Results 11 to 20 of about 2,178 (210)
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).
NESTEROV, Y.
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About the Subgradient Method for Equilibrium Problems
MathematicsConvergence results of the subgradient algorithm for equilibrium problems were mainly obtained using a Lipschitz continuity assumption on the given bifunctions. In this paper, we first provide a complexity result for monotone equilibrium problems without
Abdellatif Moudafi
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A Subgradient Method for Free Material Design [PDF]
SIAM Journal on Optimization, 2016 A small improvement in the structure of the material could save the manufactory a lot of money. The free material design can be formulated as an optimization problem. However, due to its large scale, second-order methods cannot solve the free material design problem in reasonable size.
Michal Kocvara +2 more
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Subgradient Method for Nonconvex Nonsmooth Optimization
Journal of Optimization Theory and Applications, 2012 Based on the notion of quasisecants introduced by \textit{A. M. Bagirov} and \textit{A. N. Ganjehlou} [Optim. Methods Softw. 25, No. 1, 3--18 (2010; Zbl 1202.65072)], the authors develop a version of the subgradient method for solving nonconvex nonsmooth optimization problems. Quasisecants are subgradients computed in some neighborhood of a point.
Adil M. Bagirov +4 more
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Horoballs and the subgradient method
CoRRTo explore convex optimization on Hadamard spaces, we consider an iteration in the style of a subgradient algorithm. Traditionally, such methods assume that the underlying spaces are manifolds and that the objectives are geodesically convex: the methods are described using tangent spaces and exponential maps.
Adrian S. Lewis +2 more
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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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Aggregate subgradient method for nonsmooth DC optimization [PDF]
, 2021 The aggregate subgradient method is developed for solving unconstrained nonsmooth difference of convex (DC) optimization problems. The proposed method shares some similarities with both the subgradient and the bundle methods.
Mäkelä, Marko +4 more
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Fixed point quasiconvex subgradient method [PDF]
European Journal of Operational Research, 2020 Constrained quasiconvex optimization problems appear in many fields, such as economics, engineering, and management science. In particular, fractional programming, which models ratio indicators such as the profit/cost ratio as fractional objective functions, is an important instance.
Kazuhiro Hishinuma, Hideaki Iiduka
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Primal Subgradient Methods with Predefined Step Sizes. [PDF]
J Optim Theory ApplAbstractIn this paper, we suggest a new framework for analyzing primal subgradient methods for nonsmooth convex optimization problems. We show that the classical step-size rules, based on normalization of subgradient, or on knowledge of the optimal value of the objective function, need corrections when they are applied to optimization problems with ...
Nesterov Y.
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Subgradient ellipsoid method for nonsmooth convex problems. [PDF]
Math Program, 2023 AbstractIn this paper, we present a new ellipsoid-type algorithm for solving nonsmooth problems with convex structure. Examples of such problems include nonsmooth convex minimization problems, convex-concave saddle-point problems and variational inequalities with monotone operator.
Rodomanov A, Nesterov Y.
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