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… (More)
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Highly Cited
2010
Highly Cited
2010
We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in… (More)
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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… (More)
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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… (More)
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Highly Cited
2008
Highly Cited
2008
We consider a convex unconstrained optimization problem that arises in a network of agents whose goal is to cooperatively… (More)
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Highly Cited
2007
Highly Cited
2007
3 Convergence proof 4 3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Some basic… (More)
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Highly Cited
2007
Highly Cited
2007
Promising approaches to structured learning problems have recently been developed in the maximum margin framework. Unfortunately… (More)
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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… (More)
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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… (More)
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Highly Cited
2000
Highly Cited
2000
We present an extension to the subgradient algorithm to produce primal as well as dual solutions. It can be seen as a fast way to… (More)
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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… (More)
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