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首页> 外文期刊>Journal of Optimization Theory and Applications >Interior Proximal Algorithm for Quasiconvex Programming Problems and Variational Inequalities with Linear Constraints
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Interior Proximal Algorithm for Quasiconvex Programming Problems and Variational Inequalities with Linear Constraints

机译:拟凸规划问题和带线性约束的变分不等式的内部近似算法。

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摘要

In this paper, we propose two interior proximal algorithms inspired by the logarithmic-quadratic proximal method. The first method we propose is for general linearly constrained quasiconvex minimization problems. For this method, we prove global convergence when the regularization parameters go to zero. The latter assumption can be dropped when the function is assumed to be pseudoconvex. We also obtain convergence results for quasimonotone variational inequalities, which are more general than monotone ones.
机译:在本文中,我们提出了两种基于对数二次方近端方法的内部近端算法。我们提出的第一种方法是解决一般的线性约束拟凸最小化问题。对于这种方法,当正则化参数变为零时,我们证明了全局收敛。当函数被假定为伪凸时,可以放弃后一个假设。我们还获得了比单调更为普遍的拟单调变分不等式的收敛结果。

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