All of the matrices in NLP are large sparse matrices (larger than $1M$ in $850K$). I want to decompose such matrices by the fastest method. Which one is the best method for decomposition of a sparse rectangular matrix: LU or Cholesky?

I mean: what's the best method in theoretical?

  • $\begingroup$ Which software are you using? If it can handle sparse matrices it probably already has a decomposition function that is optimized for sparse matrices. $\endgroup$ – quarague Jul 12 at 12:02
  • $\begingroup$ I use python for programming. But I need to know the theorical solution. $\endgroup$ – BarzanHayati Jul 12 at 12:14

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