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2019

2019

In this paper, we study accelerated Regularized Newton Methods for minimizing objectives formed as a sum of two functions: one is… Expand

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2019

2019

For a symmetric positive semidefinite linear system of equations $$\mathcal{Q}{{\varvec{x}}}= {{\varvec{b}}}$$Qx=b, where… Expand

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2018

2018

We consider a class of difference-of-convex (DC) optimization problems whose objective is level-bounded and is the sum of a… Expand

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2017

2017

In this paper, we further study the forward–backward envelope first introduced in Patrinos and Bemporad (Proceedings of the IEEE… Expand

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2017

2017

In this paper, we study the proximal gradient algorithm with extrapolation for minimizing the sum of a Lipschitz differentiable… Expand

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2013

2013

Given x0, a point of a convex subset C of a Euclidean space, the two following statements are proven to be equivalent: (i) every… Expand

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Review

2009

Review

2009

Computational convex analysis algorithms have been rediscovered several times in the past by researchers from different fields… Expand

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2005

2005

In order to minimize a closed convex function that is approximated by a sequence of better behaved functions, we investigate the… Expand

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Highly Cited

2005

Highly Cited

2005

Let $\Phi_0:\mathbb{R}^n\to \mathbb{R}\cup \{+\infty\}$ be a closed convex function and $\Phi_1:\mathbb{R}^n\to \mathbb{R}$ be a… Expand

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1996

1996

Let X be a real Hilbert space endowed with inner product 〈., .〉 and associated norm ‖.‖, and let f be a proper closed convex… Expand

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