Randomized (Block) Coordinate Descent Method is an optimization algorithm popularized by Nesterov (2010) and Richtárik and Takáč (2011). The first… (More)

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2016

2016

- Ion Necoara, Dragos N. Clipici
- SIAM Journal on Optimization
- 2016

In this paper we employ a parallel version of a randomized (block) coordinate descent method for minimizing the sum of a… (More)

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2015

2015

- Alina Ene, Huy L. Nguyen
- ICML
- 2015

Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning… (More)

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2015

2015

- Andrei Patrascu, Ion Necoara
- J. Global Optimization
- 2015

In this paper we analyze several new methods for solving nonconvex optimization problems with the objective function formed as a… (More)

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2015

2015

- Andrei Patrascu, Ion Necoara
- IEEE Transactions on Automatic Control
- 2015

In this paper, we study the minimization of ℓ<sub>0</sub> regularized optimization problems, where the objective function… (More)

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2014

2014

- Ion Necoara, Andrei Patrascu
- Comp. Opt. and Appl.
- 2014

In this paper we propose a variant of the random coordinate descent method for solving linearly constrained convex optimization… (More)

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2013

2013

- Ion Necoara
- IEEE Transactions on Automatic Control
- 2013

In this paper, we develop randomized block-coordinate descent methods for minimizing multi-agent convex optimization problems… (More)

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2013

2013

- Andrei Patrascu, Ion Necoara
- 2013 European Control Conference (ECC)
- 2013

In this paper we develop a random coordinate descent method suitable for solving large-scale sparse nonconvex optimization… (More)

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2013

2013

In this paper we develop a random block coordinate descent method for minimizing large-scale convex problems with linearly… (More)

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2012

2012

- Ion Necoara
- 2012

In this paper we develop a novel randomized block-coordinate descent method for minimizing multi-agent convex optimization… (More)

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2007

2007

- Ling Li, Hsuan-Tien Lin
- 2007 International Joint Conference on Neural…
- 2007

The 0/1 loss is an important cost function for perceptrons. Nevertheless it cannot be easily minimized by most existing… (More)

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