Add qrfact(SparseMatrixCSC) by wrapping SPQR. Make \(SparseMatrixCSC) work for least squares problems. Some tests.

Author: andreasnoack

base/linalg/generic.jl

@@ -237,11 +237,15 @@ function inv{T}(A::AbstractMatrix{T})
     A_ldiv_B!(factorize(convert(AbstractMatrix{S}, A)), eye(S, chksquare(A)))
 end
 
+function \{T}(A::AbstractMatrix{T}, B::AbstractVecOrMat{T})
+    size(A,1) == size(B,1) || throw(DimensionMismatch("LHS and RHS should have the same number of rows. LHS has $(size(A,1)) rows, but RHS has $(size(B,1)) rows."))
+    factorize(A)\B
+end
 function \{TA,TB}(A::AbstractMatrix{TA}, B::AbstractVecOrMat{TB})
     TC = typeof(one(TA)/one(TB))
-    size(A,1) == size(B,1) || throw(DimensionMismatch("LHS and RHS should have the same number of rows. LHS has $(size(A,1)) rows, but RHS has $(size(B,1)) rows."))
-    \(factorize(TA == TC ? A : convert(AbstractMatrix{TC}, A)), TB == TC ? copy(B) : convert(AbstractArray{TC}, B))
+    convert(AbstractMatrix{TC}, A)\convert(AbstractArray{TC}, B)
 end
+
 \(a::AbstractVector, b::AbstractArray) = reshape(a, length(a), 1) \ b
 /(A::AbstractVecOrMat, B::AbstractVecOrMat) = (B' \ A')'
 # \(A::StridedMatrix,x::Number) = inv(A)*x Should be added at some point when the old elementwise version has been deprecated long enough

base/sparse.jl

@@ -19,5 +19,6 @@ include("sparse/csparse.jl")
 include("sparse/linalg.jl")
 include("sparse/umfpack.jl")
 include("sparse/cholmod.jl")
+include("sparse/spqr.jl")
 
 end # module SparseMatrix

base/sparse/cholmod.jl

@@ -13,7 +13,7 @@ export
     Factor,
     Sparse
 
-using Base.SparseMatrix: AbstractSparseMatrix, SparseMatrixCSC, increment, increment!, indtype, decrement, decrement!
+using Base.SparseMatrix: AbstractSparseMatrix, SparseMatrixCSC, increment, indtype
 
 #########
 # Setup #
@@ -716,7 +716,7 @@ function Sparse(filename::ASCIIString)
 end
 
 ## convertion back to base Julia types
-function convert{T<:VTypes}(::Type{Matrix}, D::Dense{T})
+function convert{T}(::Type{Matrix{T}}, D::Dense{T})
     s = unsafe_load(D.p)
     a = Array(T, s.nrow, s.ncol)
     if s.d == s.nrow
@@ -730,6 +730,14 @@ function convert{T<:VTypes}(::Type{Matrix}, D::Dense{T})
     end
     a
 end
+convert{T}(::Type{Matrix}, D::Dense{T}) = convert(Matrix{T}, D)
+function convert{T}(::Type{Vector{T}}, D::Dense{T})
+    if size(D, 2) > 1
+        throw(DimensionMismatch("input must be a vector but had $(size(D, 2)) columnds"))
+    end
+    reshape(convert(Matrix, D), size(D, 1))
+end
+convert{T}(::Type{Vector}, D::Dense{T}) = convert(Vector{T}, D)
 
 function convert{Tv,Ti}(::Type{SparseMatrixCSC{Tv,Ti}}, A::Sparse{Tv,Ti})
     s = unsafe_load(A.p)

base/sparse/linalg.jl

@@ -686,8 +686,10 @@ function factorize(A::SparseMatrixCSC)
             end
         end
         return lufact(A)
+    elseif m > n
+        return qrfact(A)
     else
-        throw(ArgumentError("sparse least squares problems by QR are not handled yet"))
+        throw(ArgumentError("underdetermined systemed are not implemented yet"))
     end
 end
 

test/runtests.jl

@@ -3,7 +3,7 @@ testnames = [
     "linalg", "core", "keywordargs", "numbers", "strings", "dates",
     "dict", "hashing", "remote", "iobuffer", "staged", "arrayops",
     "subarray", "reduce", "reducedim", "random", "intfuncs",
-    "simdloop", "blas", "fft", "dsp", "sparse", "bitarray", "copy", "math",
+    "simdloop", "blas", "fft", "dsp", "sparsetests", "bitarray", "copy", "math",
     "fastmath", "functional", "bigint", "sorting", "statistics", "spawn",
     "backtrace", "priorityqueue", "arpack", "file", "version",
     "resolve", "pollfd", "mpfr", "broadcast", "complex", "socket",
@@ -25,12 +25,6 @@ push!(testnames, "parallel")
 
 tests = (ARGS==["all"] || isempty(ARGS)) ? testnames : ARGS
 
-if "sparse" in tests
-    # specifically selected case
-    filter!(x -> x != "sparse", tests)
-    prepend!(tests, ["sparse/sparse", "sparse/cholmod", "sparse/umfpack"])
-end
-
 if "linalg" in tests
     # specifically selected case
     filter!(x -> x != "linalg", tests)