Results 11 to 20 of about 884 (184)
On Robustness of the Normalized Subgradient Method with Randomly Corrupted Subgradients [PDF]
2021 American Control Conference (ACC), 2021 Numerous modern optimization and machine learning algorithms rely on subgradient information being trustworthy and hence, they may fail to converge when such information is corrupted. In this paper, we consider the setting where subgradient information may be arbitrarily corrupted (with a given probability) and study the robustness properties of the ...
Berkay Turan +3 more
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Pathological Subgradient Dynamics [PDF]
SIAM Journal on Optimization, 2020 We construct examples of Lipschitz continuous functions, with pathological subgradient dynamics both in continuous and discrete time. In both settings, the iterates generate bounded trajectories, and yet fail to detect any (generalized) critical points of the function.
Aris Daniilidis, Dmitriy Drusvyatskiy
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On Subgradient Projectors [PDF]
SIAM Journal on Optimization, 2015 The subgradient projector is of considerable importance in convex optimization because it plays the key role in Polyak's seminal work - and the many papers it spawned - on subgradient projection algorithms for solving convex feasibility problems. In this paper, we offer a systematic study of the subgradient projector.
Heinz H. Bauschke +3 more
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Mathematics, 2023 A class of controlled objects is considered, the dynamics of which are determined by a vector system of ordinary differential equations with a partially known right-hand side.
Alexander Nazin +2 more
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Increase of noise immunity of photomask images binarization in the space of the wavelet transform [PDF]
Tekhnologiya i Konstruirovanie v Elektronnoi Apparature, 2011 An information technology for histogram analysis and a method for noise immunity binary processing of integrated and printed circuits board photo-masks image based on this technology was carryed out.
Shcherbakova G. Yu. +4 more
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Radial Subgradient Method [PDF]
SIAM Journal on Optimization, 2018 We present a subgradient method for minimizing non-smooth, non-Lipschitz convex optimization problems. The only structure assumed is that a strictly feasible point is known. We extend the work of Renegar [5] by taking a different perspective, leading to an algorithm which is conceptually more natural, has notably improved convergence rates, and for ...
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Gradients and subgradients of buffered failure probability [PDF]
Operations Research Letters, 2021 Gradients and subgradients are central to optimization and sensitivity analysis of buffered failure probabilities. We furnish a characterization of subgradients based on subdifferential calculus in the case of finite probability distributions and, under additional assumptions, also a gradient expression for general distributions.
Johannes O. Royset, Ji-Eun Byun
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Journal of Inequalities and Applications, 2021 For the purpose of this article, we introduce a modified form of a generalized system of variational inclusions, called the generalized system of modified variational inclusion problems (GSMVIP).
Araya Kheawborisut, Atid Kangtunyakarn
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A delayed subgradient method for nonsmooth convex-concave min–max optimization problems
Results in Control and Optimization, 2023 In this paper, we aim to solve a convex-concave min–max optimization problem, where the convex-concave coupling function is nonsmooth in both variables.
Tipsuda Arunrat, Nimit Nimana
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Barrier subgradient method [PDF]
Mathematical Programming, 2010 The author focusses on a class of problems of minimizing a nonsmooth convex function over a feasible set endowed by a self-concordant barrier. After studying the smoothing of the support function of a convex set by a self-concordant barrier, the author describes the corresponding barrier subgradient method (BSM).
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