Splitting Methods with Variable Metric for Kurdyka-Aojasiewicz Functions and General Convergence Rates

被引:138
作者
Frankel, Pierre [1 ]
Garrigos, Guillaume [1 ,2 ,3 ]
Peypouquet, Juan [2 ,3 ]
机构
[1] Univ Montpellier 2, Inst Math & Modelisat Montpellier, UMR CNRS 5149, F-34095 Montpellier 5, France
[2] Univ Tecn Federico Santa Maria, Dept Matemat, Valparaiso, Chile
[3] Univ Tecn Federico Santa Maria, AM2V, Valparaiso, Chile
关键词
Nonconvex and nonsmooth optimization; Kurdyka-Lojasiewicz inequality; Descent methods; Convergence rates; Variable metric; Gauss-Seidel method; Newton-like method; GRADIENT-LIKE SYSTEMS; DESCENT METHODS; THRESHOLDING ALGORITHM; PROXIMAL ALGORITHM; MONOTONE-OPERATORS; OPTIMIZATION; EQUATIONS; EQUILIBRIUM; SPARSE; FLOWS;
D O I
10.1007/s10957-014-0642-3
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
We study the convergence of general descent methods applied to a lower semi-continuous and nonconvex function, which satisfies the Kurdyka-Aojasiewicz inequality in a Hilbert space. We prove that any precompact sequence converges to a critical point of the function, and obtain new convergence rates both for the values and the iterates. The analysis covers alternating versions of the forward-backward method with variable metric and relative errors. As an example, a nonsmooth and nonconvex version of the Levenberg-Marquardt algorithm is detailed.
引用
收藏
页码:874 / 900
页数:27
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