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Highly Cited

2020

Highly Cited

2020

Federated learning enables a large amount of edge computing devices to jointly learn a model without data sharing. As a leading…

Highly Cited

2012

Highly Cited

2012

Alternating direction methods (ADMs) have been well studied in the literature, and they have found many efficient applications in…

Highly Cited

2012

Highly Cited

2012

We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly…

Highly Cited

2011

Highly Cited

2011

We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal…

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…

Highly Cited

2009

Highly Cited

2009

In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization…

Highly Cited

2004

Highly Cited

2004

We propose a prox-type method with efficiency estimate $O(\epsilon^{-1})$ for approximating saddle points of convex-concave C$^{1…

Highly Cited

2001

Highly Cited

2001

It is well known that the analysis of the large-time asymptotics of Fokker-Planck type equations by the entropy method is closely…

Highly Cited

1993

Highly Cited

1986

Highly Cited

1986

The annealing algorithm is a stochastic optimization method which has attracted attention because of its success with certain…