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本文研究约束最优化锥模型拟牛顿依赖域方法的全局收敛性。文章给出了确保这类方法全局收敛的条件。 相似文献
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基于锥模型的一般信赖域算法收敛性分析 总被引:8,自引:0,他引:8
本文给出了锥模型信赖域算法的一般模型,它不仅包含通常的信赖域算法一相当于锥模型算法中bk=0的情形,而且文献[1]的算法也可看作其子类.我们研究这个模型的较强的全局收敛性,并讨论保证算法具有超线性收敛速率的条件,从而推广了文[1]和文[4]中的若干结果. 相似文献
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信赖域方法是求解非线性方程组的一种重要方法.本文研究了求解非线性方程组的信赖域半径趋于零的信赖域算法在Jacobi矩阵Hölderian连续条件下的全局收敛性质,以及其在Hölderian局部误差界和Jacobi矩阵Hölderian连续条件下的收敛速度. 相似文献
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基于非单调自适应信赖域法求解非线性方程组 总被引:1,自引:0,他引:1
本文提出了求解非线性方程组的非单调自适应信赖域法.在适当的条件下证明了非单调自适应信赖域法的局部及全局收敛性质.基本的数值实验表明该方法在处理某些非线性方程组是非常有效的. 相似文献
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本文提出了一个解线性等式约束优化问题的新锥模型信赖域方法.论文采用零空间技术消除了新锥模型子问题中的线性等式约束,用折线法求解转换后的子问题,并给出了解线性等式约束优化问题的信赖域方法.论文提出并证明了该方法的全局收敛性,并给出了该方法解线性等式约束优化问题的数值实验.理论和数值实验结果表明新锥模型信赖域方法是有效的,这给出了用新锥模型进一步研究非线性优化的基础. 相似文献
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NGLM:一类全局收敛的Newton-GMRES方法 总被引:6,自引:1,他引:5
本文提出了一类具有全局收敛性质的Newton-GMRES方法—NGLM方法.该方法是对经典Newton—GMRES方法的推广.NGLM方法的全局策略是当在非精确Newton方向上后退不能成功时,转而在一个子空间上运用信赖域方法确定迭代步长.理论分析与数值实验均表明,NGLM方法改善了Newton—GMRES方法的强健性. 相似文献
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本文提出了一种解无约束优化问题的新的非单调自适应信赖域方法.这种方法借助于目标函数的海赛矩阵的近似数量矩阵来确定信赖域半径.在通常的条件下,给出了新算法的全局收敛性以及局部超线性收敛的结果,数值试验验证了新的非单调方法的有效性. 相似文献
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结合非单调信赖域方法,和非单调线搜索技术,提出了一种新的无约束优化算法.信赖域方法的每一步采用线搜索,使得迭代每一步都充分下降加快了迭代速度.在一定条件下,证明了算法具有全局收敛性和局部超线性.收敛速度.数值试验表明算法是十分有效的. 相似文献
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In this paper, we propose a model-hybrid approach for nonlinear optimization that employs both trust region method and quasi-Newton method, which can avoid possibly resolve the trust region subproblem if the trial step is not acceptable. In particular, unlike the traditional trust region methods, the new approach does not use a single approximate model from beginning to the end, but instead employs quadratic model or conic model at every iteration adaptively. We show that the new algorithm preserves the strong convergence properties of trust region methods. Numerical results are also presented. 相似文献
13.
Wen-yuSun Jin-yunYuan Ya-xiangYuan 《计算数学(英文版)》2003,21(3):295-304
In this paper we present a trust region method of conic model for linearly constrained optimization problems.We discuss trust region approaches with conic model subproblems.Some equivalent variation properties and optimality conditions are given.A trust region algorithm based on conic model is constructed.Global convergence of the method is established. 相似文献
14.
Lijuan ZHAO Wenyu SUN Raimundo J. B. de SAMPAIO 《Frontiers of Mathematics in China》2014,9(5):1211-1238
We propose a nonmonotone adaptive trust region method based on simple conic model for unconstrained optimization. Unlike traditional trust region methods, the subproblem in our method is a simple conic model, where the Hessian of the objective function is approximated by a scalar matrix. The trust region radius is adjusted with a new self-adaptive adjustment strategy which makes use of the information of the previous iteration and current iteration. The new method needs less memory and computational efforts. The global convergence and Q-superlinear convergence of the algorithm are established under the mild conditions. Numerical results on a series of standard test problems are reported to show that the new method is effective and attractive for large scale unconstrained optimization problems. 相似文献
15.
信赖域法是一种保证全局收敛性的优化算法,为避免Hessian矩阵的计算,基于拟牛顿校正公式构造了求解带线性等式约束的非线性规划问题的截断拟牛顿型信赖域法.首先给出了截断拟牛顿型信赖域法的构造过程及具体步骤;然后针对随机用户均衡模型中变量和约束的特点对算法进行了修正,并将多种拟牛顿校正公式下所得结果与牛顿型信赖域法的结果进行了比较,结果发现基于对称秩1校正公式的信赖域法更为合适.最后基于数值算例结果得到了一些在算法编程过程中的重要结论,对其它形式信赖域法的编程实现具有一定的参考意义. 相似文献
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《高等学校计算数学学报》2016,(2)
The trust region(TR) method for optimization is a class of effective methods.The conic model can be regarded as a generalized quadratic model and it possesses the good convergence properties of the quadratic model near the minimizer.The Barzilai and Borwein(BB) gradient method is also an effective method,it can be used for solving large scale optimization problems to avoid the expensive computation and storage of matrices.In addition,the BB stepsize is easy to determine without large computational efforts.In this paper,based on the conic trust region framework,we employ the generalized BB stepsize,and propose a new nonmonotone adaptive trust region method based on simple conic model for large scale unconstrained optimization.Unlike traditional conic model,the Hessian approximation is an scalar matrix based on the generalized BB stepsize,which resulting a simple conic model.By adding the nonmonotone technique and adaptive technique to the simple conic model,the new method needs less storage location and converges faster.The global convergence of the algorithm is established under certain conditions.Numerical results indicate that the new method is effective and attractive for large scale unconstrained optimization problems. 相似文献
17.
Jinyan Fan 《Computational Optimization and Applications》2006,34(2):215-227
In this paper, we present the new trust region method for nonlinear equations with the trust region converging to zero. The
new method preserves the global convergence of the traditional trust region methods in which the trust region radius will
be larger than a positive constant. We study the convergence rate of the new method under the local error bound condition
which is weaker than the nonsingularity. An example given by Y.X. Yuan shows that the convergence rate can not be quadratic.
Finally, some numerical results are given.
This work is supported by Chinese NSFC grants 10401023 and 10371076, Research Grants for Young Teachers of Shanghai Jiao Tong
University, and E-Institute of Computational Sciences of Shanghai Universities.
An erratum to this article is available at . 相似文献
18.
In this paper we present a nonmonotone trust region method for nonlinear least squares problems with zero-residual and prove its convergence properties. The extensive numerical results are reported which show that the nonmonotone trust region method is generally superior to the usual trust region method. 相似文献
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In this paper, based on a simple model of trust region sub-problem, we combine the trust region method with the non-monotone and self-adaptive techniques to propose a new non-monotone self-adaptive trust region algorithm for unconstrained optimization. By use of the simple model, the new method needs less memory capacitance, computational complexity and CPU time. The convergence results of the method are proved under certain conditions. Numerical results show that the new method is effective and attractive for large-scale optimization problems. 相似文献
20.
In this paper, we propose a new trust region method for unconstrained optimization problems. The new trust region method can automatically adjust the trust region radius of related subproblems at each iteration and has strong global convergence under some mild conditions. We also analyze the global linear convergence, local superlinear and quadratic convergence rate of the new method. Numerical results show that the new trust region method is available and efficient in practical computation. 相似文献