Software and High-Performance Computing

Low Rank Approximation of a Sparse Matrix Based on LU Factorization with Column and Row Tournament Pivoting


In this paper we present an algorithm for computing a low rank approximation of a sparse matrix based on a truncated LU factorization with column and row permutations. We present various approaches for determining the column and row permutations that show a trade-off between speed versus deterministic/probabilistic accuracy. We show that if the permutations are chosen by using tournament pivoting based on QR factorization, then the obtained truncated LU factorization with column/row tournament pivoting, LU_CRTP, satisfies bounds on the singular values which have similarities with the ones obtained by a communication avoiding rank revealing QR factorization. Experiments on challenging matrices show that LU_CRTP provides a good low rank approximation of the input matrix and it is less expensive than the rank revealing QR factorization in terms of computational and memory usage costs, while also minimizing the communication cost. We also compare the computational complexity of our algorithm with randomized algorithms and show that for sparse matrices and high enough but still modest accuracies, our approach is faster.


  1. rank revealing
  2. LU and QR factorizations
  3. column pivoting
  4. minimize communication

MSC codes

  1. 65F25
  2. 65F20

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Information & Authors


Published In

cover image SIAM Journal on Scientific Computing
SIAM Journal on Scientific Computing
Pages: C181 - C209
ISSN (online): 1095-7197


Submitted: 9 May 2016
Accepted: 11 August 2016
Published online: 15 March 2018


  1. rank revealing
  2. LU and QR factorizations
  3. column pivoting
  4. minimize communication

MSC codes

  1. 65F25
  2. 65F20



Funding Information

Defense Advanced Research Projects Agency : HR0011-12-2-0016

Funding Information

Office of Science : DE-SC0008700, DE-SC0010200, AC02-05CH11231

Funding Information

Horizon 2020 Framework Programme : 671633

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