A Bregman inertial forward-reflected-backward method for nonconvex minimization

We propose a Bregman inertial forward-reflected-backward (BiFRB) method for nonconvex composite problems. Assuming the generalized concave Kurdyka-Łojasiewicz property, we obtain sequential convergence of BiFRB, as well as convergence rates on both the function value and actual sequence. One disting...

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Bibliographic Details
Published in:	Journal of global optimization Vol. 89; no. 2; pp. 327 - 354
Main Authors:	Wang, Xianfu, Wang, Ziyuan
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2024 Springer Springer Nature B.V
Subjects:	Algorithms Analysis Computer Science Convergence Euclidean space Mathematics Mathematics and Statistics Methods Numerical analysis Operations Research/Decision Theory Optimization Parameters Real Functions Nonconvex optimization 49J52 Secondary 26D10 Generalized concave Kurdyka-Łojasiewicz property Forward-reflected-backward splitting Bregman proximal mapping Inertial effect Primary 90C26 Implicit merit function
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
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