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• W4315704959 abstract "We consider the minimization of a differentiable Lipschitz gradient but non necessarily convex, function F defined on $${mathbb {R}}^N$$ . We propose an accelerated gradient descent approach which combines three strategies, namely (i) a variable metric derived from the majorization-minimization principle; (ii) a subspace strategy incorporating information from the past iterates; (iii) a block alternating update. Under the assumption that F satisfies the Kurdyka–Łojasiewicz property, we give conditions under which the sequence generated by the resulting block majorize-minimize subspace algorithm converges to a critical point of the objective function, and we exhibit convergence rates for its iterates." @default.
• W4315704959 created "2023-01-12" @default.
• W4315704959 creator A5029245726 @default.
• W4315704959 creator A5062777204 @default.
• W4315704959 date "2023-09-16" @default.
• W4315704959 modified "2023-09-26" @default.
• W4315704959 title "Convergence analysis of block majorize-minimize subspace approach" @default.
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• W4315704959 doi "https://doi.org/10.1007/s11590-023-02055-z" @default.
• W4315704959 hasPublicationYear "2023" @default.
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