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Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
April 30, 2024, 4:44 a.m. | Chris Kolb, Christian L. M\"uller, Bernd Bischl, David R\"ugamer
cs.LG updates on arXiv.org arxiv.org
Abstract: We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-convex problems typically rely on solvers tailored to specific models and regularizers. In contrast, our method enables fully differentiable and approximation-free optimization and is thus compatible with the ubiquitous gradient descent paradigm in deep learning. The proposed optimization transfer comprises an overparameterization of selected parameters and a change of penalties. In the overparametrized problem, smooth surrogate regularization …
abstract approximation arxiv contrast cs.lg differentiable framework free math.oc optimization regularization sparsity stat.ml type
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