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Subgradient method
Known as:
Bundle method
, Nonsmooth minimization
, Subgradient methods
Subgradient methods are iterative methods for solving convex minimization problems. Originally developed by Naum Z. Shor and others in the 1960s and…
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Related topics
Related topics
14 relations
Convex function
Convex optimization
Convex set
Drift plus penalty
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
A Novel Multiagent Control Scheme for Voltage Regulation in DC Distribution Systems
A. A. Hamad
,
H. Farag
,
E. El-Saadany
IEEE Transactions on Sustainable Energy
2015
Corpus ID: 20052287
This paper proposes a novel multiagent control scheme to mitigate the voltage regulation challenges of dc distribution systems…
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2010
2010
A gossip algorithm for convex consensus optimization over networks
Jie Lu
,
Choon Yik Tang
,
Paul R. Regier
,
T. D. Bow
Proceedings of the American Control Conference
2010
Corpus ID: 13340565
In many applications, nodes in a network wish to achieve not only a consensus, but an optimal one. To date, a family of…
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2010
2010
Globally Optimal Resource Allocation for Achieving Maximum Weighted Sum Rate
Kristoffer Eriksson
,
Shuying Shi
,
N. Vučić
,
M. Schubert
,
E. Larsson
IEEE Global Telecommunications Conference…
2010
Corpus ID: 2625815
We establish a general optimization framework for joint resource allocation and interference mitigation. By utilizing axiomatic…
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Highly Cited
2004
Highly Cited
2004
Dual optimization methods for multiuser orthogonal frequency division multiplex systems
Wei Yu
,
R. Lui
,
R. Cendrillon
IEEE Global Telecommunications Conference…
2004
Corpus ID: 9633490
The design and optimization of orthogonal frequency division multiplex (OFDM) systems typically take the following form. The…
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Highly Cited
2004
Highly Cited
2004
A DC piecewise affine model and a bundling technique in nonconvex nonsmooth minimization
A. Fuduli
,
M. Gaudioso
,
G. Giallombardo
Optim. Methods Softw.
2004
Corpus ID: 17154606
We introduce an algorithm to minimize a function of several variables with no convexity nor smoothness assumptions. The main…
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Highly Cited
2001
Highly Cited
2001
Bundle Methods in Stochastic Optimal Power Management: A Disaggregated Approach Using Preconditioners
Léonard Bacaud
,
C. Lemaréchal
,
A. Renaud
,
C. Sagastizábal
Computational optimization and applications
2001
Corpus ID: 17899155
A specialized variant of bundle methods suitable for large-scale problems with separable objective is presented. The method is…
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Highly Cited
2001
Highly Cited
2001
Filled functions for unconstrained global optimization
Zheng Xu
,
Hong-Xuan Huang
,
P. Pardalos
,
Cheng-Xian Xu
Journal of Global Optimization
2001
Corpus ID: 45716266
This paper is concerned with filled function techniques for unconstrained global minimization of a continuous function of several…
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Highly Cited
1999
Highly Cited
1999
A transmission-constrained unit commitment method in power system scheduling
C. Tseng
,
S. Oren
,
Carol S. Cheng
,
Chao-an Li
,
A. Svoboda
,
Raymond B. Johnson
Decision Support Systems
1999
Corpus ID: 14754704
Highly Cited
1988
Highly Cited
1988
Routing in a Network with Unreliable Components
B. Gavish
,
Irina Neuman
IEEE Transactions on Communications
1988
Corpus ID: 15521260
A new approach to the joint selection of primary and secondary routes in a network with unreliable components is presented. The…
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Highly Cited
1982
Highly Cited
1982
Cyclic subgradient projections
Y. Censor
,
A. Lent
Mathematical programming
1982
Corpus ID: 35218369
A cyclically controlled method of subgradient projections (CSP) for the convex feasibility problem of solving convex inequalities…
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