589-020-00215-w.

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Abstract Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimization vector where the surrogate is minimum (exploitation of the surrogate) and looking for regions of the feasible space that have not yet been visited and that may potentially contain better values of the objective function (exploration of the feasible space). This paper proposes a new glob
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Dissertations / Theses on the topic "Global function optimization"

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Gutmann, H. M. "Radial basis function methods for global optimization." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599804.

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In many real world optimization problems it is essential or desirable to determine the global minimum of the objective function. The subject of this dissertation is a new class of methods that tackle such problems. In particular, we have in mind problems where function evaluations are expensive and no additional information is available. The methods employ radial basis functions that have been proved to be useful for interpolation problems. Examples include thin plate splines and multiquadrics. Specifically, in each iteration, radial basis function interpolation is used to define a utility fun
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Bastani, Spencer, and Olov Andersson. "Stochastic Optimization in Dynamic Environments : with applications in e-commerce." Thesis, Linköping University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8509.

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<p>In this thesis we address the problem of how to construct an optimal algorithm for displaying banners (i.e advertisements shown on web sites). The optimization is based on the revenue each banner generates, with the aim of selecting those banners which maximize future total revenue. Banner optimality is of major importance in the e-commerce industry, in particular on web sites with heavy traffic. The 'micropayments' from showing banners add up to substantial profits due to the large volumes involved. We provide a broad, up-to-date and primarily theoretical treatment of this global optimizat
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Hatton, Marc. "Requirements specification for the optimisation function of an electric utility's energy flow simulator." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96956.

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Thesis (MEng)--Stellenbosch University, 2015.<br>ENGLISH ABSTRACT: Efficient and reliable energy generation capability is vital to any country's economic growth. Many strategic, tactical and operational decisions take place along the energy supply chain. Shortcomings in South Africa's electricity production industry have led to the development of an energy ow simulator. The energy ow simulator is claimed to incorporate all significant factors involved in the energy ow process from primary energy to end-use consumption. The energy ow simulator thus provides a decision support sys
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Al-Mharmah, Hisham. "Global optimization of stochastic functions." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25665.

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Sharikov, Evgenii. "Conditions for global minimum through abstract convexity." Thesis, University of Ballarat, 2008. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/45930.

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The theory of abstract convexity generalizes ideas of convex analysis by using the notion of global supports and the global definition of subdifferential. In order to apply this theory to optimization, we need to extend subdifferential calculus and separation properties into the area of abstract convexity.<br>Doctor of Philosophy
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Andramonov, Mikhail. "Global minimization of some classes of generalized convex functions." Thesis, Federation University Australia, 2001. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164850.

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Fowkes, Jaroslav Mrazek. "Bayesian numerical analysis : global optimization and other applications." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:ab268fe7-f757-459e-b1fe-a4a9083c1cba.

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We present a unifying framework for the global optimization of functions which are expensive to evaluate. The framework is based on a Bayesian interpretation of radial basis function interpolation which incorporates existing methods such as Kriging, Gaussian process regression and neural networks. This viewpoint enables the application of Bayesian decision theory to derive a sequential global optimization algorithm which can be extended to include existing algorithms of this type in the literature. By posing the optimization problem as a sequence of sampling decisions, we optimize a general co
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Selassie, Abebe Geletu W. "A coarse solution of generalized semi-infinite optimization problems via robust analysis of marginal functions and global optimization." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974862304.

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Sultanova, Nargiz. "A class of Increasing Positively Homogeneous functions for which global optimization problem is NP-hard." Thesis, University of Ballarat, 2009. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/44036.

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It is well known that global optimization problems are, generally speaking, computationally infeasible, that is solving them would require an unreasonably large amount of time and/or space. In certain cases, for example, when objective functions and constraints are convex, it is possible to construct a feasible algorithm for solving global optimization problem successfully. Convexity, however, is not a phenomenon to be often expected in the applications. Nonconvex problems frequently arise in many industrial and scienti¯c areas. Therefore, it is only natural to try to replace convexity with so
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Sultanova, Nargiz. "A class of Increasing Positively Homogeneous functions for which global optimization problem is NP-hard." University of Ballarat, 2009. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/16160.

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It is well known that global optimization problems are, generally speaking, computationally infeasible, that is solving them would require an unreasonably large amount of time and/or space. In certain cases, for example, when objective functions and constraints are convex, it is possible to construct a feasible algorithm for solving global optimization problem successfully. Convexity, however, is not a phenomenon to be often expected in the applications. Nonconvex problems frequently arise in many industrial and scienti¯c areas. Therefore, it is only natural to try to replace convexity with so
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Books on the topic "Global function optimization"

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1935-, Rockafellar R. Tyrrell, and SpringerLink (Online service), eds. Implicit functions and solution mappings: A view from variational analysis. Springer, 2009.

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Penot, Jean-Paul. Calculus Without Derivatives. Springer New York, 2013.

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Virginia, Torczon, and Langley Research Center, eds. A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds. National Aeronautics and Space Administration, Langley Research Center, 1998.

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Virginia, Torczon, and Langley Research Center, eds. A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds. National Aeronautics and Space Administration, Langley Research Center, 1998.

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1873-1950, Carathéodory Constantin, Hadjisavvas Nicolas 1953-, and Pardalos P. M. 1954-, eds. Advances in convex analysis and global optimization: Honoring the memory of C. Caratheodory (1873-1950). Kluwer Academic Publishers, 2001.

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service), SpringerLink (Online, ed. Conjugate Duality in Convex Optimization. Springer-Verlag Berlin Heidelberg, 2010.

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Çınlar, E. Real and Convex Analysis. Springer US, 2013.

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Drábek, Pavel. Methods of Nonlinear Analysis: Applications to Differential Equations. 2nd ed. Springer Basel, 2013.

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Bezhaev, Anatoly Yu. Variational Theory of Splines. Springer US, 2001.

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Ruzhansky, Michael. Evolution Equations of Hyperbolic and Schrödinger Type: Asymptotics, Estimates and Nonlinearities. Springer Basel, 2012.

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Book chapters on the topic "Global function optimization"

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Khamisov, Oleg V. "Objective Function Decomposition in Global Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69404-7_28.

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Verma, Anand, Shikha Agrawal, Jitendra Agrawal, and Sanjeev Sharma. "Advance Teaching–Learning Based Optimization for Global Function Optimization." In Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2538-6_59.

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Shang, You-Lin, Zhen-Yang Sun, and Xiang-Yi Jiang. "Modified Filled Function Method for Global Discrete Optimization." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08377-3_7.

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Locatelli, Marco. "Function Landscapes and the Difficulty of Global Optimization." In Computational Intelligence in Aerospace Sciences. American Institute of Aeronautics and Astronautics, Inc., 2014. http://dx.doi.org/10.2514/5.9781624102714.0047.0062.

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Gergel, Victor, and Alexander Sysoyev. "Global Optimization Method with Numerically Calculated Function Derivatives." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65739-0_1.

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Li, Sun, and Wang Yuncheng. "A Modified Cut-Peak Function Method for Global Optimization." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08377-3_6.

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Zheng, Fangying, and Liansheng Zhang. "Constrained Global Optimization Using a New Exact Penalty Function." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08377-3_8.

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Ziyi, Chen, and Kang Lishan. "Population Climbing Evolutionary Algorithm for Multimodal Function Global Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11903697_70.

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Chen, Ziyi, and Lishan Kang. "Steady-State Evolutionary Algorithm for Multimodal Function Global Optimization." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_29.

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Zhu, Wenxing. "A Dynamic Convexized Function with the Same Global Minimizers for Global Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_124.

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Conference papers on the topic "Global function optimization"

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Isshiki, Masaki. "Global optimization with escape function." In International Optical Design Conference, edited by Leo R. Gardner and Kevin P. Thompson. SPIE, 1998. http://dx.doi.org/10.1117/12.321984.

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Isshiki, Masaki. "Global optimization with escape function." In International Optical Design Conference. Optica Publishing Group, 1998. http://dx.doi.org/10.1364/iodc.1998.lmc.1.

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In the optimization of lens design, the damped least squares method (DLS) allows us to find a solution easily, but it corresponds to a local minimum of the merit function which happens to lie near the starting design. There must be many other solutions in the wider area, some of which can be much better than the first solution. When the design falls into a local minimum, it is almost impossible with DLS to get out of that trap to find other solutions, because the control of damping factor forces the design to remain within that trap.
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Chen, Zhong, Cai-Yun Liu, and Yi-Bing Lv. "Global Minimum of Measure Function and Quasi-convex Function." In 2010 Third International Joint Conference on Computational Science and Optimization. IEEE, 2010. http://dx.doi.org/10.1109/cso.2010.47.

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Huang, Zhiyong, Youlin Shang, and Fengye Wang. "Transformation Function Method for Unconstrained Global Optimization." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.75.

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Yanxia, Niu, and Zhao Hengjun. "Generalized Filled Function for Global Continuous Optimization Problems." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.87.

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Sakalauskas, Leonidas, and Jurgis Susinskas. "On bayes approach to univariate global optimization." In International Workshop of "Stochastic Programming for Implementation and Advanced Applications". The Association of Lithuanian Serials, 2012. http://dx.doi.org/10.5200/stoprog.2012.18.

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In this paper the Bayesian approach to global optimization of univariate continuous functions is developed, when the objective function is modelled by Ornstein-Uhlenbeck process. The parameters of model of function to be optimised are calibrated by maximal likelihood method using the learning set. The resulting optimization algorithm is rather simple and consists of reselection of values of expected step utility function, which maximizes at each step the expected increment of minimal observed value of the objective function. The convergence of method developed is studied by theoretical and exp
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Kou, Jiaxun, Teng Long, Zhu Wang, Yonglu Wen, and Li Liu. "A Deterministic Constrained Global Optimization Algorithm Without Penalty Function." In 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4296.

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Hendrix, E. M. T., J. M. G. Salmerón, and L. G. Casado. "On function monotonicity in simplicial branch and bound." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089974.

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Yang, Yongjian, and Yuelin Gao. "A new filled function method for global optimization." In 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251329.

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Zhang, L. S. "Advances in Global Optimization: Novel Function Transformation Approaches." In APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/apccas.2006.342064.

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Reports on the topic "Global function optimization"

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AL-Khayyal, Fais A., Reiner Horst, and Panos M. Pardalos. Global Optimization of Concave Functions Subject to Separable Quadratic Constraints and of All-Quadratic Separable Problems. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada197747.

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McElwain, Terry, Eugene Pipano, Guy Palmer, Varda Shkap, Stephen Hines, and Douglas Jasmer. Protection of Cattle Against Babesiosis: Immunization with Recombinant DNA Derived Apical Complex Antigens of Babesia bovis. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7612835.bard.

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Bovine babesiosis caused by Babesia bovis continues to be a significant deterrent to global livestock production. Current control methods have both biological and technical drawbacks that have stimulated research on improved methods of vaccination. This BARD project has focused on characterization of candidate Babesia bovis vaccine antigens located in the apical complex, a unique group of subcellular organelles - including rhoptries, micronemes, and spherical bodies - involved in the invation of erythrocytes. Spherical bodies and rhoptries were partially purified and their contents characteriz
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Rémy, Elisabeth, Romain Escudier, and Alexandre Mignot. Access impact of observations. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.8.

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The accuracy of the Copernicus Marine Environment and Monitoring Service (CMEMS) ocean analysis and forecasts highly depend on the availability and quality of observations to be assimilated. In situ observations are complementary to satellite observations that are restricted to the ocean surface. Higher resolution model forecasts are required by users of the CMEMS global and regional ocean analysis and forecasts. To support this with an efficient observational constrain of the model forecast via data assimilation, an increase observation coverage is needed, associated with an improved usage of
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