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P0-函数箱约束变分不等式的正则半光滑牛顿法

**总被引：8，自引：0，他引：8**1引言设X C R~n,F：R~n→R~n,变分不等式Ⅵ(X,F)是指：求x∈X,使F(x)~T(y-x)≥0,(?)_y∈X．(1)记i∈N={1,2,…,n},当X=[a,b]：={x∈(?)~n|a_i≤x_i≤b_i,i∈N}时,称Ⅵ(X,F)为箱约束变分不等式(也有些文献称为混合互补问题),记为Ⅵ(a,b,F)．若a_i=0,b_i= ∞,i∈N,即X=(?)_ ~n：={x∈(?)~n|x≥0}时,Ⅵ(a,b,F)化为非线性互补问题NCP(F)：求x∈(?)_ ~n,使x≥0,F(x)≥0,x~TF(x)=0．(2) 相似文献

2.

Dong-Hui Li Masao Fukushima Liqun Qi Nobuo Yamashita 《Computational Optimization and Applications》2004,28(2):131-147

This paper studies convergence properties of regularized Newton methods for minimizing a convex function whose Hessian matrix may be singular everywhere. We show that if the objective function is LC

^{2}, then the methods possess local quadratic convergence under a local error bound condition without the requirement of isolated nonsingular solutions. By using a backtracking line search, we globalize an inexact regularized Newton method. We show that the unit stepsize is accepted eventually. Limited numerical experiments are presented, which show the practical advantage of the method. 相似文献3.

We consider the inverse problem to determine the shape of a open cavity embedded in the infinite ground plane from knowledge of the far-field pattern of the scattering of TM polarization.For its approximate solution we propose a regularized Newton iteration scheme.For a foundation of Newton type methods we establish the Fréchet differentiability of solution to the scattering problem with respect to the boundary of the cavity.Some numerical examples of the feasibility of the method are presented. 相似文献

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In this paper, a regularization Newton method for mixed complementarity problem(MCP) based on the reformulation of MCP in [1] is proposed. Its global conver-gence is proved under the assumption that F is a Po-function. The main feature of our algorithm is that a priori of the existence of an accumulation point for convergence need not to be assumed. 相似文献

6.

A. S. Antipin F. P. Vasil’ev A. S. Stukalov 《Computational Mathematics and Mathematical Physics》2007,47(1):19-31

Unstable equilibrium problems are examined in which the objective function and the set where the equilibrium point is sought are specified inexactly. A regularized Newton method, combined with penalty functions, is proposed for solving such problems, and its convergence is analyzed. A regularizing operator is constructed. 相似文献

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Image restoration is often solved by minimizing an energy function consisting of a data-fidelity term and a regularization term. A regularized convex term can usually preserve the image edges well in the restored image. In this paper, we consider a class of convex and edge-preserving regularization functions, I.e., multiplicative half-quadratic regularizations, and we use the Newton method to solve the correspondingly reduced systems of nonlinear equations. At each Newton iterate, the preconditioned conjugate gradient method, incorporated with a constraint preconditioner, is employed to solve the structured Newton equation that has a symmetric positive definite coefficient matrix.The igenvalue bounds of the preconditioned matrix are deliberately derived, which can be used to estimate the convergence speed of the preconditioned conjugate gradient method. We use experimental results to demonstrate that this new approach is efficient,and the effect of image restoration is r0easonably well. 相似文献

8.

Recently, Li et al. (Comput. Optim. Appl. 26:131–147, 2004) proposed a regularized Newton method for convex minimization problems. The method retains local quadratic convergence property
without requirement of the singularity of the Hessian. In this paper, we develop a truncated regularized Newton method and
show its global convergence. We also establish a local quadratic convergence theorem for the truncated method under the same
conditions as those in Li et al. (Comput. Optim. Appl. 26:131–147, 2004). At last, we test the proposed method through numerical experiments and compare its performance with the regularized Newton
method. The results show that the truncated method outperforms the regularized Newton method.
The work was supported by the 973 project granted 2004CB719402 and the NSF project of China granted 10471036. 相似文献

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