標題: Titlebook: Conjugate Gradient Algorithms in Nonconvex Optimization; Rados?aw Pytlak Book 2009 Springer-Verlag Berlin Heidelberg 2009 Algebra.Bound Co [打印本頁] 作者: interminable 時間: 2025-3-21 18:27
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization影響因子(影響力)
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization影響因子(影響力)學科排名
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization網(wǎng)絡(luò)公開度
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization網(wǎng)絡(luò)公開度學科排名
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization被引頻次
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization被引頻次學科排名
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization年度引用
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization年度引用學科排名
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization讀者反饋
書目名稱Conjugate Gradient Algorithms in Nonconvex Optimization讀者反饋學科排名
作者: 努力趕上 時間: 2025-3-21 23:39
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 作者: 吸引人的花招 時間: 2025-3-22 02:17 作者: 狂怒 時間: 2025-3-22 08:08
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作者: NOMAD 時間: 2025-3-22 09:48 作者: liaison 時間: 2025-3-22 14:03
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作者: liaison 時間: 2025-3-22 19:37
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作者: SPASM 時間: 2025-3-22 23:59
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.作者: Increment 時間: 2025-3-23 04:19 作者: 講個故事逗他 時間: 2025-3-23 05:41
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.作者: 想象 時間: 2025-3-23 13:40
https://doi.org/10.1007/978-3-540-77817-2In this chapter we consider algorithms for the unconstrained minimization problem: ..作者: 施加 時間: 2025-3-23 17:39 作者: Munificent 時間: 2025-3-23 18:21
Trauma - An Engineering AnalysisIn the chapter we consider the problem . subject to the simple bounds ., where we assume that ., . are fixed vectors and the inequalities are taken componentwise. It is the special case of the problem considered in the previous chapter if we notice that the set . is a polyhedron.作者: persistence 時間: 2025-3-24 00:29 作者: Madrigal 時間: 2025-3-24 04:47
Limited Memory Quasi-Newton Algorithms,The memoryless quasi-Newton method stops short in creating an efficient compromise between a robust conjugate gradient algorithm and more efficient quasi-Newton method which uses limited storage.作者: 寬度 時間: 2025-3-24 09:02 作者: Conducive 時間: 2025-3-24 14:22
The Method of Shortest Residuals for Differentiable Problems,In this chapter we consider algorithms for the unconstrained minimization problem: ..作者: 健談的人 時間: 2025-3-24 15:24
Optimization on a Polyhedron,In the chapter our interest focuses on algorithms for problems with constraints. We consider the problem . where .. The set Ω is convex and is called the . [187].作者: 卵石 時間: 2025-3-24 20:00 作者: 中子 時間: 2025-3-25 01:29 作者: Capture 時間: 2025-3-25 04:41 作者: FATAL 時間: 2025-3-25 10:23 作者: Addictive 時間: 2025-3-25 14:52 作者: pus840 時間: 2025-3-25 17:50
978-3-642-09925-0Springer-Verlag Berlin Heidelberg 2009作者: 摻和 時間: 2025-3-25 23:29 作者: Increment 時間: 2025-3-26 03:52 作者: lipoatrophy 時間: 2025-3-26 07:31
Conjugate Direction Methods for Quadratic Problems,tion. Consider the problem of finding . ∈ . satisfying ., where . ∈ ., . ∈ . and . is symmetric positive definite. The solution to this problem is also a solution of the optimization problem (.): .. Consider the point x? such that .. We can show that (1.2) are the necessary optimality conditions for problem (1.1).作者: agenda 時間: 2025-3-26 10:43
Conjugate Gradient Methods for Nonconvex Problems,us chapter a conjugate gradient algorithm is an iterative process which requires at each iteration the current gradient and the previous direction. The simple scheme for calculating the current direction was easy to extend to a nonquadratic problem ..作者: 真實的你 時間: 2025-3-26 14:58 作者: nonplus 時間: 2025-3-26 19:28
https://doi.org/10.1007/3-540-36416-1inear Hestenes-Stiefel algorithm provided that the directional minimization is exact. Having that in mind and the fact that Hager and Zhang do not stipulate condition (2.68) in Theorem 2.14 their main convergence result is remarkable.作者: 否決 時間: 2025-3-26 22:45
Subcritical Solitons I: Saturable Absorber,rations comparable to the number of clusters. Preconditioning in the quadratic case significantly improves the efficiency of a conjugate gradient algorithm. In fact it transforms a conjugate gradient algorithm to a viable optimization technique widely used in several numerical algebra problems especially when problem’s dimension is large.作者: 搖曳的微光 時間: 2025-3-27 04:58 作者: gerontocracy 時間: 2025-3-27 06:01
Memoryless Quasi-Newton Methods,inear Hestenes-Stiefel algorithm provided that the directional minimization is exact. Having that in mind and the fact that Hager and Zhang do not stipulate condition (2.68) in Theorem 2.14 their main convergence result is remarkable.作者: 頭腦冷靜 時間: 2025-3-27 10:15 作者: 外來 時間: 2025-3-27 16:23 作者: anus928 時間: 2025-3-27 21:10
Book 2009ugate gradient algorithm perspective. ..Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and limited memory quasi-Newton algorithms are presented and numerically compared to standard conjugate gradient algorithms. ..The special attention is 作者: 吞下 時間: 2025-3-28 01:12
Book 2009ience. It can be used by researches in optimization, graduate students in operations research, engineering, mathematics and computer science. Practitioners can benefit from numerous numerical comparisons of professional optimization codes discussed in the book. .作者: Rebate 時間: 2025-3-28 05:37
1571-568X timization techniques are shown from a conjugate gradient algorithm perspective. ..Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and limited memory quasi-Newton algorithms are presented and numerically compared to standard conjugate gradi作者: Forage飼料 時間: 2025-3-28 07:26
Phase Domains and Phase Solitons,us chapter a conjugate gradient algorithm is an iterative process which requires at each iteration the current gradient and the previous direction. The simple scheme for calculating the current direction was easy to extend to a nonquadratic problem ..作者: 努力趕上 時間: 2025-3-28 14:30
Todd Shelly,Nancy Epsky,Roger Vargasnsformation the nonlinear problem is . to solve — eigenvalues of Hessian matrices of the objective function of the new optimization problem are more clustered (see Chap. 1 for the discussion of how eigenvalues clustering influences the behavior of conjugate gradient algorithms).作者: 感情 時間: 2025-3-28 18:16 作者: Addictive 時間: 2025-3-28 20:02 作者: 貨物 時間: 2025-3-28 23:19 作者: LAVA 時間: 2025-3-29 04:30
Preconditioned Conjugate Gradient Algorithms, 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作者: 花爭吵 時間: 2025-3-29 09:52 作者: Carbon-Monoxide 時間: 2025-3-29 12:07 作者: 間諜活動 時間: 2025-3-29 16:01 作者: INERT 時間: 2025-3-29 22:46 作者: APNEA 時間: 2025-3-30 03:23
