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arXiv · 2212.13278

A linearly convergent Gauss-Newton subgradient method for ill-conditioned problems

Damek Davis·Tao Jiang
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Abstract

We analyze a preconditioned subgradient method for optimizing composite functions $h \circ c$, where $h$ is a locally Lipschitz function and $c$ is a smooth nonlinear mapping. We prove that when $c$ satisfies a constant rank property and $h$ is semismooth and sharp on the image of $c$, the method converges linearly. In contrast to standard subgradient methods, its oracle complexity is invariant under reparameterizations of $c$.

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Damek Davis, Tao Jiang. 2022-12-26. A linearly convergent Gauss-Newton subgradient method for ill-conditioned problems. https://arxiv.org/abs/2212.13278

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