eliminate uses of full from base/linalg/lq.jl

base/linalg/lq.jl

@@ -38,11 +38,12 @@ lqfact(x::Number) = lqfact(fill(x,1,1))
 Perform an LQ factorization of `A` such that `A = L*Q`. The
 default is to compute a thin factorization. The LQ factorization
 is the QR factorization of `A.'`. `L` is not extended with
-zeros if the full `Q` is requested.
+zeros if the explicit, square form of `Q` is requested via `thin = false`.
 """
-function lq(A::Union{Number, AbstractMatrix}; thin::Bool=true)
+function lq(A::Union{Number,AbstractMatrix}; thin::Bool = true)
     F = lqfact(A)
-    F[:L], full(F[:Q], thin=thin)
+    L, Q = F[:L], F[:Q]
+    return L, thin ? Array(Q) : A_mul_B!(Q, eye(eltype(Q), size(Q.factors, 2)))
 end
 
 copy(A::LQ) = LQ(copy(A.factors), copy(A.τ))
@@ -90,19 +91,9 @@ convert(::Type{LQPackedQ{T}}, Q::LQPackedQ) where {T} = LQPackedQ(convert(Abstra
 convert(::Type{AbstractMatrix{T}}, Q::LQPackedQ) where {T} = convert(LQPackedQ{T}, Q)
 convert(::Type{Matrix}, A::LQPackedQ) = LAPACK.orglq!(copy(A.factors),A.τ)
 convert(::Type{Array}, A::LQPackedQ) = convert(Matrix, A)
-function full(A::LQPackedQ{T}; thin::Bool = true) where T
-    #= We construct the full eye here, even though it seems inefficient, because
-    every element in the output matrix is a function of all the elements of
-    the input matrix. The eye is modified by the elementary reflectors held
-    in A, so this is not just an indexing operation. Note that in general
-    explicitly constructing Q, rather than using the ldiv or mult methods,
-    may be a wasteful allocation. =#
-    if thin
-        convert(Array, A)
-    else
-        A_mul_B!(A, eye(T, size(A.factors,2), size(A.factors,1)))
-    end
-end
+
+full(Q::LQPackedQ; thin::Bool = true) =
+    thin ? Array(Q) : A_mul_B!(Q, eye(eltype(Q), size(Q.factors, 2)))
 
 size(A::LQ, dim::Integer) = size(A.factors, dim)
 size(A::LQ) = size(A.factors)
@@ -119,9 +110,12 @@ end
 size(A::LQPackedQ) = size(A.factors)
 
 ## Multiplication by LQ
-A_mul_B!(A::LQ{T}, B::StridedVecOrMat{T}) where {T<:BlasFloat} = A[:L]*LAPACK.ormlq!('L','N',A.factors,A.τ,B)
-A_mul_B!(A::LQ{T}, B::QR{T}) where {T<:BlasFloat} = A[:L]*LAPACK.ormlq!('L','N',A.factors,A.τ,full(B))
-A_mul_B!(A::QR{T}, B::LQ{T}) where {T<:BlasFloat} = A_mul_B!(zeros(full(A)), full(A), full(B))
+A_mul_B!(A::LQ{T}, B::StridedVecOrMat{T}) where {T<:BlasFloat} =
+    A[:L] * LAPACK.ormlq!('L', 'N', A.factors, A.τ, B)
+A_mul_B!(A::LQ{T}, B::QR{T}) where {T<:BlasFloat} =
+    A[:L] * LAPACK.ormlq!('L', 'N', A.factors, A.τ, Matrix(B))
+A_mul_B!(A::QR{T}, B::LQ{T}) where {T<:BlasFloat} =
+    A_mul_B!(zeros(eltype(A), size(A)), Matrix(A), Matrix(B))
 function *(A::LQ{TA}, B::StridedVecOrMat{TB}) where {TA,TB}
     TAB = promote_type(TA, TB)
     A_mul_B!(convert(Factorization{TAB},A), copy_oftype(B, TAB))

test/linalg/lq.jl

@@ -95,11 +95,34 @@ bimg  = randn(n,2)/2
             lqa = lqfact(a[:,1:n1])
             l,q = lqa[:L], lqa[:Q]
             @test full(q)*full(q)' ≈ eye(eltya,n1)
-            @test (full(q,thin=false)'*full(q,thin=false))[1:n1,:] ≈ eye(eltya,n1,n)
+            @test full(q,thin=false)'*full(q,thin=false) ≈ eye(eltya, n1)
             @test_throws DimensionMismatch A_mul_B!(eye(eltya,n+1),q)
             @test Ac_mul_B!(q,full(q)) ≈ eye(eltya,n1)
             @test_throws DimensionMismatch A_mul_Bc!(eye(eltya,n+1),q)
             @test_throws BoundsError size(q,-1)
         end
     end
 end
+
+@testset "correct form of Q from lq(...) (#23729)" begin
+    # matrices with more rows than columns
+    m, n = 4, 2
+    A = randn(m, n)
+    for thin in (true, false)
+        L, Q = lq(A, thin = thin)
+        @test size(L) == (m, n)
+        @test size(Q) == (n, n)
+        @test isapprox(A, L*Q)
+    end
+    # matrices with more columns than rows
+    m, n = 2, 4
+    A = randn(m, n)
+    Lthin, Qthin = lq(A, thin = true)
+    @test size(Lthin) == (m, m)
+    @test size(Qthin) == (m, n)
+    @test isapprox(A, Lthin * Qthin)
+    Lsquare, Qsquare = lq(A, thin = false)
+    @test size(Lsquare) == (m, m)
+    @test size(Qsquare) == (n, n)
+    @test isapprox(A, [Lsquare zeros(m, n - m)] * Qsquare)
+end