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Review

2016

Review

2016

Machine learning techniques are applied for solving a large variety of practical problems. The tasks attacked by machine learning… Expand

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Highly Cited

2012

Highly Cited

2012

Finding the sparsest solution to underdetermined systems of linear equations y = Φx is NP-hard in general. We show here that for… Expand

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Highly Cited

2006

Highly Cited

2006

We consider linear equations y = Φx where y is a given vector in ℝn and Φ is a given n × m matrix with n 0 so that for large n… Expand

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Highly Cited

2006

Highly Cited

2006

Finding the sparsest solution to underdetermined systems of linear equations y = Φx is NP-hard in general. We show here that for… Expand

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Highly Cited

2004

Highly Cited

2004

Supernode partitioning for unsymmetric matrices together with complete block diagonal supernode pivoting and asynchronous… Expand

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Highly Cited

1994

Highly Cited

1994

In this book, which focuses on the use of iterative methods for solving large sparse systems of linear equations, templates are… Expand

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Highly Cited

1986

Highly Cited

1986

A "coordinate recurrence" method for solving sparse systems of linear equations over finite fields is described. The algorithms… Expand

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Highly Cited

1983

Highly Cited

1983

We consider a class of iterative algorithms for solving systems of linear equations where the coefficient matrix is nonsymmetric… Expand

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Highly Cited

1965

Highly Cited

1965

Abstract : From a theory of Hohenberg and Kohn, approximation methods for treating an inhomogeneous system of interacting… Expand

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Highly Cited

1952

Highly Cited

1952

In an earlier publication [14] a method was described which generated the eigenvalues and eigenvectors of a matrix by a… Expand

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