Improve svdfact(A, B) docs

base/docs/helpdb/Base.jl

@@ -1373,20 +1373,6 @@ for all `i` and `j`.
 """
 mapslices
 
-"""
-    svdvals(A)
-
-Returns the singular values of `A`.
-"""
-svdvals(A)
-
-"""
-    svdvals(A, B)
-
-Return only the singular values from the generalized singular value decomposition of `A` and `B`.
-"""
-svdvals(A, B)
-
 """
     issocket(path) -> Bool
 
@@ -1595,26 +1581,6 @@ of the problem that is solved on each processor.
 """
 peakflops
 
-"""
-    svd(A, [thin=true]) -> U, S, V
-
-Wrapper around `svdfact` extracting all parts the factorization to a tuple. Direct use of
-`svdfact` is therefore generally more efficient. Computes the SVD of `A`, returning `U`,
-vector `S`, and `V` such that `A == U*diagm(S)*V'`. If `thin` is `true`, an economy mode
-decomposition is returned. The default is to produce a thin decomposition.
-"""
-svd
-
-"""
-    svd(A, B) -> U, V, Q, D1, D2, R0
-
-Wrapper around `svdfact` extracting all parts the factorization to a tuple. Direct use of
-`svdfact` is therefore generally more efficient. The function returns the generalized SVD of
-`A` and `B`, returning `U`, `V`, `Q`, `D1`, `D2`, and `R0` such that `A = U*D1*R0*Q'` and `B =
-V*D2*R0*Q'`.
-"""
-svd(A::AbstractMatrix, B::AbstractMatrix)
-
 """
     ones(type, dims)
 
@@ -1774,13 +1740,6 @@ Sum absolute values of elements of an array over the given dimensions.
 """
 sumabs(A, dims)
 
-"""
-    svdvals!(A)
-
-Returns the singular values of `A`, while saving space by overwriting the input.
-"""
-svdvals!
-
 """
     consume(task, values...)
 
@@ -3002,25 +2961,6 @@ are itself and `None` (but `T` itself is not `None`).
 """
 isleaftype
 
-"""
-    svdfact(A, [thin=true]) -> SVD
-
-Compute the Singular Value Decomposition (SVD) of `A` and return an `SVD` object. `U`, `S`,
-`V` and `Vt` can be obtained from the factorization `F` with `F[:U]`, `F[:S]`, `F[:V]` and
-`F[:Vt]`, such that `A = U*diagm(S)*Vt`. If `thin` is `true`, an economy mode decomposition
-is returned. The algorithm produces `Vt` and hence `Vt` is more efficient to extract than
-`V`. The default is to produce a thin decomposition.
-"""
-svdfact(A)
-
-"""
-    svdfact(A, B) -> GeneralizedSVD
-
-Compute the generalized SVD of `A` and `B`, returning a `GeneralizedSVD` Factorization
-object `F`, such that `A = F[:U]*F[:D1]*F[:R0]*F[:Q]'` and `B = F[:V]*F[:D2]*F[:R0]*F[:Q]'`.
-"""
-svdfact(A, B)
-
 """
     string(xs...)
 
@@ -4578,15 +4518,6 @@ interrupt safe. Intended to be called using `do` block syntax as follows:
 """
 disable_sigint
 
-"""
-    svdfact!(A, [thin=true]) -> SVD
-
-`svdfact!` is the same as [`svdfact`](:func:`svdfact`), but saves space by overwriting the
-input `A`, instead of creating a copy. If `thin` is `true`, an economy mode decomposition is
-returned. The default is to produce a thin decomposition.
-"""
-svdfact!
-
 """
     hist2d(M, e1, e2) -> (edge1, edge2, counts)
 

base/linalg/svd.jl

@@ -18,13 +18,41 @@ function svdfact!{T<:BlasFloat}(A::StridedMatrix{T}; thin::Bool=true)
     end
     SVD(u,s,vt)
 end
-function svdfact{T}(A::StridedVecOrMat{T};thin = true)
+
+"""
+    svdfact(A, [thin=true]) -> SVD
+
+Compute the singular value decomposition (SVD) of `A` and return an `SVD` object.
+
+`U`, `S`, `V` and `Vt` can be obtained from the factorization `F` with `F[:U]`,
+`F[:S]`, `F[:V]` and `F[:Vt]`, such that `A = U*diagm(S)*Vt`.
+The algorithm produces `Vt` and hence `Vt` is more efficient to extract than `V`.
+
+If `thin=true` (default), a thin SVD is returned. For a ``M \\times N`` matrix
+`A`, `U` is ``M \\times M`` for a full SVD (`thin=false`) and
+``M \\times \\min(M, N)`` for a thin SVD.
+"""
+function svdfact{T}(A::StridedVecOrMat{T}; thin::Bool = true)
     S = promote_type(Float32, typeof(one(T)/norm(one(T))))
     svdfact!(copy_oftype(A, S), thin = thin)
 end
 svdfact(x::Number; thin::Bool=true) = SVD(x == 0 ? fill(one(x), 1, 1) : fill(x/abs(x), 1, 1), [abs(x)], fill(one(x), 1, 1))
 svdfact(x::Integer; thin::Bool=true) = svdfact(float(x), thin=thin)
 
+"""
+    svd(A, [thin=true]) -> U, S, V
+
+Computes the SVD of `A`, returning `U`, vector `S`, and `V` such that
+`A == U*diagm(S)*V'`.
+
+If `thin=true` (default), a thin SVD is returned. For a ``M \\times N`` matrix
+`A`, `U` is ``M \\times M`` for a full SVD (`thin=false`) and
+``M \\times \\min(M, N)`` for a thin SVD.
+
+`svd` is a wrapper around [`svdfact`](:func:`svdfact(A)`), extracting all parts
+of the `SVD` factorization to a tuple. Direct use of `svdfact` is therefore more
+efficient.
+"""
 function svd(A::Union{Number, AbstractArray}; thin::Bool=true)
     F = svdfact(A, thin=thin)
     F.U, F.S, F.Vt'
@@ -44,8 +72,19 @@ function getindex(F::SVD, d::Symbol)
     end
 end
 
+"""
+    svdvals!(A)
+
+Returns the singular values of `A`, saving space by overwriting the input.
+"""
 svdvals!{T<:BlasFloat}(A::StridedMatrix{T}) = any([size(A)...].==0) ? zeros(T, 0) : LAPACK.gesdd!('N', A)[2]
 svdvals{T<:BlasFloat}(A::AbstractMatrix{T}) = svdvals!(copy(A))
+
+"""
+    svdvals(A)
+
+Returns the singular values of `A`.
+"""
 function svdvals{T}(A::AbstractMatrix{T})
     S = promote_type(Float32, typeof(one(T)/norm(one(T))))
     svdvals!(copy_oftype(A, S))
@@ -73,6 +112,16 @@ immutable GeneralizedSVD{T,S} <: Factorization{T}
 end
 GeneralizedSVD{T}(U::AbstractMatrix{T}, V::AbstractMatrix{T}, Q::AbstractMatrix{T}, a::Vector, b::Vector, k::Int, l::Int, R::AbstractMatrix{T}) = GeneralizedSVD{T,typeof(U)}(U, V, Q, a, b, k, l, R)
 
+"""
+    svdfact!(A, [thin=true]) -> SVD
+
+`svdfact!` is the same as [`svdfact`](:func:`svdfact`), but saves space by
+overwriting the input `A`, instead of creating a copy.
+
+If `thin=true` (default), a thin SVD is returned. For a ``M \\times N`` matrix
+`A`, `U` is ``M \\times M`` for a full SVD (`thin=false`) and
+``M \\times \\min(M, N)`` for a thin SVD.
+"""
 function svdfact!{T<:BlasFloat}(A::StridedMatrix{T}, B::StridedMatrix{T})
     # xggsvd3 replaced xggsvd in LAPACK 3.6.0
     if LAPACK.VERSION[] < v"3.6.0"
@@ -83,11 +132,45 @@ function svdfact!{T<:BlasFloat}(A::StridedMatrix{T}, B::StridedMatrix{T})
     GeneralizedSVD(U, V, Q, a, b, Int(k), Int(l), R)
 end
 svdfact{T<:BlasFloat}(A::StridedMatrix{T}, B::StridedMatrix{T}) = svdfact!(copy(A),copy(B))
+
+"""
+    svdfact(A, B) -> GeneralizedSVD
+
+Compute the generalized SVD of `A` and `B`, returning a `GeneralizedSVD` factorization
+object `F`, such that `A = F[:U]*F[:D1]*F[:R0]*F[:Q]'` and `B = F[:V]*F[:D2]*F[:R0]*F[:Q]'`.
+
+For an M-by-N matrix `A` and P-by-N matrix `B`,
+
+- `F[:U]` is a M-by-M orthogonal matrix,
+- `F[:V]` is a P-by-P orthogonal matrix,
+- `F[:Q]` is a N-by-N orthogonal matrix,
+- `F[:R0]` is a (K+L)-by-N matrix whose rightmost (K+L)-by-(K+L) block is
+           nonsingular upper block triangular,
+- `F[:D1]` is a M-by-(K+L) diagonal matrix with 1s in the first K entries,
+- `F[:D2]` is a P-by-(K+L) matrix whose top right L-by-L block is diagonal,
+
+`K+L` is the effective numerical rank of the matrix `[A; B]`.
+
+The entries of `F[:D1]` and `F[:D2]` are related, as explained in the LAPACK
+documentation for the
+[generalized SVD](http://www.netlib.org/lapack/lug/node36.html) and the
+[xGGSVD3](http://www.netlib.org/lapack/explore-html/d6/db3/dggsvd3_8f.html)
+routine which is called underneath (in LAPACK 3.6.0 and newer).
+"""
 function svdfact{TA,TB}(A::StridedMatrix{TA}, B::StridedMatrix{TB})
     S = promote_type(Float32, typeof(one(TA)/norm(one(TA))),TB)
     return svdfact!(copy_oftype(A, S), copy_oftype(B, S))
 end
 
+"""
+    svd(A, B) -> U, V, Q, D1, D2, R0
+
+Wrapper around [`svdfact`](:func:`svdfact(A, B)`) extracting all parts of the
+factorization to a tuple. Direct use of
+`svdfact` is therefore generally more efficient. The function returns the generalized SVD of
+`A` and `B`, returning `U`, `V`, `Q`, `D1`, `D2`, and `R0` such that `A = U*D1*R0*Q'` and `B =
+V*D2*R0*Q'`.
+"""
 function svd(A::AbstractMatrix, B::AbstractMatrix)
     F = svdfact(A, B)
     F[:U], F[:V], F[:Q], F[:D1], F[:D2], F[:R0]
@@ -141,6 +224,13 @@ function svdvals!{T<:BlasFloat}(A::StridedMatrix{T}, B::StridedMatrix{T})
     a[1:k + l] ./ b[1:k + l]
 end
 svdvals{T<:BlasFloat}(A::StridedMatrix{T},B::StridedMatrix{T}) = svdvals!(copy(A),copy(B))
+
+"""
+    svdvals(A, B)
+
+Return the generalized singular values from the generalized singular value
+decomposition of `A` and `B`.
+"""
 function svdvals{TA,TB}(A::StridedMatrix{TA}, B::StridedMatrix{TB})
     S = promote_type(Float32, typeof(one(TA)/norm(one(TA))), TB)
     return svdvals!(copy_oftype(A, S), copy_oftype(B, S))

doc/stdlib/linalg.rst

@@ -586,19 +586,29 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    .. Docstring generated from Julia source
 
-   Compute the Singular Value Decomposition (SVD) of ``A`` and return an ``SVD`` object. ``U``\ , ``S``\ , ``V`` and ``Vt`` can be obtained from the factorization ``F`` with ``F[:U]``\ , ``F[:S]``\ , ``F[:V]`` and ``F[:Vt]``\ , such that ``A = U*diagm(S)*Vt``\ . If ``thin`` is ``true``\ , an economy mode decomposition is returned. The algorithm produces ``Vt`` and hence ``Vt`` is more efficient to extract than ``V``\ . The default is to produce a thin decomposition.
+   Compute the singular value decomposition (SVD) of ``A`` and return an ``SVD`` object.
+
+   ``U``\ , ``S``\ , ``V`` and ``Vt`` can be obtained from the factorization ``F`` with ``F[:U]``\ , ``F[:S]``\ , ``F[:V]`` and ``F[:Vt]``\ , such that ``A = U*diagm(S)*Vt``\ . The algorithm produces ``Vt`` and hence ``Vt`` is more efficient to extract than ``V``\ .
+
+   If ``thin=true`` (default), a thin SVD is returned. For a :math:`M \times N` matrix ``A``\ , ``U`` is :math:`M \times M` for a full SVD (``thin=false``\ ) and :math:`M \times \min(M, N)` for a thin SVD.
 
 .. function:: svdfact!(A, [thin=true]) -> SVD
 
    .. Docstring generated from Julia source
 
-   ``svdfact!`` is the same as :func:`svdfact`\ , but saves space by overwriting the input ``A``\ , instead of creating a copy. If ``thin`` is ``true``\ , an economy mode decomposition is returned. The default is to produce a thin decomposition.
+   ``svdfact!`` is the same as :func:`svdfact`\ , but saves space by overwriting the input ``A``\ , instead of creating a copy.
+
+   If ``thin=true`` (default), a thin SVD is returned. For a :math:`M \times N` matrix ``A``\ , ``U`` is :math:`M \times M` for a full SVD (``thin=false``\ ) and :math:`M \times \min(M, N)` for a thin SVD.
 
 .. function:: svd(A, [thin=true]) -> U, S, V
 
    .. Docstring generated from Julia source
 
-   Wrapper around ``svdfact`` extracting all parts the factorization to a tuple. Direct use of ``svdfact`` is therefore generally more efficient. Computes the SVD of ``A``\ , returning ``U``\ , vector ``S``\ , and ``V`` such that ``A == U*diagm(S)*V'``\ . If ``thin`` is ``true``\ , an economy mode decomposition is returned. The default is to produce a thin decomposition.
+   Computes the SVD of ``A``\ , returning ``U``\ , vector ``S``\ , and ``V`` such that ``A == U*diagm(S)*V'``\ .
+
+   If ``thin=true`` (default), a thin SVD is returned. For a :math:`M \times N` matrix ``A``\ , ``U`` is :math:`M \times M` for a full SVD (``thin=false``\ ) and :math:`M \times \min(M, N)` for a thin SVD.
+
+   ``svd`` is a wrapper around :func:`svdfact(A)`\ , extracting all parts of the ``SVD`` factorization to a tuple. Direct use of ``svdfact`` is therefore more efficient.
 
 .. function:: svdvals(A)
 
@@ -610,25 +620,38 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    .. Docstring generated from Julia source
 
-   Returns the singular values of ``A``\ , while saving space by overwriting the input.
+   Returns the singular values of ``A``\ , saving space by overwriting the input.
 
 .. function:: svdfact(A, B) -> GeneralizedSVD
 
    .. Docstring generated from Julia source
 
-   Compute the generalized SVD of ``A`` and ``B``\ , returning a ``GeneralizedSVD`` Factorization object ``F``\ , such that ``A = F[:U]*F[:D1]*F[:R0]*F[:Q]'`` and ``B = F[:V]*F[:D2]*F[:R0]*F[:Q]'``\ .
+   Compute the generalized SVD of ``A`` and ``B``\ , returning a ``GeneralizedSVD`` factorization object ``F``\ , such that ``A = F[:U]*F[:D1]*F[:R0]*F[:Q]'`` and ``B = F[:V]*F[:D2]*F[:R0]*F[:Q]'``\ .
+
+   For an M-by-N matrix ``A`` and P-by-N matrix ``B``\ ,
+
+   * ``F[:U]`` is a M-by-M orthogonal matrix,
+   * ``F[:V]`` is a P-by-P orthogonal matrix,
+   * ``F[:Q]`` is a N-by-N orthogonal matrix,
+   * ``F[:R0]`` is a (K+L)-by-N matrix whose rightmost (K+L)-by-(K+L) block is            nonsingular upper block triangular,
+   * ``F[:D1]`` is a M-by-(K+L) diagonal matrix with 1s in the first K entries,
+   * ``F[:D2]`` is a P-by-(K+L) matrix whose top right L-by-L block is diagonal,
+
+   ``K+L`` is the effective numerical rank of the matrix ``[A; B]``\ .
+
+   The entries of ``F[:D1]`` and ``F[:D2]`` are related, as explained in the LAPACK documentation for the `generalized SVD <http://www.netlib.org/lapack/lug/node36.html>`_ and the `xGGSVD3 <http://www.netlib.org/lapack/explore-html/d6/db3/dggsvd3_8f.html>`_ routine which is called underneath (in LAPACK 3.6.0 and newer).
 
 .. function:: svd(A, B) -> U, V, Q, D1, D2, R0
 
    .. Docstring generated from Julia source
 
-   Wrapper around ``svdfact`` extracting all parts the factorization to a tuple. Direct use of ``svdfact`` is therefore generally more efficient. The function returns the generalized SVD of ``A`` and ``B``\ , returning ``U``\ , ``V``\ , ``Q``\ , ``D1``\ , ``D2``\ , and ``R0`` such that ``A = U*D1*R0*Q'`` and ``B = V*D2*R0*Q'``\ .
+   Wrapper around :func:`svdfact(A, B)` extracting all parts of the factorization to a tuple. Direct use of ``svdfact`` is therefore generally more efficient. The function returns the generalized SVD of ``A`` and ``B``\ , returning ``U``\ , ``V``\ , ``Q``\ , ``D1``\ , ``D2``\ , and ``R0`` such that ``A = U*D1*R0*Q'`` and ``B = V*D2*R0*Q'``\ .
 
 .. function:: svdvals(A, B)
 
    .. Docstring generated from Julia source
 
-   Return only the singular values from the generalized singular value decomposition of ``A`` and ``B``\ .
+   Return the generalized singular values from the generalized singular value decomposition of ``A`` and ``B``\ .
 
 .. function:: givens{T}(::T, ::T, ::Integer, ::Integer) -> {Givens, T}