Gotowe bibliografie tematyczne / Global function optimization

Gotowa bibliografia na temat „Global function optimization”

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Data publikacji: 16 lipca 2024

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Artykuły w czasopismach na temat "Global function optimization"

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Liu, Y., i K. L. Teo. "A bridging method for global optimization". Journal of the Australian Mathematical Society. Series B. Applied Mathematics 41, nr 1 (lipiec 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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Zhu, Jinghao, Jiani Zhou i David Gao. "Global optimization by canonical dual function". Journal of Computational and Applied Mathematics 234, nr 2 (maj 2010): 538–44. http://dx.doi.org/10.1016/j.cam.2009.12.045.

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Norkin, Vladimir. "A Stochastic Smoothing Method for Nonsmooth Global Optimization". Cybernetics and Computer Technologies, nr 1 (31.03.2020): 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 large value of the smoothing parameter, the averaged function can have only one minimum. The smoothing method consists in replacing the original function with a sequence of smoothed approximations with vanishing to zero smoothing parameter and optimization of the latter functions by contemporary stochastic optimization methods. Passing from the minimum of one smoothed function to a close minimum of the next smoothed function, we can gradually come to the region of the global minimum of the original function. The smoothing method is also applicable for the optimization of nonsmooth nonconvex functions. It is shown that the smoothing method steadily solves test global optimization problems of small dimensions from the literature.
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Satapathy, Suresh Chandra. "Improved teaching learning based optimization for global function optimization". Decision Science Letters 2, nr 1 (1.01.2013): 23–34. http://dx.doi.org/10.5267/j.dsl.2012.10.005.

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Satapathy, Suresh Chandra, Anima Naik i K. Parvathi. "Weighted Teaching-Learning-Based Optimization for Global Function Optimization". Applied Mathematics 04, nr 03 (2013): 429–39. http://dx.doi.org/10.4236/am.2013.43064.

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Wang, Wei, Xiaoshan Zhang i 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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DJALIL, BOUDJEHEM, BOUDJEHEM BADREDDINE i BOUKAACHE ABDENOUR. "REDUCING DIMENSION IN GLOBAL OPTIMIZATION". International Journal of Computational Methods 08, nr 03 (wrzesień 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 global optimum in very restricted time based on a stochastic tests and an adaptive partition of the search space. Illustrative examples are presented to show the efficiency of the proposed idea. It was found that the algorithm was able to locate the global optimum even though the objective function has a large number of optima.
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Xiao, Jin-ke, Wei-min Li, Wei Li i Xin-rong Xiao. "Optimization on Black Box Function Optimization Problem". Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/647234.

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There are a large number of engineering optimization problems in real world, whose input-output relationships are vague and indistinct. Here, they are called black box function optimization problem (BBFOP). Then, inspired by the mechanism of neuroendocrine system regulating immune system, BP neural network modified immune optimization algorithm (NN-MIA) is proposed. NN-MIA consists of two phases: the first phase is training BP neural network with expected precision to confirm input-output relationship and the other phase is immune optimization phase, whose aim is to search global optima. BP neural network fitting without expected fitting precision could be replaced with polynomial fitting or other fitting methods within expected fitting precision. Experimental simulation confirms global optimization capability of MIA and the practical application of BBFOP optimization method.
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Mou-Yan Zou i Xi Zou. "Global optimization: an auxiliary cost function approach". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, nr 3 (maj 2000): 347–54. http://dx.doi.org/10.1109/3468.844358.

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Han, Qiaoming, i Jiye Han. "Revised filled function methods for global optimization". Applied Mathematics and Computation 119, nr 2-3 (kwiecień 2001): 217–28. http://dx.doi.org/10.1016/s0096-3003(99)00266-0.

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Rozprawy doktorskie na temat "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 function. A maximizer of this function is chosen to be the next point where the objective function is evaluated. Relations to similar optimization methods are established, and a general framework is presented that combines these methods and our methods. A large part of the dissertation is devoted to the convergence theory. We show that convergence can be achieved for most types of basis functions without further assumptions on the objective function. For other types, however, a similar results could not be obtained. This is due to the properties of the so-called native space that is associated with a basis function. In particular, it is of interest whether this space contains sufficiently smooth functions with compact support. In order to address this question, we present two approaches. First, we establish a characterization of the native space in terms of generalized Fourier transforms. For many types, for example thin plate splines, this helps to derive conditions on the smoothness of a function that guarantee that it is in the native space. For other types, for example multiquadrics, however, we show that the native space does not contain a nonzero function with compact support. The second approach we present gives slightly weaker results, but it employs some new theory using interpolation on an infinite regular grid.
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Bastani, Spencer, i 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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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 optimization problem. Through a synthesis of mathematical modeling, statistical methodology and computer science we construct a stochastic 'planning algorithm'. The superiority of our algorithm is based on empirical analysis conducted by us on real internet-data at TradeDoubler AB, as well as test-results on a selection of stylized data-sets. The algorithm is flexible and adapts well to new environments.

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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.
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 system for electric utility planners. The original aim of this study was to develop a global optimisation model and integrate it into the existing energy ow simulator. After gaining an understanding of the architecture of the energy ow simulator and scrutinising a large number of variables, it was concluded that global optimisation was infeasible. The energy ow simulator is made up of four modules and is operated on a module-by-module basis, with inputs and outputs owing between modules. One of the modules, namely the primary energy module, lends itself well to optimisation. The primary energy module simulates coal stockpile levels through Monte Carlo simulation. Classic inventory management policies were adapted to fit the structure of the primary energy module, which is treated as a black box. The coal stockpile management policies that are introduced provide a prescriptive means to deal with the stochastic nature of the coal stockpiles. As the planning horizon continuously changes and the entire energy ow simulator has to be re-run, an efficient algorithm is required to optimise stockpile management policies. Optimisation is achieved through the rapidly converging cross-entropy method. By integrating the simulation and optimisation model, a prescriptive capability is added to the primary energy module. Furthermore, this study shows that coal stockpile management policies can be improved. An integrated solution is developed by nesting the primary energy module within the optimisation model. Scalability is incorporated into the optimisation model through a coding approach that automatically adjusts to an everchanging planning horizon as well as the commission and decommission of power stations. As this study is the first of several research projects to come, it paves the way for future research on the energy ow simulator by proposing future areas of investigation.
AFRIKAANSE OPSOMMING: Effektiewe en betroubare energie-opwekkingsvermoë is van kardinale belang in enige land se ekonomiese groei. Baie strategiese, taktiese en operasionele besluite word deurgaans in die energie-verskaffingsketting geneem. Tekortkominge in Suid-Afrika se elektrisiteitsopwekkingsindustrie het tot die ontwikkeling van 'n energie-vloei-simuleerder gelei. Die energie-vloei-simuleerder vervat na bewering al die belangrike faktore wat op die energie-vloei-proses betrekking het van primêre energieverbruik tot eindgebruik. Die energie-vloei-simuleerder verskaf dus 'n ondersteuningstelsel aan elektrisiteitsdiensbeplanners vir die neem van besluite. Die oorspronklike doel van hierdie studie was om 'n globale optimeringsmodel te ontwikkel en te integreer in die bestaande energie-vloeisimuleerder. Na 'n begrip aangaande die argitektuur van die energievloei- simuleerder gevorm is en 'n groot aantal veranderlikes ondersoek is, is die slotsom bereik dat globale optimering nie lewensvatbaar is nie. Die energie-vloei-simuleerder bestaan uit vier eenhede en werk op 'n eenheid-tot-eenheid basis met insette en uitsette wat tussen eenhede vloei. Een van die eenhede, naamlik die primêre energiemodel, leen dit goed tot optimering. Die primêre energiemodel boots steenkoolreserwevlakke deur Monte Carlo-simulering na. Tradisionele voorraadbestuursbeleide is aangepas om die primêre energiemodel se struktuur wat as 'n swartboks hanteer word, te pas. Die steenkoolreserwebestuursbeleide wat ingestel is, verskaf 'n voorgeskrewe middel om met die stogastiese aard van die steenkoolreserwes te werk. Aangesien die beplanningshorison deurgaans verander en die hele energie-vloei-simulering weer met die energie-vloei-simuleerder uitgevoer moet word, word 'n effektiewe algoritme benodig om die re-serwebestuursbeleide te optimeer. Optimering word bereik deur die vinnige konvergerende kruis-entropie-metode. 'n Geïntegreerde oplossing is ontwikkel deur die primêre energiemodel en die optimering funksie saam te voeg. Skalering word ingesluit in die optimeringsmodel deur 'n koderingsbenadering wat outomaties aanpas tot 'n altyd-veranderende beplanningshorison asook die ingebruikneem en uitgebruikstel van kragstasies. Aangesien hierdie studie die eerste van verskeie navorsingsprojekte is, baan dit die weg vir toekomstige navorsing oor die energie-vloeisimuleerder deur ondersoekareas vir die toekoms voor te stel.
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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.
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 cost function at each stage of the algorithm. An extension to multi-stage decision processes is also discussed. The key idea of the framework is to replace the underlying expensive function by a cheap surrogate approximation. This enables the use of existing branch and bound techniques to globally optimize the cost function. We present a rigorous analysis of the canonical branch and bound algorithm in this setting as well as newly developed algorithms for other domains including convex sets. In particular, by making use of Lipschitz continuity of the surrogate approximation, we develop an entirely new algorithm based on overlapping balls. An application of the framework to the integration of expensive functions over rectangular domains and spherical surfaces in low dimensions is also considered. To assess performance of the framework, we apply it to canonical examples from the literature as well as an industrial model problem from oil reservoir simulation.
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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 some other structure at least for some classes of nonconvex optimization problems to render the global optimization problem feasible. A theory of abstract convexity has been developed as a result of the above considerations. Monotonic analysis, a branch of abstract convex analysis, is analogous in many ways to convex analysis, and sometimes is even simpler. It turned out that many problems of nonconvex optimization encountered in applications can be described in terms of monotonic functions. The analogies with convex analysis were considered to aid in solving some classes of nonconvex optimization problems. In this thesis we will focus on one of the elements of monotonic analysis - Increasing Positively Homogeneous functions of degree one or in short IPH functions. The aim of present research is to show that finding the solution and ²-approximation to the solution of the global optimization problem for IPH functions restricted to a unit simplex is an NP-hard problem. These results can be further extended to positively homogeneous functions of degree ´, ´ > 0.
Master of Mathematical Sciences (Research)
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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 some other structure at least for some classes of nonconvex optimization problems to render the global optimization problem feasible. A theory of abstract convexity has been developed as a result of the above considerations. Monotonic analysis, a branch of abstract convex analysis, is analogous in many ways to convex analysis, and sometimes is even simpler. It turned out that many problems of nonconvex optimization encountered in applications can be described in terms of monotonic functions. The analogies with convex analysis were considered to aid in solving some classes of nonconvex optimization problems. In this thesis we will focus on one of the elements of monotonic analysis - Increasing Positively Homogeneous functions of degree one or in short IPH functions. The aim of present research is to show that finding the solution and ²-approximation to the solution of the global optimization problem for IPH functions restricted to a unit simplex is an NP-hard problem. These results can be further extended to positively homogeneous functions of degree ´, ´ > 0.
Master of Mathematical Sciences (Research)
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Książki na temat "Global function optimization"

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Hoang, Tuy. Convex analysis and global optimization. Dordrecht: Kluwer Academic Publishers, 1998.

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

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Dontchev, A. L. Implicit functions and solution mappings: A view from variational analysis. Dordrecht: Springer, 2009.

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

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

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

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Boţ, Radu Ioan. Conjugate Duality in Convex Optimization. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Scholtes, Stefan. Introduction to Piecewise Differentiable Equations. New York, NY: Springer New York, 2012.

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Çınlar, E. Real and Convex Analysis. Boston, MA: Springer US, 2013.

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

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Części książek na temat "Global function optimization"

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Khamisov, Oleg V. "Objective Function Decomposition in Global Optimization". W Lecture Notes in Computer Science, 338–44. Cham: 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 i Sanjeev Sharma. "Advance Teaching–Learning Based Optimization for Global Function Optimization". W Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, 573–80. New Delhi: 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 i Xiang-Yi Jiang. "Modified Filled Function Method for Global Discrete Optimization". W Springer Proceedings in Mathematics & Statistics, 57–68. Cham: 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". W Computational Intelligence in Aerospace Sciences, 47–62. Reston, VA: American Institute of Aeronautics and Astronautics, Inc., 2014. http://dx.doi.org/10.2514/5.9781624102714.0047.0062.

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Gergel, Victor, i Alexander Sysoyev. "Global Optimization Method with Numerically Calculated Function Derivatives". W Communications in Computer and Information Science, 3–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65739-0_1.

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Li, Sun, i Wang Yuncheng. "A Modified Cut-Peak Function Method for Global Optimization". W Springer Proceedings in Mathematics & Statistics, 51–56. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08377-3_6.

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Zheng, Fangying, i Liansheng Zhang. "Constrained Global Optimization Using a New Exact Penalty Function". W Springer Proceedings in Mathematics & Statistics, 69–76. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08377-3_8.

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Ziyi, Chen, i Kang Lishan. "Population Climbing Evolutionary Algorithm for Multimodal Function Global Optimization". W Lecture Notes in Computer Science, 553–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11903697_70.

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Chen, Ziyi, i Lishan Kang. "Steady-State Evolutionary Algorithm for Multimodal Function Global Optimization". W Computational Intelligence and Security, 200–207. Berlin, Heidelberg: 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". W Lecture Notes in Computer Science, 939–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_124.

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Streszczenia konferencji na temat "Global function optimization"

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

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Isshiki, Masaki. "Global optimization with escape function". W International Optical Design Conference. Washington, D.C.: 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 i Yi-Bing Lv. "Global Minimum of Measure Function and Quasi-convex Function". W 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 i Fengye Wang. "Transformation Function Method for Unconstrained Global Optimization". W 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, i Zhao Hengjun. "Generalized Filled Function for Global Continuous Optimization Problems". W 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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Kou, Jiaxun, Teng Long, Zhu Wang, Yonglu Wen i Li Liu. "A Deterministic Constrained Global Optimization Algorithm Without Penalty Function". W 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia: 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 i L. G. Casado. "On function monotonicity in simplicial branch and bound". W PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089974.

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Sakalauskas, Leonidas, i Jurgis Susinskas. "On bayes approach to univariate global optimization". W 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 experimental way. Efficiency of the Bayes optimization method created is studied by computer simulation, too.
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Yang, Yongjian, i Yuelin Gao. "A new filled function method for global optimization". W 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". W 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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Raporty organizacyjne na temat "Global function optimization"

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

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McElwain, Terry, Eugene Pipano, Guy Palmer, Varda Shkap, Stephen Hines i Douglas Jasmer. Protection of Cattle Against Babesiosis: Immunization with Recombinant DNA Derived Apical Complex Antigens of Babesia bovis. United States Department of Agriculture, czerwiec 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 characterized using monoclonal antibodies. Existing and newly developed monoclonal antibodies bound to antigens in the spherical body, rhoptry, merozoite membrane, and infected erythrocyte membrane. In an initial immunization study using biologically cloned strains, it was demonstrated that strain-common epitopes are important for inducing immune protection against heterologous challenge. Rhoptry-associated antigen 1 (RAP-1) had been demonstrated previously to induce partial immune protection, fulfilled criteria of broad interstrain B and T cell epitope conservation, and thus was further characterized. The RAP-1 gene family consists of at least two gene copies, is homologous to the RAP-1 gene family in B. bigemina, and contains significant sequence similarity to other erythroparasitic protozoan candidate vaccine antigens, including the apical membrane antigen of Plasmodium falciparum. A new RAP-1 monoclonal antibody was developed that inhibits merozoite growth in vitro, demonstrating the presence of a RAP-1 neutralization sensitive domain. Based on these observations, cattle were immunized with Mo7 (Mexico) strain recombinant RAP-1 representing one of the two gene copies. All cattle responded with variable levels of serum antibodies inhibitory to heterologous Israel strain merozoite growth in vitro, and RAP-1 specific T lymphocytes that proliferated when stimulated with either homologous or heterologous native parasite antigen. Minimal protection from clinical disease was present after virulent Israel (heterologous) strain B. bovis challenge. In total, the results support the continued development of RAP-1 as a vaccine antigen, but indicate that additional information about the native structure and function of both RAP-1 gene copies, including the relationship of conserved and polymorphic sequences to B and T cell lepitopes relevant for protection, is necessary for optimization of RAP-1 as a vaccine component.
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Rémy, Elisabeth, Romain Escudier i 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 the available ocean observations. This work exploits the capabilities of operational systems to provide comprehensive information for the evolution of the GOOS. In this report, we analyse the use and the efficiency of the in-situ observations to constrain regional and global Mercator Ocean systems. Physical and biogeochemical variables are considered. The in-situ observations are used either to estimate physical ocean state at global and regional scale via data assimilation or to estimate BGC model parameters. The impact of the physical in situ observations assimilated in open ocean and coastal areas is assessed with numerical data assimilation experiments. The experiments are conducted with the regional 1/36° resolution and global 1/12° resolution systems operated by Mercator Ocean for the Copernicus Marine Service. For the global physical ocean, the focus is on the tropical ocean to better understand how the tropical mooring observations constrain the intraseasonal to daily variability and the complementarity with satellite observations and the deep ocean. The tropical moorings provide unique high frequency observations at different depth, but they are far away from each other, so part of the signal in the observation are decorrelated from one mooring to the others. It is only via an integrated approach, as data assimilation into a dynamical model and complementarity with other observing networks that those observations can efficiently constrain the different scales of variability of the tropical ocean circulation. As the satellite observations brings higher spatial resolution between the tropical moorings but for the ocean surface, we show that the tropical mooring and Argo profile data assimilation constrain the larger scale ocean thermohaline vertical structure (EuroSea D2.2; Gasparin et al., 2023). The representation of the high frequency signals observed at mooring location is also significantly improved in the model analysis compared to a non-assimilative simulation. The ocean below 2000 m depth is still largely under constrained as very few observations exist. Some deep ocean basins, as the Antarctic deep ocean, shows significant trend over the past decade but they are still not accurately monitored. Based on the spread of four deep ocean reanalysis estimates, large uncertainties were estimated in representing local heat and freshwater content in the deep ocean. Additionally, temperature and salinity field comparison with deep Argo observations demonstrates that reanalysis errors in the deep ocean are of the same size as or even stronger than the observed deep ocean signal. OSSE already suggested that the deployment of a global deep Argo array will significantly constrain the deep ocean in reanalysis to be closer to the observations (Gasparin et al., 2020). At regional and coastal scales, the physical ocean circulation is dominated by higher frequency, smaller scale processes than the open ocean which requires different observation strategy to be well monitor. The impact of assimilating high frequency and high-resolution observations provided by gliders on European shelves is analysed with the regional Iberic Biscay and Irish (IBI) system. It was found that repetitive glider sections can efficiently help to constrain the transport of water masses flowing across those sections. BGC ocean models are less mature than physical ocean models and some variable dependencies are still based on empirical functions. In this task, Argo BGC profile observations were used to optimize the parameters of the global CMEMS biogeochemical model, PISCES. A particle filter algorithm was chosen to optimize a 1D configuration of PISCES in the North Atlantic. The optimization of the PISCES 1D model significantly improves the model's ability to reproduce the North Atlantic bloom Recommendations on the in-situ network extensions for real time ocean monitoring are given based on those results, and the one also obtained in the WP2, Task 2.2 where data assimilation experiments but with simulated observations where conducted. Argo extension and the complementarity with satellite altimetry was also extensively studied. (EuroSea Deliverable ; D4.8)
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