Different behavior of .* in sparse and non-sparse matrices and problem in sparse lufact()

[jasax]

I'm fiddling with sparse matrices and have found a few quirks :-)

What I pretend to do is use LU factorization in sparse matrices (for now in real matrices, but I hope to get it done also in sparse complex matrices)

First, in the manual, in the Linear Algebra section (http://docs.julialang.org/en/release-0.3/stdlib/linalg/) there is the relation between the L and U factors (in the case of F=lufact(A) operation they are named/extracted as F[:L] and F[:U] ) and a line and column permutation of the factorized sparse matrix A that reads as F[:L] * F[:U] == Rs .* A[F[:p], F[:q]] It seems to me (according to the explanation in that section) that it should read as F[:L] * F[:U] == F[:Rs] .* A[F[:p], F[:q]] because Rs doesn't exist per se (it has to be extracted from the object F, and so is F[:Rs]).

Aside that typo, additionally the RHS of that expression when evaluated gives an error due to a mismatch in dimensions. Indeed there is not a perfect equivalence/behavior of the .* operator (this doesn't happen with the .+ operator, for example) in real and in complex matrices, as the session below clearly shows. I end that session with an example of the failure, by applying lufact() and exemplifying the error.

I checked the code around "sparse/sparsematrix.jl:531" where the error is triggered and there is a check of conformant dimensions for matrix dot product .* (and of other operators)

       .............
           if size(A,1) != size(B,1) || size(A,2) != size(B,2)
                throw(DimensionMismatch(""))            # line 531 of the source code
            end
       ...............

(where A and B are the operands) which means that the .* operator only accepts operands with the same dimensions. I think that it is what triggers the error.

But then this configures a different behavior for .* in sparse and non sparse matrix representations.

Below is a session with julia 0.3.0 running in windows 7 in AMD 8-core Piledriver processor where the above cases are visible, with added comments. I experimented in julia 0.2.1 in Linux-Ubuntu (over Virtualbox) and the behavior of .* is the same as in windows 7; however, in 0.2.1 lufact() is not implemented for sparses, so I could not check it.

# H and v are a matrix and a vector, Hs and vs the corresponding sparse objects.

julia> H=[1 0; 2 -1.0]
2x2 Array{Float64,2}:
 1.0   0.0
 2.0  -1.0

julia> v=[2,3.0]
2-element Array{Float64,1}:
 2.0
 3.0

julia> Hs=sparse(H)
2x2 sparse matrix with 3 Float64 entries:
        [1, 1]  =  1.0
        [2, 1]  =  2.0
        [2, 2]  =  -1.0

julia> vs=sparse(v)
2x1 sparse matrix with 2 Float64 entries:
        [1, 1]  =  2.0
        [2, 1]  =  3.0

julia> v .* H
2x2 Array{Float64,2}:
 2.0   0.0
 6.0  -3.0

julia> v .* Hs
ERROR: DimensionMismatch("")
 in .* at sparse/sparsematrix.jl:531

julia> vs .* H
ERROR: DimensionMismatch("")
 in .* at sparse/sparsematrix.jl:531

julia> vs .* Hs
ERROR: DimensionMismatch("")
 in .* at sparse/sparsematrix.jl:531

# product * is OK

julia> H * Hs
2x2 Array{Float64,2}:
 1.0  0.0
 0.0  1.0

julia> Hs * H
2x2 Array{Float64,2}:
 1.0  0.0
 0.0  1.0

# does difference in sizes of v and vs explain something?

julia> size(H)
(2,2)

julia> size(Hs)
(2,2)

julia> size(v)
(2,)

julia> size(vs)
(2,1)

## the .+ operator is OK with mismatch in operand dimensions (sparse or not)

julia> v .+ H
2x2 Array{Float64,2}:
 3.0  2.0
 5.0  2.0

julia> v .+ Hs
2x2 Array{Float64,2}:
 3.0  2.0
 5.0  2.0

julia> vs .+ H
2x2 Array{Float64,2}:
 3.0  2.0
 5.0  2.0

julia> vs .+ Hs
2x2 Array{Float64,2}:
 3.0  2.0
 5.0  2.0

# for conformant sparse operands is OK

julia> Hs .* Hs
2x2 sparse matrix with 3 Float64 entries:
        [1, 1]  =  1.0
        [2, 1]  =  4.0
        [2, 2]  =  1.0

# now we lufact(Hs) and show the problem with F[:Rs] .* A[F[:p], F[:q]]

julia> S=lufact(Hs)
UMFPACK LU Factorization of a 2-by-2 sparse matrix
Ptr{Void} @0x0000000020c65560

julia> S[:L]
2x2 sparse matrix with 2 Float64 entries:
        [1, 1]  =  1.0
        [2, 2]  =  1.0

julia> S[:U]
2x2 sparse matrix with 3 Float64 entries:
        [1, 1]  =  -0.333333
        [1, 2]  =  0.666667
        [2, 2]  =  1.0

julia> S[:p]
2-element Array{Int64,1}:
 2
 1

julia> S[:q]
2-element Array{Int64,1}:
 2
 1

julia> S[:Rs]
2-element Array{Float64,1}:
 1.0
 0.333333

julia> Hsperm=Hs[S[:p],S[:q]]
2x2 sparse matrix with 3 Float64 entries:
        [1, 1]  =  -1.0
        [1, 2]  =  2.0
        [2, 2]  =  1.0

julia> S[:Rs] .* Hsperm
ERROR: DimensionMismatch("")
 in .* at sparse/sparsematrix.jl:531

[tkelman]

Not positive, but I think you actually want to be using F[:Rs] as the diagonal elements of a scaling matrix, not as a vector. Some of the other issues are due to not currently having true sparse vectors, the same way we have 1-d dense vectors. Sparse vectors are inconsistently implemented as n-by-1 CSC matrices, which leads to some problems.

[jasax]

This is a bit awkward, as well as the existence of a 1-D Array type which after double transposition (should return the original object) becomes a different type. The code below shows that the "black sheep" is the original vector, g; if g was created as a 5x1 Array{Float64,2} (which g becomes in, after double transposition), instead of as a 5-element Array{Float64,1}, the inconsistency wouldn't exist. But that change (vector becomes matrix) probably should have some impact (in time of execution) in many of vector uses... :-)

julia> g=[1;2;3;4;5.0]
5-element Array{Float64,1}:
 1.0
 2.0
 3.0
 4.0
 5.0
julia> g==g''
false
julia> sparse(g)==sparse(g'')
true
julia> g'
1x5 Array{Float64,2}:
 1.0  2.0  3.0  4.0  5.0
julia> g''
5x1 Array{Float64,2}:
 1.0
 2.0
 3.0
 4.0
 5.0
julia> sparse(g)
5x1 sparse matrix with 5 Float64 entries:
        [1, 1]  =  1.0
        [2, 1]  =  2.0
        [3, 1]  =  3.0
        [4, 1]  =  4.0
        [5, 1]  =  5.0
julia> sparse(g'')
5x1 sparse matrix with 5 Float64 entries:
        [1, 1]  =  1.0
        [2, 1]  =  2.0
        [3, 1]  =  3.0
        [4, 1]  =  4.0
        [5, 1]  =  5.0
julia> (sparse(g))'==sparse(g')
true
julia> full(sparse(g)')==g'
true

I apologize if these questions have already been discussed and settled down previously (as probably they have...) but I couldn't find when searching.

[jiahao]

4774

[jasax]

Thanks @jiahao. The funny thing is that the sparse() and full() functions have eliminated the inconsistency, that is, sparse objects are "mathematically" consistent with transposition :-)

More funny stuff :-) (we can always use multiple dispatch to tackle both types...)

julia> function f(v::Array{Float64,1})
println(v)
end
f (generic function with 1 method)
julia> g
5-element Array{Float64,1}:
 1.0
 2.0
 3.0
 4.0
 5.0
julia> f(g)
[1.0,2.0,3.0,4.0,5.0]
julia> f(g'')
ERROR: `f` has no method matching f(::Array{Float64,2})
julia> f(full(sparse(g)))
ERROR: `f` has no method matching f(::Array{Float64,2})
julia> function f(v::Array{Float64,2})
println(v)
end
f (generic function with 2 methods)
julia> f(g'')
[1.0
 2.0
 3.0
 4.0
 5.0]

But i'm diverging... the original issue was that the v .* Msparse operation doesn't work, while v .* M does... and I don't know if it is seen as a "bug" or as a "feature" in Julia :-).

[tkelman]

sparse objects are "mathematically" consistent with transposition :-)

Only because sparse vectors are currently implemented in a more Matlab-ish manner than the Julia way dense vectors work.

v .* Msparse operation doesn't work, while v .* M does... and I don't know if it is seen as a "bug" or as a "feature" in Julia :-)

Bug. Sparse vectors should probably be the 1-D case of general N-D COO sparse, rather than shoehorned in as 1-column CSC matrices like they are now.

The inconsistency of dense g'', as Jiahao pointed out, is a separate bug in Julia's handling of transpose vectors. There's another PR open for a Transpose type which I think would be a nicer solution here. And if we had true 1-D sparse vectors, then the same behaviors would apply to both sparse and dense 1-D vectors w.r.t. transposition and linear algebraic operations.

[tkelman]

Oh, and the v .* Msparse actually has another cause unrelated to sparse vectors - the broadcasting behavior of v .* Mdense just isn't implemented in the sparse version of .*.

julia> [5,6] .* [1 2; 3 4]
2x2 Array{Int64,2}:
  5  10
 18  24

There are cases where this is what you want, if :Rs stands for row scaling. But evidently the broadcast machinery isn't generalized to sparse. not quite, rather the sparse methods are taking priority:

julia> @which [5,6] .* [1 2; 3 4]
.*(As::AbstractArray{T,N}...) at broadcast.jl:278

julia> @which [5,6] .* sparse([1 2; 3 4])
.*(A::Array{T,N},B::SparseMatrixCSC{Tv,Ti<:Integer}) at sparse/sparsematrix.jl:634

Line 634 of sparse/sparsematrix.jl is (.*)(A::Array, B::SparseMatrixCSC) = (.*)(sparse(A), B), which doesn't do broadcasting correctly because sparse vectors are not 1-D.

[jasax]

OK, thanks @tkelman. Unfortunately I don't know too much (i.e. near zero...) of Julia's innards. I browsed umfpack.jl in the sources and saw that C functions from the package are being called (with ccall()). I cannot yet juggle with Julia's types and type system.Could not go to a spot and propose a patch :-(

I'm in the (spare time) process of building simulation tools for handling (hopefully) very large electric circuits, which is only viable in a sparse matrix landscape. Once upon a time I did those things in C, but now I was hoping to benefit from sparse implementation in Julia to avoid the malloc() - free() tango in C :-), use PCRE to parse input files, and escape from turtle velocity offered by Perl, Python, Ruby, etc... :-)

[tkelman]

@jasax I think the best place to look is around line 634 of sparse/sparsematrix,jl. That file should be mostly pure-Julia code. I think unconditionally converting A::Array to sparse(A) might be the wrong thing to do for .* here. You may be able to make headway experimenting with some alternatives.