SparseSparse

Author: Sébastien Loisel

Installation

using Pkg; Pkg.add(url="https://github.com/sloisel/SparseSparse")

Introduction

We say that a matrix $A$ is doubly sparse if both $A$ and $A^{-1}$ are sparse. The class of doubly sparse matrices includes all matrices of the form $P(\prod B_k) Q$ where $P,Q$ are permutations and $B_k$ are block diagonal. SparseSparse is a package that inverts sparse matrices with sparse inverses, or otherwise solve sparse linear problems with sparse right-hand-sides.

With stock Julia, here is what happens if you try to invert a sparse matrix:

julia> using LinearAlgebra, SparseArrays
       A = sparse([2.0  3.0  0.0  0.0
                   4.0  5.0  0.0  0.0
                   0.0  0.0  6.0  7.0
                   0.0  0.0  8.0  9.0])
       inv(A)
The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory[...]

Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
[...]

The above matrix A is block-diagonal so it has a sparse inverse. The SparseSparse package overrides Base.inv so that it inverts sparse matrices and produces a sparse inverse, as follows.

julia> using SparseSparse
       inv(A)

4×4 SparseMatrixCSC{Float64, Int64} with 8 stored entries:
 -2.5   1.5    ⋅     ⋅ 
  2.0  -1.0    ⋅     ⋅ 
   ⋅     ⋅   -4.5   3.5
   ⋅     ⋅    4.0  -3.0

The implementation is based on SparseSparse.Factorization:

julia> Factorization(A)
SparseSparse.Factorization(...)

The Factorization object is as follows:

struct Factorization{Tv,Ti<:Integer} 
    L::Union{Missing,SparseMatrixCSC{Tv,Ti}}
    U::Union{Missing,SparseMatrixCSC{Tv,Ti}}
    p::Union{Missing,Vector{Ti}}
    q::Union{Missing,Vector{Ti}}
end

Fields L and U are sparse lower and upper triangular matrices, and vectors p and q are permutations. All these fields are optional and may take on the value missing when that particular term is omitted. Factorizations can be used to solve linear problems via

function Base.:\(A::Factorization, B::SparseMatrixCSC)

The underlying LU decomposition is computed using the stdlib's lu (or ldlt or cholesky). The problem is that stdlib refuses to compute L\B when B is itself sparse. The core of module SparseSparse is the following function:

function solve(L::SparseMatrixCSC{Tv,Ti},B::SparseMatrixCSC{Tv,Ti};solvemode=detect,numthreads=min(B.n,nthreads())) where {Tv,Ti<:Integer}

This function is able to solve lower or upper triangular sparse systems with sparse right-hand-sides. The algorithm is similar to the one described in Tim Davis's book and implemented in SuiteSparse here. The SparseSparse implementation also uses multithreading to speed up the solution time. The parameter solvemode should be either lower=1, upper=2 or detect=3.

Benchmarks

Here is a numerical experiment, calculating the inverse of a doubly sparse matrix.

julia> using Random
       A = blockdiag([sparse(randn(10,10)) for k=1:100]...)
       P = randperm(1000)
       Q = randperm(1000)
       A = A[P,Q]
1000×1000 SparseMatrixCSC{Float64, Int64} with 10000 stored entries:
[...]

julia> using BenchmarkTools
       @benchmark inv(Matrix(A))
BenchmarkTools.Trial: 155 samples with 1 evaluation.
 Range (min … max):  26.345 ms … 42.085 ms  ┊ GC (min … max): 0.00% … 13.34%
 Time  (median):     31.265 ms              ┊ GC (median):    0.00%
 Time  (mean ± σ):   32.239 ms ±  3.650 ms  ┊ GC (mean ± σ):  6.67% ±  8.61%

        ▃  ▁█▆ █▃▁▄▁▄ ▄            ▄ ▃▁▃  ▁                    
  ▇▄▄▄▆▆█▇▇███▆██████▇█▇▇▄▄▆▆▆▇▄▄▄▁█▆███▄▆█▄▆▄▄▆▆▇▁▄▇▆▁▁▁▄▁▁▄ ▄
  26.3 ms         Histogram: frequency by time        40.9 ms <

 Memory estimate: 15.76 MiB, allocs estimate: 7.

julia> @benchmark(inv(A))
BenchmarkTools.Trial: 1859 samples with 1 evaluation.
 Range (min … max):  1.744 ms … 13.188 ms  ┊ GC (min … max):  0.00% … 67.60%
 Time  (median):     2.105 ms              ┊ GC (median):     0.00%
 Time  (mean ± σ):   2.670 ms ±  1.735 ms  ┊ GC (mean ± σ):  11.39% ± 13.40%

  ▆█▇▆▅▃▁                                                     
  ██████████████▇▇▆▇▄▅▁▄▄▄▄▄▅▄▅▄▁▅▅▄▅▆▅▆▆▆▄▆▆▆▇▅▇▄▆▅▄▄▆▄▄▅▆▄ █
  1.74 ms      Histogram: log(frequency) by time     10.6 ms <

 Memory estimate: 3.69 MiB, allocs estimate: 585.

In the case of this 1000x1000 matrix, the SparseSparse method is approximately 12x faster and requires 76% less memory, compared to using dense algorithms.

Applications

We now briefly sketch an application of SparseSparse on Woodbury and $H$ matrices. Note that these matrix types are not part of the SparseSparse module.

The Woodbury matrix identity states that the inverse of $A+UCV$ is $$(A+UCV)^{-1} = A^{-1}-A^{-1}U(C^{-1}+VA^{-1}U)^{-1}VA^{-1}.$$ We can implement this in Julia as

struct Woodbury A; U; C; V end

Then, the inverse of a Woodbury matrix is

function Base.inv(X::Woodbury)
    Ainv = inv(X.A)
    Woodbury(Ainv,Ainv*X.U,-inv(inv(X.C)+X.V*Ainv*X.U),X.V*Ainv) 
end

Such matrices have a complete algebra of operations; we can add, subtract and multiply them, and thanks to the Woodbury identity, we can also invert them.

This is related to Hierarchical matrices (see the book by Hackbusch). Consider a matrix

$$\left[\begin{array}{cc} X & Y \\ Z & W \end{array} \right] \approx \left[\begin{array}{cc} X & U_1 S_1 V_1 \\ U_2 S_2 V_2 & W \end{array} \right].$$

Here, $Y \approx U_1S_1V_1$ is a low rank approximation obtained by sparse SVD, e.g. using Arpack. This approximation can efficiently be represented by a Woodbury decomposition. The decomposition is applied recursively to sub-blocks $X$ and $W$, resulting in an $H$-matrix. Here's an example with the 1d Laplacian:

julia> n = 4
       A = spdiagm(0=>2*ones(n),1=>-ones(n-1),-1=>-ones(n-1))
       H = hmat(A;r=1)
       Matrix(H)
4×4 Matrix{Float64}:
  2.0  -1.0   0.0   0.0
 -1.0   2.0  -1.0  -1.11022e-16
  0.0  -1.0   2.0  -1.0
  0.0   0.0  -1.0   2.0

In this case, the $H$-matrix representation of $A$ happens to be exact.

License

sloisel/SparseSparse

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