Results 31 to 40 of about 19,030 (190)
Subgradient Techniques for Passivity Enforcement of Linear Device and Interconnect Macromodels [PDF]
, 2012 This paper presents a class of nonsmooth convex optimization methods for the passivity enforcement of reduced-order macromodels of electrical interconnects, packages, and linear passive devices.
Calafiore, Giuseppe Carlo +2 more
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Incremental Stochastic Subgradient Algorithms for Convex Optimization [PDF]
, 2008 In this paper we study the effect of stochastic errors on two constrained incremental sub-gradient algorithms. We view the incremental sub-gradient algorithms as decentralized network optimization algorithms as applied to minimize a sum of functions ...
Nedich, A +2 more
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Low-Complexity LP Decoding of Nonbinary Linear Codes [PDF]
, 2013 Linear Programming (LP) decoding of Low-Density Parity-Check (LDPC) codes has attracted much attention in the research community in the past few years. LP decoding has been derived for binary and nonbinary linear codes.
Mark F. Flanagan +5 more
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One-Rank Linear Transformations and Fejer-Type Methods: An Overview
MathematicsSubgradient methods are frequently used for optimization problems. However, subgradient techniques are characterized by slow convergence for minimizing ravine convex functions.
Volodymyr Semenov +3 more
doaj +1 more source
Axioms, 2020 We propose two new iterative algorithms for solving K-pseudomonotone variational inequality problems in the framework of real Hilbert spaces. These newly proposed methods are obtained by combining the viscosity approximation algorithm, the Picard Mann ...
Godwin Amechi Okeke +2 more
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New results on subgradient methods for strongly convex optimization problems with a unified analysis
, 2015 We develop subgradient- and gradient-based methods for minimizing strongly convex functions under a notion which generalizes the standard Euclidean strong convexity.
Ito, Masaru
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Journal of Function Spaces, 2021 The primary objective of this study is to introduce two novel extragradient-type iterative schemes for solving variational inequality problems in a real Hilbert space.
Chainarong Khunpanuk +2 more
doaj +1 more source
``Efficient” Subgradient Methods for General Convex Optimization [PDF]
SIAM Journal on Optimization, 2016 A subgradient method is presented for solving general convex optimization problems, the main requirement being that a strictly-feasible point is known. A feasible sequence of iterates is generated, which converges to within user-specified error of optimality.
openaire +3 more sources
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.
Kočvara, Michal +2 more
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Journal of Inequalities and Applications, 2019 In this paper, we introduce and investigate composite inertial gradient-based algorithms with a line-search process for solving a variational inequality problem (VIP) with a pseudomonotone and Lipschitz continuous mapping and a common fixed-point problem
Lu-Chuan Ceng, Qing Yuan
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