CONVERGENCE PROPERTIES OF PROXIMAL (SUB)GRADIENT METHODS WITHOUT CONVEXITY OR SMOOTHNESS OF ANY OF THE FUNCTIONS.
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| Název: | CONVERGENCE PROPERTIES OF PROXIMAL (SUB)GRADIENT METHODS WITHOUT CONVEXITY OR SMOOTHNESS OF ANY OF THE FUNCTIONS. |
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| Autoři: | SOLODOV, MIKHAIL V. |
| Zdroj: | SIAM Journal on Optimization; 2025, Vol. 35 Issue 1, p28-41, 14p |
| Témata: | SMOOTHNESS of functions, NONSMOOTH optimization, SUBGRADIENT methods |
| Abstrakt: | We establish convergence properties for a framework that includes a variety of proximal subgradient methods, where none of the involved functions needs to be convex or differentiable. The functions are assumed to be Clarke-regular. Our results cover the projected and conditional variants for the constrained case, the use of the inertial/momentum terms, and incremental methods when each of the functions is itself a sum, and the methods process the components in this sum separately. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Complementary Index |
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