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首页> 外文期刊>Journal of Optimization Theory and Applications >Inexact Operator Splitting Methods with Selfadaptive Strategy for Variational Inequality Problems
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Inexact Operator Splitting Methods with Selfadaptive Strategy for Variational Inequality Problems

机译:具有变分不等式问题的具有自适应策略的不精确算子分裂方法

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摘要

The Peaceman-Rachford and Douglas-Rachford operator splitting methods are advantageous for solving variational inequality problems, since they attack the original problems via solving a sequence of systems of smooth equations, which are much easier to solve than the variational inequalities. However, solving the subproblems exactly may be prohibitively difficult or even impossible. In this paper, we propose an inexact operator splitting method, where the subproblems are solved approximately with some relative error tolerance. Another contribution is that we adjust the scalar parameter automatically at each iteration and the adjustment parameter can be a positive constant, which makes the methods more practical and efficient. We prove the convergence of the method and present some preliminary computational results, showing that the proposed method is promising.
机译:Peaceman-Rachford和Douglas-Rachford算子拆分方法对于解决变分不等式问题非常有利,因为它们通过解决一系列平滑方程组来攻击原始问题,而光滑方程组比变分不等式要容易得多。然而,精确地解决子问题可能是非常困难的,甚至是不可能的。在本文中,我们提出了一种不精确的算子拆分方法,该方法可以用一些相对误差容限近似地解决子问题。另一个贡献是,我们在每次迭代时都会自动调整标量参数,并且调整参数可以为正常数,这使方法更加实用和高效。我们证明了该方法的收敛性,并给出了一些初步的计算结果,表明该方法是有前途的。

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