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A Globally Convergent Polak-Ribiere-Polyak Conjugate Gradient Method with Armijo-Type Line Search 被引量:11
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作者 Gaohang Yu Lutai Guan zengxin wei 《Numerical Mathematics A Journal of Chinese Universities(English Series)》 SCIE 2006年第4期357-366,共10页
In this paper, we propose a globally convergent Polak-Ribiere-Polyak (PRP) conjugate gradient method for nonconvex minimization of differentiable functions by employing an Armijo-type line search which is simpler and ... In this paper, we propose a globally convergent Polak-Ribiere-Polyak (PRP) conjugate gradient method for nonconvex minimization of differentiable functions by employing an Armijo-type line search which is simpler and less demanding than those defined in [4,10]. A favorite property of this method is that we can choose the initial stepsize as the one-dimensional minimizer of a quadratic modelΦ(t):= f(xk)+tgkTdk+(1/2) t2dkTQkdk, where Qk is a positive definite matrix that carries some second order information of the objective function f. So, this line search may make the stepsize tk more easily accepted. Preliminary numerical results show that this method is efficient. 展开更多
关键词 非约束最优化 共轭梯度法 整体收敛 可微函数
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A Line Search Algorithm for Unconstrained Optimization 被引量:1
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作者 Gonglin Yuan Sha Lu zengxin wei 《Journal of Software Engineering and Applications》 2010年第5期503-509,共7页
It is well known that the line search methods play a very important role for optimization problems. In this paper a new line search method is proposed for solving unconstrained optimization. Under weak conditions, thi... It is well known that the line search methods play a very important role for optimization problems. In this paper a new line search method is proposed for solving unconstrained optimization. Under weak conditions, this method possesses global convergence and R-linear convergence for nonconvex function and convex function, respectively. Moreover, the given search direction has sufficiently descent property and belongs to a trust region without carrying out any line search rule. Numerical results show that the new method is effective. 展开更多
关键词 LINE SEARCH UNCONSTRAINED Optimization Global CONVERGENCE R-linear CONVERGENCE
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A Rank-One Fitting Method with Descent Direction for Solving Symmetric Nonlinear Equations
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作者 Gonglin YUAN Zhongxing WANG zengxin wei 《International Journal of Communications, Network and System Sciences》 2009年第6期555-561,共7页
In this paper, a rank-one updated method for solving symmetric nonlinear equations is proposed. This method possesses some features: 1) The updated matrix is positive definite whatever line search technique is used;2)... In this paper, a rank-one updated method for solving symmetric nonlinear equations is proposed. This method possesses some features: 1) The updated matrix is positive definite whatever line search technique is used;2) The search direction is descent for the norm function;3) The global convergence of the given method is established under reasonable conditions. Numerical results show that the presented method is interesting. 展开更多
关键词 Rank-One UPDATE Global CONVERGENCE Nonlinear EQUATIONS DESCENT Direction
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THE CONVERGENCE OF A NEW MODIFIED BFGS METHOD WITHOUT LINE SEARCHES FOR UNCONSTRAINED OPTIMIZATION OR COMPLEXITY SYSTEMS
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作者 Liying LIU zengxin wei Xiaoping WU 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2010年第4期861-872,共12页
In this paper,a new modified BFGS method without line searches is proposed.Unlike traditionalBFGS method,this modified BFGS method is proposed based on the so-called fixed steplengthstrategy introduced by Sun and Zhan... In this paper,a new modified BFGS method without line searches is proposed.Unlike traditionalBFGS method,this modified BFGS method is proposed based on the so-called fixed steplengthstrategy introduced by Sun and Zhang.Under some suitable assumptions,the global convergence andthe superlinear convergence of the new algorithm are established,respectively.And some preliminarynumerical experiments,which shows that the new Algorithm is feasible,is also reported. 展开更多
关键词 BFGS方法 全局收敛性 线搜索 无约束 修改 系统 优化 超线性收敛
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