共查询到20条相似文献,搜索用时 187 毫秒
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交替方向法是求解可分离结构变分不等式问题的经典方法之一, 它将一个大型的变分不等式问题分解成若干个小规模的变分不等式问题进行迭代求解. 但每步迭代过程中求解的子问题仍然摆脱不了求解变分不等式子问题的瓶颈. 从数值计算上来说, 求解一个变分不等式并不是一件容易的事情.因此, 本文提出一种新的交替方向法, 每步迭代只需要求解一个变分不等式子问题和一个强单调的非线性方程组子问题. 相对变分不等式问题而言, 我们更容易、且有更多的有效算法求解一个非线性方程组问题. 在与经典的交替方向法相同的假设条件下, 我们证明了新算法的全局收敛性. 进一步的数值试验也验证了新算法的有效性. 相似文献
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交替方向法适合于求解大规模问题.该文对于一类变分不等式提出了一种新的交替方向法.在每步迭代计算中,新方法提出了易于计算的子问题,该子问题由强单调的线性变分不等式和良态的非线性方程系统构成.基于子问题的精确求解,该文证明了算法的收敛性.进一步,又提出了一类非精确交替方向法,每步迭代计算只需非精确求解子问题.在一定的非精确条件下,算法的收敛性得以证明. 相似文献
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荣祯 《应用泛函分析学报》2012,14(1):109-112
给出了求解单调变分不等式的两类迭代算法.通过解强单调变分不等式子问题,产生两个迭代点列,都弱收敛到变分不等式的解.最后,给出了这两类新算法的收敛性分析. 相似文献
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对于一类特殊的变分不等式,提出一种新的交替方向方法.与通常的交替方向方法相比,该方法计算量更小.在函数强迫单调的条件下证明了算法的全局收敛性. 相似文献
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《应用数学学报》2020,(4)
本文提出了求解单调包含问题的一类新的惯性混合非精确邻近点算法(简记为iHIPPA)在适当的参数假设下,我们证明了求解单调包含问题的iHIPPA所产生点列的弱收敛性,获得了iHIPPA的非渐近收敛率为■及iHIPPA的遍历迭代复杂性为O(1/k).作为应用,我们还建立了求解单调变分包含问题的惯性邻近收缩算法,求解广义变分不等式问题的惯性投影邻近点算法,及求解原始一对偶问题的惯性非精确调比部分逆算法产生点列的收敛性及相应算法的非渐近收敛率及遍历迭代复杂性.本文结果推广和改进了文献中的相应结论.最后,本文应用新的惯性交替方向乘子法用以求解LASSO问题,而且一些初步的试验结果表明了新的算法的优越性. 相似文献
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2020年Liu和Yang提出了求解Hilbert空间中拟单调且Lipschitz连续的变分不等式问题的投影算法,简称LYA。本文在欧氏空间中提出了一种新的求解拟单调变分不等式的压缩投影算法,简称NPCA。新算法削弱了LYA中映射的Lipschitz连续性。在映射连续、拟单调且对偶变分不等式解集非空的条件下得到了NPCA所生成点列的聚点是解的结论。当变分不等式的解集还满足一定条件时,得到了NPCA的全局收敛性。数值实验结果表明NPCA所需的迭代步数少于LYA的迭代步数,NPCA在高维拟单调例子中所需的计算机耗时也更少。 相似文献
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We presented a new logarithmic-quadratic proximal alternating direction scheme for the separable constrained convex programming problem. The predictor is obtained by solving series of related systems of non-linear equations in a parallel wise. The new iterate is obtained by searching the optimal step size along a new descent direction. The new direction is obtained by the linear combination of two descent directions. Global convergence of the proposed method is proved under certain assumptions. We show the O(1 / t) convergence rate for the parallel LQP alternating direction method. 相似文献
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Xiaoming Yuan 《Computational Optimization and Applications》2011,49(1):17-29
To solve a class of variational inequalities with separable structure, this paper presents a new method to improve the proximal
alternating direction method (PADM) in the following senses: an iterate generated by the PADM is utilized to generate a descent
direction; and an appropriate step size along this descent direction is identified. Hence, a descent-like method is developed.
Convergence of the new method is proved under mild assumptions. Some numerical results demonstrate that the new method is
efficient. 相似文献
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This paper proposes a descent method to solve a class of structured monotone variational inequalities. The descent directions are constructed from the iterates generated by a prediction-correction method [B.S. He, Y. Xu, X.M. Yuan, A logarithmic-quadratic proximal prediction-correction method for structured monotone variational inequalities, Comput. Optim. Appl. 35 (2006) 19-46], which is based on the logarithmic-quadratic proximal method. In addition, the optimal step-sizes along these descent directions are identified to accelerate the convergence of the new method. Finally, some numerical results for solving traffic equilibrium problems are reported. 相似文献
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Optimization Letters - By reviewing the Logarithmic–quadratic proximal (LQP) method, in this paper we suggest and analyze a new LQP alternating direction scheme for the separable constrained... 相似文献
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Max L. N. Gonçalves Maicon Marques Alves Jefferson G. Melo 《Journal of Optimization Theory and Applications》2018,177(2):448-478
In this paper, we obtain global pointwise and ergodic convergence rates for a variable metric proximal alternating direction method of multipliers for solving linearly constrained convex optimization problems. We first propose and study nonasymptotic convergence rates of a variable metric hybrid proximal extragradient framework for solving monotone inclusions. Then, the convergence rates for the former method are obtained essentially by showing that it falls within the latter framework. To the best of our knowledge, this is the first time that global pointwise (resp. pointwise and ergodic) convergence rates are obtained for the variable metric proximal alternating direction method of multipliers (resp. variable metric hybrid proximal extragradient framework). 相似文献
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Application of the alternating direction method of multipliers to separable convex programming problems 总被引:3,自引:0,他引:3
Masao Fukushima 《Computational Optimization and Applications》1992,1(1):93-111
This paper presents a decomposition algorithm for solving convex programming problems with separable structure. The algorithm is obtained through application of the alternating direction method of multipliers to the dual of the convex programming problem to be solved. In particular, the algorithm reduces to the ordinary method of multipliers when the problem is regarded as nonseparable. Under the assumption that both primal and dual problems have at least one solution and the solution set of the primal problem is bounded, global convergence of the algorithm is established. 相似文献
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To solve a class of variational inequalities with separable structures, some classical methods such as the augmented Lagrangian
method and the alternating direction methods require solving two subvariational inequalities at each iteration. The most recent
work (B.S. He in Comput. Optim. Appl. 42(2):195–212, 2009) improved these classical methods by allowing the subvariational inequalities arising at each iteration to be solved in parallel,
at the price of executing an additional descent step. This paper aims at developing this strategy further by refining the
descent directions in the descent steps, while preserving the practical characteristics suitable for parallel computing. Convergence
of the new parallel descent-like method is proved under the same mild assumptions on the problem data. 相似文献
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A self-adaptive descent LQP alternating direction method for the structured variational inequalities
Numerical Algorithms - In this paper, by combining the logarithmic-quadratic proximal (LQP) method and alternating direction method, we proposed an LQP alternating direction method for solving... 相似文献
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《European Journal of Operational Research》2020,280(2):417-427
Inverse variational inequalities have broad applications in various disciplines, and some of them have very appealing structures. There are several algorithms (e.g., proximal point algorithms and projection-type algorithms) for solving the inverse variational inequalities in general settings, while few of them have fully exploited the special structures. In this paper, we consider a class of inverse variational inequalities that has a separable structure and linear constraints, which has its root in spatial economic equilibrium problems. To design an efficient algorithm, we develop an alternating direction method of multipliers (ADMM) based method by utilizing the separable structure. Under some mild assumptions, we prove its global convergence. We propose an improved variant that makes the subproblems much easier and derive the convergence result under the same conditions. Finally, we present the preliminary numerical results to show the capability and efficiency of the proposed methods. 相似文献
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Numerical Algorithms - The alternating direction method of multipliers (ADMM) is a popular method for solving separable convex programs with linear constraints, and its proximal version is an... 相似文献