Stiefel流形上的黎曼共轭梯度法CSCD

1引言Stiefel流形上的优化问题一般形式可以表示为:min x∈S_(n,p) f(X)(1.1)其中目标函数f:R^(n×p)→R为连续可微函数,S_(n,p)表示Stiefel流形,即S_(n,p)={X∈R^(n×p):X^(T)X=Ip,p相似文献   

一种求解非线性无约束优化问题的充分下降的共轭梯度法

共轭梯度法是一类具有广泛应用的求解大规模无约束优化问题的方法. 提出了一种新的非线性共轭梯度(CG)法,理论分析显示新算法在多种线搜索条件下具有充分下降性. 进一步证明了新CG算法的全局收敛性定理. 最后,进行了大量数值实验,其结果表明与传统的几类CG方法相比,新算法具有更为高效的计算性能.     

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一个充分下降的有效共轭梯度法

对于大规模无约束优化问题,本文提出了一个充分下降的共轭梯度法公式,并建立相应的算法.该算法在不依赖于任何线搜索条件下,每步迭代都能产生一个充分下降方向.若采用标准Wolfe非精确线搜索求步长,则在常规假设条件下可获得算法良好的全局收敛性最后,对算法进行大规模数值试验,并采用Dolan和More的性能图对试验效果进行刻画,结果表明该算法是有效的.     

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本文我们得到了黎曼流形上一类非线性抛物方程的局部Hamilton梯度估计. 利用这个局部估计,我们得到了一个Harnack型不等式和一个Liouville型定理.     

具有充分下降性的两个共轭梯度法

对无约束优化问题,本文给出了两个改进的共轭梯度法公式.在不依赖于任何线搜索条件下,由新公式所产生的算法方向均是充分下降的,且在标准Wolfe非精确线搜索条件下,算法都具有全局收敛性.最后,对新算法进行大量的比对试验,数值结果表明所提方法是有效的.     

非线性约束条件下的梯度投影—可行方向法

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一族非线性约束条件下的摄动梯度投影法

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非线性不等式约束最优化问题一个新的广义既约梯度法

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On the Behavior of the Gradient Norm in the Steepest Descent Method

It is well known that the norm of the gradient may be unreliable as a stopping test in unconstrained optimization, and that it often exhibits oscillations in the course of the optimization. In this paper we present results descibing the properties of the gradient norm for the steepest descent method applied to quadratic objective functions. We also make some general observations that apply to nonlinear problems, relating the gradient norm, the objective function value, and the path generated by the iterates.     

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Runge-Kutta Type Methods Based on Geodesics for Systems of ODEs on the Stiefel Manifold

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In this paper,we study gradient estimates for the nonlinear heat equation ut-△u =au log u,on compact Riemannian manifold with or without boundary.We get a Hamilton type gradient estimate for the positi...     

Maximization of the sum of the trace ratio on the Stiefel manifold,I: Theory

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Homotopy exponents of Stiefel manifolds in the stable range

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无约束优化的一个充分下降共轭梯度算法

在修正PRP共轭梯度法的基础上,提出了求解无约束优化问题的一个充分下降共轭梯度算法,证明了算法在Wolfe线搜索下全局收敛,并用数值实验表明该算法具有较好的数值结果.