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math.OC2022
A linearly convergent Gauss-Newton subgradient method for ill-conditioned problems
Damek Davis, Tao Jiang
We analyze a preconditioned subgradient method for optimizing composite functions , where is a locally Lipschitz function and is a smooth nonlinear mapping. We p…
math.OC2022★ 1 cited
A nearly linearly convergent first-order method for nonsmooth functions with quadratic growth
Damek Davis, Liwei Jiang
Classical results show that gradient descent converges linearly to minimizers of smooth strongly convex functions. A natural question is whether there exists a locally nearly linea…
math.OC2022
A superlinearly convergent subgradient method for sharp semismooth problems
Vasileios Charisopoulos, Damek Davis
Subgradient methods comprise a fundamental class of nonsmooth optimization algorithms. Classical results show that certain subgradient methods converge sublinearly for general Lips…