# Gradient method

## Papers overview

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Highly Cited

2014

Highly Cited

2014

- NIPS
- 2014

In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recentlyâ€¦Â (More)

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Highly Cited

2014

Highly Cited

2014

- SIAM Journal on Optimization
- 2014

We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth componentâ€¦Â (More)

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Highly Cited

2013

Highly Cited

2013

- Math. Program.
- 2013

In this paper we analyze several new methods for solving optimization problems with the objective function formed as a sum of twoâ€¦Â (More)

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Highly Cited

2012

Highly Cited

2012

- NIPS
- 2012

We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is stronglyâ€¦Â (More)

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Highly Cited

2012

Highly Cited

2012

- IEEE Transactions on Signal Processing
- 2012

Nonnegative matrix factorization (NMF) is a powerful matrix decomposition technique that approximates a nonnegative matrix by theâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- ICML
- 2009

We consider the minimization of a smooth loss function regularized by the trace norm of the matrix variable. Such formulationâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- 2007

In this paper we analyze several new methods for solving optimization problems with the objective function formed as a sum of twoâ€¦Â (More)

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Highly Cited

2001

Highly Cited

2001

- NIPS
- 2001

We provide a natural gradient method that represents the steepest descent direction based on the underlying structure of theâ€¦Â (More)

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Highly Cited

2000

Highly Cited

2000

- SIAM Journal on Optimization
- 2000

We consider the gradient method xt+1 = xt + Î³t(st + wt), where st is a descent direction of a function f : <n â†’ < and wt is aâ€¦Â (More)

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Highly Cited

2000

Highly Cited

2000

- IROS
- 2000

Despite many decades of research into mobile robot control, reliable, high-speed motion in complicated, uncertain environmentsâ€¦Â (More)

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