Malitsky-Tam forward-reflected-backward splitting method for nonconvex minimization problems
نویسندگان
چکیده
We extend the Malitsky-Tam forward-reflected-backward (FRB) splitting method for inclusion problems of monotone operators to nonconvex minimization problems. By assuming generalized concave Kurdyka-Łojasiewicz (KL) property a quadratic regularization objective, we show that FRB converges globally stationary point objective and enjoys finite length property. Convergence rates are also given. The sharpness our approach is guaranteed by virtue exact modulus associated with KL Numerical experiments suggest competitive compared Douglas-Rachford Boţ-Csetnek inertial Tseng’s method.
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ژورنال
عنوان ژورنال: Computational Optimization and Applications
سال: 2022
ISSN: ['0926-6003', '1573-2894']
DOI: https://doi.org/10.1007/s10589-022-00364-0