Subsampled First-Order Optimization Methods with Applications in Imaging

Stefania Bellavia (), Tommaso Bianconcini (), Nataša Krejić () and Benedetta Morini ()
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Stefania Bellavia: Università degli Studi di Firenze (INdAM-GNCS members), Dipartimento di Ingegneria Industriale
Tommaso Bianconcini: Verizon Connect
Nataša Krejić: Faculty of Sciences, University of Novi Sad, Department of Mathematics and Informatics
Benedetta Morini: Università degli Studi di Firenze (INdAM-GNCS members), Dipartimento di Ingegneria Industriale

Chapter 2 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 61-95 from Springer

Abstract: Abstract This work presents and discusses optimization methods for solving finite-sum minimization problems which are pervasive in applications, including image processing. The procedures analyzed employ first-order models for the objective function and stochastic gradient approximations based on subsampling. Among the variety of methods in the literature, the focus is on selected algorithms which can be cast into two groups: algorithms using gradient estimates evaluated on samples of very small size and algorithms relying on gradient estimates and machinery from standard globally convergent optimization procedures. Neural networks and convolutional neural networks widely used for image processing tasks are considered, and a classification problem of images is solved with some of the methods presented.

Keywords: Finite-sum minimization; First-order methods; Stochastic gradient; Neural networks; Convolutional neural networks; Image classification (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98661-2_78

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DOI: 10.1007/978-3-030-98661-2_78

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