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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… Expand
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Papers overview

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Highly Cited
2010
Highly Cited
2010
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in… Expand
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Highly Cited
2009
Highly Cited
2009
We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For… Expand
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Highly Cited
2009
Highly Cited
2009
In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with… Expand
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Highly Cited
2009
Highly Cited
2009
We present an algorithm that generalizes the randomized incremental subgradient method with fixed stepsize due to Nedic and… Expand
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Highly Cited
2009
Highly Cited
2009
In this paper, we study methods for generating approximate primal solutions as a byproduct of subgradient methods applied to the… Expand
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Highly Cited
2009
Highly Cited
2009
We study subgradient methods for computing the saddle points of a convex-concave function. Our motivation comes from networking… Expand
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Highly Cited
2003
Highly Cited
2003
The mirror descent algorithm (MDA) was introduced by Nemirovsky and Yudin for solving convex optimization problems. This method… Expand
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Highly Cited
2001
Highly Cited
2001
We consider a class of subgradient methods for minimizing a convex function that consists of the sum of a large number of… Expand
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Highly Cited
2000
Highly Cited
2000
Abstract.We present an extension to the subgradient algorithm to produce primal as well as dual solutions. It can be seen as a… Expand
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Highly Cited
1974
Highly Cited
1974
The “relaxation” procedure introduced by Held and Karp for approximately solving a large linear programming problem related to… Expand
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