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Titlebook: Optimization and Control with Applications; Liqun Qi,Koklay Teo,Xiaoqi Yang Conference proceedings 2005 Springer-Verlag US 2005 algorithm.

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發(fā)表于 2025-3-21 16:30:09 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Optimization and Control with Applications
編輯Liqun Qi,Koklay Teo,Xiaoqi Yang
視頻videohttp://file.papertrans.cn/704/703200/703200.mp4
叢書名稱Applied Optimization
圖書封面Titlebook: Optimization and Control with Applications;  Liqun Qi,Koklay Teo,Xiaoqi Yang Conference proceedings 2005 Springer-Verlag US 2005 algorithm.
描述This book contains refereed papers which were presented at the 34th Workshop of the International School of Mathematics "G. Stampacchia,” the International Workshop on Optimization and Control with Applications. The book contains 28 papers that are grouped according to four broad topics: duality and optimality conditions, optimization algorithms, optimal control, and variational inequality and equilibrium problems. The specific topics covered in the individual chapters include optimal control, unconstrained and constrained optimization, complementarity and variational inequalities, equilibrium problems, semi-definite programs, semi-infinite programs, matrix functions and equations, nonsmooth optimization, generalized convexity and generalized monotinicity, and their applications. ..?.Audience..This book is suitable for researchers, practitioners, and postgraduate students in optimization, operations research, and optimal control. .
出版日期Conference proceedings 2005
關(guān)鍵詞algorithm; algorithms; calculus; dynamical system; dynamical systems; dynamische Systeme; global optimizat
版次1
doihttps://doi.org/10.1007/b104943
isbn_softcover978-1-4419-3709-4
isbn_ebook978-0-387-24255-2Series ISSN 1384-6485
issn_series 1384-6485
copyrightSpringer-Verlag US 2005
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發(fā)表于 2025-3-21 22:59:46 | 只看該作者
The Modified Subgraident Method for Equality Constrained Nonconvex Optimization Problemsteria. We do not use any convexity and differentiability conditions, and show that the sequence of the values of dual function converges to the optimal value. Finally, we demonstrate the presented method on numerical examples.
板凳
發(fā)表于 2025-3-22 02:14:45 | 只看該作者
Inexact Restoration Methods for Nonlinear Programming: Advances and Perspectivesegions are centered not in the current point but in the inexactly restored intermediate one. Global convergence has been proved, based on merit functions of augmented Lagrangian type. In this survey we point out some applications and we relate recent advances in the theory.
地板
發(fā)表于 2025-3-22 07:51:04 | 只看該作者
1384-6485 e International Workshop on Optimization and Control with Applications. The book contains 28 papers that are grouped according to four broad topics: duality and optimality conditions, optimization algorithms, optimal control, and variational inequality and equilibrium problems. The specific topics c
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發(fā)表于 2025-3-22 11:57:59 | 只看該作者
A Comparison of Two Approaches to Second-Order Subdifferentiability Concepts with Application to Optn directions, using the exposed facets of a convex set of symmetric matrices. This allows us to make a comparison between some optimality conditions. A nonsmooth formulation of a standard smooth mathematical programming problem is used to derive a novel set of sufficient optimality conditions.
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發(fā)表于 2025-3-22 16:47:13 | 只看該作者
An Analysis of the Barzilai and Borwein Gradient Method for Unsymmetric Linear Equationsi and Borwein gradient method for two unsymmetric linear equations with only two variables. Under mild conditions, we prove that the convergence rate of the Barzilai and Borwein gradient method is .-superlinear if the coefficient matrix . has the same eigenvalue; if the eigenvalues of . are different, then the convergence rate is .-superlinear.
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發(fā)表于 2025-3-22 21:08:13 | 只看該作者
On Minimization of Max-Min Functionsss the application of the cutting angle and discrete gradient methods as well as a special penalization method for solving constrained problems. We report the results of preliminary numerical experiments.
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發(fā)表于 2025-3-23 03:34:06 | 只看該作者
An Implementation of Training Dual-nu Support Vector Machinesod for training this class of SVM is described in this chapter. The training is divided into the initialisation process and the optimisation process, with both processes using similar iterative techniques. Implementation issues, such as caching, which reduces the memory usage and redundant kernel calculations are discussed.
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