Relatively Smooth Convex Optimization by First-Order Methods, and Applications
The usual approach to developing and analyzing first-order methods for smooth convex optimization assumes that the gradient of the objective function is uniformly smooth with some Lipschitz constant L. However, in many settings the differentiable convex function f(?) is not uniformly smooth-for exam...
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| Format: | Article |
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Society for Industrial & Applied Mathematics (SIAM)
2019
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| Online Access: | http://hdl.handle.net/1721.1/120867 https://orcid.org/0000-0002-5217-1894 https://orcid.org/0000-0002-1733-5363 |