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

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书目名称Advances in Convex Analysis and Global Optimization影响因子(影响力)




书目名称Advances in Convex Analysis and Global Optimization影响因子(影响力)学科排名




书目名称Advances in Convex Analysis and Global Optimization网络公开度




书目名称Advances in Convex Analysis and Global Optimization网络公开度学科排名




书目名称Advances in Convex Analysis and Global Optimization被引频次




书目名称Advances in Convex Analysis and Global Optimization被引频次学科排名




书目名称Advances in Convex Analysis and Global Optimization年度引用




书目名称Advances in Convex Analysis and Global Optimization年度引用学科排名




书目名称Advances in Convex Analysis and Global Optimization读者反馈




书目名称Advances in Convex Analysis and Global Optimization读者反馈学科排名

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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

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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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