FB-LISA accelerates 3D computed tomography reconstruction by applying a line-search stochastic gradient method with forward-backward splitting on structured mini-batches of full projections.
Beck.First-order methods in optimization
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Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.
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A Line--Search--Based Stochastic Gradient Method for 3D Computed Tomography
FB-LISA accelerates 3D computed tomography reconstruction by applying a line-search stochastic gradient method with forward-backward splitting on structured mini-batches of full projections.
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Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.