Convergence Rates for Deterministic and Stochastic Subgradient Methods without Lipschitz Continuity
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Publication:5231668
DOI10.1137/18M117306XzbMath1421.90115arXiv1712.04104OpenAlexW2963531627WikidataQ127856914 ScholiaQ127856914MaRDI QIDQ5231668
Publication date: 27 August 2019
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1712.04104
Convex programming (90C25) Methods of reduced gradient type (90C52) Numerical methods for variational inequalities and related problems (65K15)
Related Items (6)
Unified analysis of stochastic gradient methods for composite convex and smooth optimization ⋮ Stochastic approximation with discontinuous dynamics, differential inclusions, and applications ⋮ On optimal universal first-order methods for minimizing heterogeneous sums ⋮ Convergence Rates of Proximal Gradient Methods via the Convex Conjugate ⋮ A unified convergence analysis of stochastic Bregman proximal gradient and extragradient methods ⋮ Cyclic coordinate descent in the Hölder smooth setting
Uses Software
Cites Work
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