More 0.7 updates and some fixes

.travis.yml

@@ -3,7 +3,6 @@ os:
   - linux
   - osx
 julia:
-  - 0.6
   - nightly
 notifications:
   email: true

REQUIRE

@@ -1,2 +1 @@
-julia 0.6
-Compat 0.49
+julia 0.7-alpha

src/SymWoodburyMatrices.jl

@@ -1,16 +1,16 @@
 import Base:+,*,-,\,^,copy
 
-using Compat.LinearAlgebra.BLAS:gemm!,gemm,axpy!
-using Compat.SparseArrays
-import Compat.SparseArrays.sparse
+using LinearAlgebra.BLAS: gemm!, gemm, axpy!
+using SparseArrays
+import SparseArrays.sparse
 
 """
 Represents a matrix of the form A + BDBᵀ.
 """
-mutable struct SymWoodbury{T,AType, BType, DType} <: AbstractMatrix{T}
-  A::AType;
-  B::BType;
-  D::DType;
+struct SymWoodbury{T,AType, BType, DType} <: Factorization{T}
+  A::AType
+  B::BType
+  D::DType
 end
 
 """
@@ -39,7 +39,7 @@ end
 convert(::Type{W}, O::SymWoodbury) where {W<:Woodbury} = Woodbury(O.A, O.B, O.D, O.B')
 
 inv_invD_BtX(invD, B, X) = inv(invD - B'*X);
-inv_invD_BtX(invD, B::AbstractVector, X) = inv(invD - vecdot(B,X));
+inv_invD_BtX(invD, B::AbstractVector, X) = inv(invD - dot(B,X));
 
 function calc_inv(A, B, D)
   W = inv(A);
@@ -81,7 +81,7 @@ function liftFactorVars(A,B,D)
   k  = size(B,2)
   M = [A    B   ;
        B'  -Di ];
-  M = lufact(M) # ldltfact once it's available.
+  M = lu(M) # ldltfact once it's available.
   return x -> (M\[x; zeros(k,1)])[1:n,:];
 end
 
@@ -123,18 +123,15 @@ end
 # Minor optimization for the rank one case
 function plusBDBtx!(o, B::Array{Float64,1}, d::Real, x::Union{Array{Float64,2}, SubArray})
   if size(x,2) == 1
-    axpy!(vecdot(B,x)*d, B, o)
+    axpy!(dot(B,x)*d, B, o)
   else
     w = d*gemm('T', 'N' ,reshape(B, size(B,1), 1),x);
     gemm!('N','N',1.,B,w,1., o)
   end
 end
 
-Base.Ac_mul_B(O1::SymWoodbury{T}, x::AbstractVector{T}) where {T} = O1*x
-Base.Ac_mul_B(O1::SymWoodbury, x::AbstractMatrix) = O1*x
-
 +(O::SymWoodbury, M::SymWoodbury)    = SymWoodbury(O.A + M.A, [O.B M.B],
-                                                   cat([1,2],O.D,M.D) );
+                                                   cat(O.D,M.D; dims=(1,2)) );
 *(α::Real, O::SymWoodbury)           = SymWoodbury(α*O.A, O.B, α*O.D);
 *(O::SymWoodbury, α::Real)           = SymWoodbury(α*O.A, O.B, α*O.D);
 +(M::AbstractMatrix, O::SymWoodbury) = SymWoodbury(O.A + M, O.B, O.D);
@@ -176,18 +173,24 @@ function *(O1::SymWoodbury, O2::SymWoodbury)
   end
 end
 
-Base.A_mul_Bc(O1::SymWoodbury, O2::SymWoodbury) = O1*O2
-
 conjm(O::SymWoodbury, M) = SymWoodbury(M*O.A*M', M*O.B, O.D);
 
 Base.getindex(O::SymWoodbury, I::UnitRange, I2::UnitRange) =
   SymWoodbury(O.A[I,I], O.B[I,:], O.D);
 
 # This is a slow hack, but generally these matrices aren't sparse.
-Compat.SparseArrays.sparse(O::SymWoodbury) = sparse(Matrix(O))
+SparseArrays.sparse(O::SymWoodbury) = sparse(Matrix(O))
 
 # returns a pointer to the original matrix, this is consistent with the
 # behavior of Symmetric in Base.
-Compat.adjoint(O::SymWoodbury) = O
+adjoint(O::SymWoodbury) = O
 
-Compat.LinearAlgebra.det(W::SymWoodbury) = det(convert(Woodbury, W))
+det(W::SymWoodbury) = det(convert(Woodbury, W))
+
+function show(io::IO, W::SymWoodbury)
+    println(io, "Symmetric Woodbury factorization:\nA:")
+    show(io, MIME("text/plain"), W.A)
+    print(io, "\nB:\n")
+    Base.print_matrix(IOContext(io,:compact=>true), W.B)
+    print(io, "\nD: ", W.D)
+end

src/WoodburyMatrices.jl

@@ -1,27 +1,19 @@
 __precompile__()
 
-
 module WoodburyMatrices
 
-using Compat
-using Compat.LinearAlgebra
-import Compat.LinearAlgebra: det, A_ldiv_B!
+using LinearAlgebra
+import LinearAlgebra: det, ldiv!, mul!, adjoint
 import Base: *, \, convert, copy, show, similar, size
 
-# TOOD: remove these definitions once Compat.jl catches up
-@static if VERSION <= v"0.7.0-DEV.3185"
-    const ldiv! = A_ldiv_B!
-    const mul! = A_mul_B!
-end
-
 export Woodbury, SymWoodbury, liftFactor
 
 #### Woodbury matrices ####
 """
 `W = Woodbury(A, U, C, V)` creates a matrix `W` identical to `A + U*C*V` whose inverse will be calculated using
 the Woodbury matrix identity.
 """
-mutable struct Woodbury{T,AType,UType,VType,CType,CpType} <: AbstractMatrix{T}
+struct Woodbury{T,AType,UType,VType,CType,CpType} <: Factorization{T}
     A::AType
     U::UType
     C::CType
@@ -46,10 +38,10 @@ function Woodbury(A, U::AbstractMatrix{T}, C, V::AbstractMatrix{T}) where {T}
     end
     Cp = inv(convert(Matrix, inv(C) .+ V*(A\U)))
     # temporary space for allocation-free solver
-    tmpN1 = Array{T,1}(uninitialized, N)
-    tmpN2 = Array{T,1}(uninitialized, N)
-    tmpk1 = Array{T,1}(uninitialized, k)
-    tmpk2 = Array{T,1}(uninitialized, k)
+    tmpN1 = Array{T,1}(undef, N)
+    tmpN2 = Array{T,1}(undef, N)
+    tmpk1 = Array{T,1}(undef, k)
+    tmpk2 = Array{T,1}(undef, k)
 
     # Construct the struct based on the types of the copies,
     # not the originals. See: https://github.com/JuliaLang/julia/issues/26294
@@ -58,27 +50,25 @@ function Woodbury(A, U::AbstractMatrix{T}, C, V::AbstractMatrix{T}) where {T}
 end
 
 Woodbury(A, U::Vector{T}, C, V::Matrix{T}) where {T} = Woodbury(A, reshape(U, length(U), 1), C, V)
-@static if VERSION <= v"0.7.0-DEV.3040"
-    Woodbury(A, U::AbstractVector, C, V::RowVector) = Woodbury(A, U, C, Matrix(V))
-else
-    Woodbury(A, U::AbstractVector, C, V::Adjoint) = Woodbury(A, U, C, Matrix(V))
-end
+
+Woodbury(A, U::AbstractVector, C, V::Adjoint) = Woodbury(A, U, C, Matrix(V))
 
 size(W::Woodbury) = size(W.A)
+size(W::Woodbury, d) = size(W.A, d)
 
 function show(io::IO, W::Woodbury)
-    println(io, summary(W), ":")
-    print(io, "A:\n", W.A)
+    println(io, "Woodbury factorization:\nA:")
+    show(io, MIME("text/plain"), W.A)
     print(io, "\nU:\n")
-    Base.print_matrix(io, W.U)
+    Base.print_matrix(IOContext(io, :compact=>true), W.U)
     if isa(W.C, Matrix)
         print(io, "\nC:\n")
-        Base.print_matrix(io, W.C)
+        Base.print_matrix(IOContext(io, :compact=>true), W.C)
     else
         print(io, "\nC: ", W.C)
     end
     print(io, "\nV:\n")
-    Base.print_matrix(io, W.V)
+    Base.print_matrix(IOContext(io, :compact=>true), W.V)
 end
 
 Base.Matrix(W::Woodbury{T}) where {T} = convert(Matrix{T}, W)
@@ -97,10 +87,10 @@ end
 
 det(W::Woodbury)=det(W.A)*det(W.C)/det(W.Cp)
 
-function A_ldiv_B!(W::Woodbury, B::AbstractVector)
+function ldiv!(W::Woodbury, B::AbstractVector)
     length(B) == size(W, 1) || throw(DimensionMismatch("Vector length $(length(B)) must match matrix size $(size(W,1))"))
     copyto!(W.tmpN1, B)
-    Alu = lufact(W.A) # Note. This makes an allocation (unless A::LU). Alternative is to destroy W.A.
+    Alu = lu(W.A) # Note. This makes an allocation (unless A::LU). Alternative is to destroy W.A.
     ldiv!(Alu, W.tmpN1)
     mul!(W.tmpk1, W.V, W.tmpN1)
     mul!(W.tmpk2, W.Cp, W.tmpk1)

test/runtests.jl

@@ -1,9 +1,6 @@
 using WoodburyMatrices
-using Compat
-using Compat.LinearAlgebra
-using Compat.Random: srand
-using Compat.SparseArrays
-using Compat.Test
+using LinearAlgebra, SparseArrays, Test
+using Random: srand
 
 @testset "WoodburyMatrices" begin
 srand(123)
@@ -56,7 +53,7 @@ for elty in (Float32, Float64, ComplexF32, ComplexF64, Int)
     @test abs((det(W) - det(F))/det(F)) <= n*cond(F)*ε # Revisit. Condition number is wrong
     iWv = similar(iFv)
     if elty != Int
-        iWv = A_ldiv_B!(W, copy(v))
+        iWv = ldiv!(W, copy(v))
         @test iWv ≈ iFv
     end
 end

test/runtests_sym.jl

@@ -1,6 +1,5 @@
-using Compat
-using Compat.Test
 using WoodburyMatrices
+using Test
 
 srand(123)
 n = 5