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Highly Cited

2015

Highly Cited

2015

Coordinate descent algorithms solve optimization problems by successively performing approximate minimization along coordinate… Expand

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Highly Cited

2015

Highly Cited

2015

We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably… Expand

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Highly Cited

2015

Highly Cited

2015

We propose a new randomized coordinate descent method for minimizing the sum of convex functions each of which depends on a small… Expand

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Highly Cited

2014

Highly Cited

2014

In this paper we develop a randomized block-coordinate descent method for minimizing the sum of a smooth and a simple nonsmooth… Expand

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Highly Cited

2013

Highly Cited

2013

In this paper we study smooth convex programming problems where the decision variables vector is split into several blocks of… Expand

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Highly Cited

2013

Highly Cited

2013

Stochastic gradient descent is popular for large scale optimization but has slow convergence asymptotically due to the inherent… Expand

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Highly Cited

2012

Highly Cited

2012

In this paper we propose new methods for solving huge-scale optimization problems. For problems of this size, even the simplest… Expand

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Highly Cited

2011

Highly Cited

2011

We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1-regularized losses. Though coordinate descent seems… Expand

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Highly Cited

2008

Highly Cited

2008

In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is… Expand

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Highly Cited

2008

Highly Cited

2008

Imposition of a lasso penalty shrinks parameter estimates toward zero and performs continuous model selection. Lasso penalized… Expand

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