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Titlebook: Advances in Convex Analysis and Global Optimization; Honoring the Memory Nicolas Hadjisavvas,Panos M. Pardalos Book 20011st edition Kluwer

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發(fā)表于 2025-3-21 17:52:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Convex Analysis and Global Optimization
期刊簡稱Honoring the Memory
影響因子2023Nicolas Hadjisavvas,Panos M. Pardalos
視頻videohttp://file.papertrans.cn/148/147380/147380.mp4
學(xué)科分類Nonconvex Optimization and Its Applications
圖書封面Titlebook: Advances in Convex Analysis and Global Optimization; Honoring the Memory  Nicolas Hadjisavvas,Panos M. Pardalos Book 20011st edition Kluwer
影響因子There has been much recent progress in global optimization algo- rithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. Convex analysis plays a fun- damental role in the analysis and development of global optimization algorithms. This is due essentially to the fact that virtually all noncon- vex optimization problems can be described using differences of convex functions and differences of convex sets. A conference on Convex Analysis and Global Optimization was held during June 5 -9, 2000 at Pythagorion, Samos, Greece. The conference was honoring the memory of C. Caratheodory (1873-1950) and was en- dorsed by the Mathematical Programming Society (MPS) and by the Society for Industrial and Applied Mathematics (SIAM) Activity Group in Optimization. The conference was sponsored by the European Union (through the EPEAEK program), the Department of Mathematics of the Aegean University and the Center for Applied Optimization of the University of Florida, by the General Secretariat of Research and Tech- nology of Greece, by the Ministry of Education of Greece, and several local Greek government agencies and companies. This volume contain
Pindex Book 20011st edition
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Undergraduate Texts in Mathematicsowever, the state constraint is inactive. Unlike the penalization methods previously employed, this gives rise to a new problem with fewer admissible trajectories whose value approximates the original one from above rather than below. The techniques of nonsmooth analysis play the essential role in t
地板
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Undergraduate Texts in Mathematicsimental data etc., can be described by mathematical models which require the solution of the problem of separation of two or more sets. In many cases the sets mentioned are inseparable, and the problem arises to “separate” them in the “best” possible way (e.g., to identify as many points as possible
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https://doi.org/10.1007/978-3-319-77637-8ations research to computational chemistry and molecular biology. In this chapter we explore the application of deterministic global optimization approaches to problems related to protein structure prediction. Due to the complex nature of protein interactions, energy landscapes which model these sys
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https://doi.org/10.1007/978-3-319-77637-8ed by M. Goemans and D.P. Williamson. As observed by S. Poljak, F. Rendl and H. Wolkowicz, this technique can be interpreted from the point of view of Lagrangian duality. A central tool for this is dualization of quadratic constraints, an operation pioneered by N.Z. Shor. We synthesize these various
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Variational Problems with Constraints,ave their origin there. Conceptual developments in contemporary convex analysis have in turn enriched that venerable subject by making it possible to treat a vastly larger class of problems effectively in a “neoclassical” framework of extended-real-valued functions and their subgradients.
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Variational Problems with Constraints,ng problem), and the docking of a ligand on a protein (drug design). Both of these problems require the computation of the global minimum (or near global minimum) of a differentiable function with many local minima. The Convex Global Underestimator (CGU) algorithm for solving this type of problem ha
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