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On proximal gradient mapping and its minimization in norm via potential function-based acceleration (2212.07149v1)
Published 14 Dec 2022 in math.OC
Abstract: The proximal gradient descent method, well-known for composite optimization, can be completely described by the concept of proximal gradient mapping. In this paper, we highlight our previous two discoveries of proximal gradient mapping--norm monotonicity and refined descent, with which we are able to extend the recently proposed potential function-based framework from gradient descent to proximal gradient descent.
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