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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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