Titlebook: Conjugate Gradient Algorithms in Nonconvex Optimization; Radosław Pytlak Book 2009 Springer-Verlag Berlin Heidelberg 2009 Algebra.Bound Co

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书目名称Conjugate Gradient Algorithms in Nonconvex Optimization影响因子(影响力)




书目名称Conjugate Gradient Algorithms in Nonconvex Optimization影响因子(影响力)学科排名




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书目名称Conjugate Gradient Algorithms in Nonconvex Optimization网络公开度学科排名




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书目名称Conjugate Gradient Algorithms in Nonconvex Optimization被引频次学科排名




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书目名称Conjugate Gradient Algorithms in Nonconvex Optimization年度引用学科排名




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书目名称Conjugate Gradient Algorithms in Nonconvex Optimization读者反馈学科排名

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1571-568X mathematics and computer science. Practitioners can benefit from numerous numerical comparisons of professional optimization codes discussed in the book. .978-3-642-09925-0978-3-540-85634-4Series ISSN 1571-568X

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Phase Domains and Phase Solitons,iable problems were proposed. These propositions relied on the simplicity of their counterparts for quadratic problems. As we have shown in the previous chapter a conjugate gradient algorithm is an iterative process which requires at each iteration the current gradient and the previous direction. Th

NOMAD

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Subcritical Solitons I: Saturable Absorber, preconditioned conjugate gradient algorithms by others. The purpose of scaling in methods applied to quadratics is to transform eigenvalues of the Hessian matrix. Theorem 1.11 suggests that if eigenvalues are clustered then a conjugate gradient algorithm minimizes the quadratic in the number of ite

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Todd Shelly,Nancy Epsky,Roger Vargason. The idea behind preconditioned conjugate gradient algorithm is to transform the decision vector by linear transformation . such that after the transformation the nonlinear problem is . to solve — eigenvalues of Hessian matrices of the objective function of the new optimization problem are more c

SPASM

https://doi.org/10.1007/978-3-540-36308-8duals which uses the projection operator to cope with box constraints is competitive to the benchmark code L-BFGS-B in terms of CPU time (cf. Figs. 10.1, 10.2, 10.4, 10.6). For larger problems it is almost as efficient as L-BFGS-B program also in terms of the number of function evaluations (cf. Fig.

Increment

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Fundamental tests with trapped antiprotons,The method of shortest residuals is briefly discussed in Chap. 1. We show there that the method differs from a standard conjugate gradient algorithm only by scaling factors applied to conjugate directions. This is true when problems with quadratics are considered. However, these methods are quite different if applied to nonconvex functions.