dense.jl

# This file is a part of Julia. License is MIT: http://julialang.org/license

# Linear algebra functions for dense matrices in column major format

## BLAS cutoff threshold constants

const SCAL_CUTOFF = 2048
const DOT_CUTOFF = 128
const ASUM_CUTOFF = 32
const NRM2_CUTOFF = 32

function scale!{T<:BlasFloat}(X::Array{T}, s::T)
    s == 0 && return fill!(X, zero(T))
    s == 1 && return X
    if length(X) < SCAL_CUTOFF
        generic_scale!(X, s)
    else
        BLAS.scal!(length(X), s, X, 1)
    end
    X
end

scale!{T<:BlasFloat}(s::T, X::Array{T}) = scale!(X, s)

scale!{T<:BlasFloat}(X::Array{T}, s::Number) = scale!(X, convert(T, s))
function scale!{T<:BlasComplex}(X::Array{T}, s::Real)
    R = typeof(real(zero(T)))
    BLAS.scal!(2*length(X), convert(R,s), convert(Ptr{R},pointer(X)), 1)
    X
end

#Test whether a matrix is positive-definite
isposdef!{T<:BlasFloat}(A::StridedMatrix{T}, UL::Symbol) = LAPACK.potrf!(char_uplo(UL), A)[2] == 0

"""
    isposdef!(A) -> Bool

Test whether a matrix is positive definite, overwriting `A` in the process.
"""
isposdef!(A::StridedMatrix) = ishermitian(A) && isposdef!(A, :U)

isposdef{T}(A::AbstractMatrix{T}, UL::Symbol) = (S = typeof(sqrt(one(T))); isposdef!(S == T ? copy(A) : convert(AbstractMatrix{S}, A), UL))

"""
    isposdef(A) -> Bool

Test whether a matrix is positive definite.

# Example

```jldoctest
julia> A = [1 2; 2 50]
2×2 Array{Int64,2}:
 1   2
 2  50

julia> isposdef(A)
true
```
"""
isposdef{T}(A::AbstractMatrix{T}) = (S = typeof(sqrt(one(T))); isposdef!(S == T ? copy(A) : convert(AbstractMatrix{S}, A)))
isposdef(x::Number) = imag(x)==0 && real(x) > 0

stride1(x::Array) = 1
stride1(x::StridedVector) = stride(x, 1)::Int

function norm{T<:BlasFloat, TI<:Integer}(x::StridedVector{T}, rx::Union{UnitRange{TI},Range{TI}})
    if minimum(rx) < 1 || maximum(rx) > length(x)
        throw(BoundsError(x, rx))
    end
    BLAS.nrm2(length(rx), pointer(x)+(first(rx)-1)*sizeof(T), step(rx))
end

vecnorm1{T<:BlasReal}(x::Union{Array{T},StridedVector{T}}) =
    length(x) < ASUM_CUTOFF ? generic_vecnorm1(x) : BLAS.asum(x)

vecnorm2{T<:BlasFloat}(x::Union{Array{T},StridedVector{T}}) =
    length(x) < NRM2_CUTOFF ? generic_vecnorm2(x) : BLAS.nrm2(x)

"""
    triu!(M, k::Integer)

Returns the upper triangle of `M` starting from the `k`th superdiagonal,
overwriting `M` in the process.

# Example
```jldoctest
julia> M = [1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5]
5×5 Array{Int64,2}:
 1  2  3  4  5
 1  2  3  4  5
 1  2  3  4  5
 1  2  3  4  5
 1  2  3  4  5

julia> triu!(M, 1)
5×5 Array{Int64,2}:
 0  2  3  4  5
 0  0  3  4  5
 0  0  0  4  5
 0  0  0  0  5
 0  0  0  0  0
```
"""
function triu!(M::AbstractMatrix, k::Integer)
    m, n = size(M)
    if (k > 0 && k > n) || (k < 0 && -k > m)
        throw(ArgumentError("requested diagonal, $k, out of bounds in matrix of size ($m,$n)"))
    end
    idx = 1
    for j = 0:n-1
        ii = min(max(0, j+1-k), m)
        for i = (idx+ii):(idx+m-1)
            M[i] = zero(M[i])
        end
        idx += m
    end
    M
end

triu(M::Matrix, k::Integer) = triu!(copy(M), k)

"""
    tril!(M, k::Integer)

Returns the lower triangle of `M` starting from the `k`th superdiagonal, overwriting `M` in
the process.

# Example

```jldoctest
julia> M = [1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5]
5×5 Array{Int64,2}:
 1  2  3  4  5
 1  2  3  4  5
 1  2  3  4  5
 1  2  3  4  5
 1  2  3  4  5

julia> tril!(M, 2)
5×5 Array{Int64,2}:
 1  2  3  0  0
 1  2  3  4  0
 1  2  3  4  5
 1  2  3  4  5
 1  2  3  4  5
```
"""
function tril!(M::AbstractMatrix, k::Integer)
    m, n = size(M)
    if (k > 0 && k > n) || (k < 0 && -k > m)
        throw(ArgumentError("requested diagonal, $k, out of bounds in matrix of size ($m,$n)"))
    end
    idx = 1
    for j = 0:n-1
        ii = min(max(0, j-k), m)
        for i = idx:(idx+ii-1)
            M[i] = zero(M[i])
        end
        idx += m
    end
    M
end
tril(M::Matrix, k::Integer) = tril!(copy(M), k)

function gradient(F::AbstractVector, h::Vector)
    n = length(F)
    T = typeof(oneunit(eltype(F))/oneunit(eltype(h)))
    g = similar(F, T)
    if n == 1
        g[1] = zero(T)
    elseif n > 1
        g[1] = (F[2] - F[1]) / (h[2] - h[1])
        g[n] = (F[n] - F[n-1]) / (h[end] - h[end-1])
        if n > 2
            h = h[3:n] - h[1:n-2]
            g[2:n-1] = (F[3:n] - F[1:n-2]) ./ h
        end
    end
    g
end

function diagind(m::Integer, n::Integer, k::Integer=0)
    if !(-m <= k <= n)
        throw(ArgumentError("requested diagonal, $k, out of bounds in matrix of size ($m,$n)"))
    end
    k <= 0 ? range(1-k, m+1, min(m+k, n)) : range(k*m+1, m+1, min(m, n-k))
end

"""
    diagind(M, k::Integer=0)

A `Range` giving the indices of the `k`th diagonal of the matrix `M`.

# Example

```jldoctest
julia> A = [1 2 3; 4 5 6; 7 8 9]
3×3 Array{Int64,2}:
 1  2  3
 4  5  6
 7  8  9

julia> diagind(A,-1)
2:4:6
```
"""
diagind(A::AbstractMatrix, k::Integer=0) = diagind(size(A,1), size(A,2), k)

"""
    diag(M, k::Integer=0)

The `k`th diagonal of a matrix, as a vector.
Use [`diagm`](@ref) to construct a diagonal matrix.

# Example

```jldoctest
julia> A = [1 2 3; 4 5 6; 7 8 9]
3×3 Array{Int64,2}:
 1  2  3
 4  5  6
 7  8  9

julia> diag(A,1)
2-element Array{Int64,1}:
 2
 6
```
"""
diag(A::AbstractMatrix, k::Integer=0) = A[diagind(A,k)]

"""
    diagm(v, k::Integer=0)

Construct a matrix by placing `v` on the `k`th diagonal.

# Example

```jldoctest
julia> diagm([1,2,3],1)
4×4 Array{Int64,2}:
 0  1  0  0
 0  0  2  0
 0  0  0  3
 0  0  0  0
```
"""
function diagm{T}(v::AbstractVector{T}, k::Integer=0)
    n = length(v) + abs(k)
    A = zeros(T,n,n)
    A[diagind(A,k)] = v
    A
end

diagm(x::Number) = (X = Array{typeof(x)}(1,1); X[1,1] = x; X)

function trace{T}(A::Matrix{T})
    n = checksquare(A)
    t = zero(T)
    for i=1:n
        t += A[i,i]
    end
    t
end

"""
    kron(A, B)

Kronecker tensor product of two vectors or two matrices.

# Example

```jldoctest
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
 1  2
 3  4

julia> B = [im 1; 1 -im]
2×2 Array{Complex{Int64},2}:
 0+1im  1+0im
 1+0im  0-1im

julia> kron(A, B)
4×4 Array{Complex{Int64},2}:
 0+1im  1+0im  0+2im  2+0im
 1+0im  0-1im  2+0im  0-2im
 0+3im  3+0im  0+4im  4+0im
 3+0im  0-3im  4+0im  0-4im
```
"""
function kron{T,S}(a::AbstractMatrix{T}, b::AbstractMatrix{S})
    R = Array{promote_op(*,T,S)}(size(a,1)*size(b,1), size(a,2)*size(b,2))
    m = 1
    for j = 1:size(a,2), l = 1:size(b,2), i = 1:size(a,1)
        aij = a[i,j]
        for k = 1:size(b,1)
            R[m] = aij*b[k,l]
            m += 1
        end
    end
    R
end

kron(a::Number, b::Union{Number, AbstractVecOrMat}) = a * b
kron(a::AbstractVecOrMat, b::Number) = a * b
kron(a::AbstractVector, b::AbstractVector)=vec(kron(reshape(a,length(a),1),reshape(b,length(b),1)))
kron(a::AbstractMatrix, b::AbstractVector)=kron(a,reshape(b,length(b),1))
kron(a::AbstractVector, b::AbstractMatrix)=kron(reshape(a,length(a),1),b)

^(A::AbstractMatrix, p::Integer) = p < 0 ? inv(A^-p) : Base.power_by_squaring(A,p)

function ^(A::AbstractMatrix, p::Number)
    if isinteger(p)
        return A^Integer(real(p))
    end
    checksquare(A)
    v, X = eig(A)
    any(v.<0) && (v = complex(v))
    Xinv = ishermitian(A) ? X' : inv(X)
    (X * Diagonal(v.^p)) * Xinv
end

# Matrix exponential

"""
    expm(A)

Compute the matrix exponential of `A`, defined by

```math
e^A = \\sum_{n=0}^{\\infty} \\frac{A^n}{n!}.
```

For symmetric or Hermitian `A`, an eigendecomposition ([`eigfact`](@ref)) is
used, otherwise the scaling and squaring algorithm (see [^H05]) is chosen.

[^H05]: Nicholas J. Higham, "The squaring and scaling method for the matrix exponential revisited", SIAM Journal on Matrix Analysis and Applications, 26(4), 2005, 1179-1193. [doi:10.1137/090768539](http://dx.doi.org/10.1137/090768539)

# Example

```jldoctest
julia> A = eye(2, 2)
2×2 Array{Float64,2}:
 1.0  0.0
 0.0  1.0

julia> expm(A)
2×2 Array{Float64,2}:
 2.71828  0.0
 0.0      2.71828
```
"""
expm{T<:BlasFloat}(A::StridedMatrix{T}) = expm!(copy(A))
expm{T<:Integer}(A::StridedMatrix{T}) = expm!(float(A))
expm(x::Number) = exp(x)

## Destructive matrix exponential using algorithm from Higham, 2008,
## "Functions of Matrices: Theory and Computation", SIAM
function expm!{T<:BlasFloat}(A::StridedMatrix{T})
    n = checksquare(A)
    if ishermitian(A)
        return full(expm(Hermitian(A)))
    end
    ilo, ihi, scale = LAPACK.gebal!('B', A)    # modifies A
    nA   = norm(A, 1)
    I    = eye(T,n)
    ## For sufficiently small nA, use lower order Padé-Approximations
    if (nA <= 2.1)
        if nA > 0.95
            C = T[17643225600.,8821612800.,2075673600.,302702400.,
                     30270240.,   2162160.,    110880.,     3960.,
                           90.,         1.]
        elseif nA > 0.25
            C = T[17297280.,8648640.,1995840.,277200.,
                     25200.,   1512.,     56.,     1.]
        elseif nA > 0.015
            C = T[30240.,15120.,3360.,
                    420.,   30.,   1.]
        else
            C = T[120.,60.,12.,1.]
        end
        A2 = A * A
        P  = copy(I)
        U  = C[2] * P
        V  = C[1] * P
        for k in 1:(div(size(C, 1), 2) - 1)
            k2 = 2 * k
            P *= A2
            U += C[k2 + 2] * P
            V += C[k2 + 1] * P
        end
        U = A * U
        X = V + U
        LAPACK.gesv!(V-U, X)
    else
        s  = log2(nA/5.4)               # power of 2 later reversed by squaring
        if s > 0
            si = ceil(Int,s)
            A /= convert(T,2^si)
        end
        CC = T[64764752532480000.,32382376266240000.,7771770303897600.,
                1187353796428800.,  129060195264000.,  10559470521600.,
                    670442572800.,      33522128640.,      1323241920.,
                        40840800.,           960960.,           16380.,
                             182.,                1.]
        A2 = A * A
        A4 = A2 * A2
        A6 = A2 * A4
        U  = A * (A6 * (CC[14]*A6 + CC[12]*A4 + CC[10]*A2) +
                  CC[8]*A6 + CC[6]*A4 + CC[4]*A2 + CC[2]*I)
        V  = A6 * (CC[13]*A6 + CC[11]*A4 + CC[9]*A2) +
                   CC[7]*A6 + CC[5]*A4 + CC[3]*A2 + CC[1]*I

        X = V + U
        LAPACK.gesv!(V-U, X)

        if s > 0            # squaring to reverse dividing by power of 2
            for t=1:si; X *= X end
        end
    end

    # Undo the balancing
    for j = ilo:ihi
        scj = scale[j]
        for i = 1:n
            X[j,i] *= scj
        end
        for i = 1:n
            X[i,j] /= scj
        end
    end

    if ilo > 1       # apply lower permutations in reverse order
        for j in (ilo-1):-1:1; rcswap!(j, Int(scale[j]), X) end
    end
    if ihi < n       # apply upper permutations in forward order
        for j in (ihi+1):n;    rcswap!(j, Int(scale[j]), X) end
    end
    X
end

## Swap rows i and j and columns i and j in X
function rcswap!{T<:Number}(i::Integer, j::Integer, X::StridedMatrix{T})
    for k = 1:size(X,1)
        X[k,i], X[k,j] = X[k,j], X[k,i]
    end
    for k = 1:size(X,2)
        X[i,k], X[j,k] = X[j,k], X[i,k]
    end
end

"""
    logm(A::StridedMatrix)

If `A` has no negative real eigenvalue, compute the principal matrix logarithm of `A`, i.e.
the unique matrix ``X`` such that ``e^X = A`` and ``-\\pi < Im(\\lambda) < \\pi`` for all
the eigenvalues ``\\lambda`` of ``X``. If `A` has nonpositive eigenvalues, a nonprincipal
matrix function is returned whenever possible.

If `A` is symmetric or Hermitian, its eigendecomposition ([`eigfact`](@ref)) is
used, if `A` is triangular an improved version of the inverse scaling and squaring method is
employed (see [^AH12] and [^AHR13]). For general matrices, the complex Schur form
([`schur`](@ref)) is computed and the triangular algorithm is used on the
triangular factor.

[^AH12]: Awad H. Al-Mohy and Nicholas J. Higham, "Improved inverse  scaling and squaring algorithms for the matrix logarithm", SIAM Journal on Scientific Computing, 34(4), 2012, C153-C169. [doi:10.1137/110852553](http://dx.doi.org/10.1137/110852553)

[^AHR13]: Awad H. Al-Mohy, Nicholas J. Higham and Samuel D. Relton, "Computing the Fréchet derivative of the matrix logarithm and estimating the condition number", SIAM Journal on Scientific Computing, 35(4), 2013, C394-C410. [doi:10.1137/120885991](http://dx.doi.org/10.1137/120885991)

# Example

```jldoctest
julia> A = 2.7182818 * eye(2)
2×2 Array{Float64,2}:
 2.71828  0.0
 0.0      2.71828

julia> logm(A)
2×2 Array{Float64,2}:
 1.0  0.0
 0.0  1.0
```
"""
function logm(A::StridedMatrix)
    # If possible, use diagonalization
    if ishermitian(A)
        return full(logm(Hermitian(A)))
    end

    # Use Schur decomposition
    n = checksquare(A)
    if istriu(A)
        retmat = full(logm(UpperTriangular(complex(A))))
        d = diag(A)
    else
        S,Q,d = schur(complex(A))
        R = logm(UpperTriangular(S))
        retmat = Q * R * Q'
    end

    # Check whether the matrix has nonpositive real eigs
    np_real_eigs = false
    for i = 1:n
        if imag(d[i]) < eps() && real(d[i]) <= 0
            np_real_eigs = true
            break
        end
    end

    if isreal(A) && !np_real_eigs
        return real(retmat)
    else
        return retmat
    end
end
function logm(a::Number)
    b = log(complex(a))
    return imag(b) == 0 ? real(b) : b
end
logm(a::Complex) = log(a)

"""
    sqrtm(A)

If `A` has no negative real eigenvalues, compute the principal matrix square root of `A`,
that is the unique matrix ``X`` with eigenvalues having positive real part such that
``X^2 = A``. Otherwise, a nonprincipal square root is returned.

If `A` is symmetric or Hermitian, its eigendecomposition ([`eigfact`](@ref)) is
used to compute the square root. Otherwise, the square root is determined by means of the
Björck-Hammarling method [^BH83], which computes the complex Schur form ([`schur`](@ref))
and then the complex square root of the triangular factor.

[^BH83]:

    Åke Björck and Sven Hammarling, "A Schur method for the square root of a matrix",
    Linear Algebra and its Applications, 52-53, 1983, 127-140.
    [doi:10.1016/0024-3795(83)80010-X](http://dx.doi.org/10.1016/0024-3795(83)80010-X)

# Example

```jldoctest
julia> A = [4 0; 0 4]
2×2 Array{Int64,2}:
 4  0
 0  4

julia> sqrtm(A)
2×2 Array{Float64,2}:
 2.0  0.0
 0.0  2.0
```
"""
function sqrtm{T<:Real}(A::StridedMatrix{T})
    if issymmetric(A)
        return full(sqrtm(Symmetric(A)))
    end
    n = checksquare(A)
    if istriu(A)
        return full(sqrtm(UpperTriangular(A)))
    else
        SchurF = schurfact(complex(A))
        R = full(sqrtm(UpperTriangular(SchurF[:T])))
        return SchurF[:vectors] * R * SchurF[:vectors]'
    end
end
function sqrtm{T<:Complex}(A::StridedMatrix{T})
    if ishermitian(A)
        return full(sqrtm(Hermitian(A)))
    end
    n = checksquare(A)
    if istriu(A)
        return full(sqrtm(UpperTriangular(A)))
    else
        SchurF = schurfact(A)
        R = full(sqrtm(UpperTriangular(SchurF[:T])))
        return SchurF[:vectors] * R * SchurF[:vectors]'
    end
end
sqrtm(a::Number) = (b = sqrt(complex(a)); imag(b) == 0 ? real(b) : b)
sqrtm(a::Complex) = sqrt(a)

function inv{T}(A::StridedMatrix{T})
    checksquare(A)
    S = typeof((one(T)*zero(T) + one(T)*zero(T))/one(T))
    AA = convert(AbstractArray{S}, A)
    if istriu(AA)
        Ai = inv(UpperTriangular(AA))
    elseif istril(AA)
        Ai = inv(LowerTriangular(AA))
    else
        Ai = inv(lufact(AA))
    end
    return convert(typeof(parent(Ai)), Ai)
end

"""
    factorize(A)

Compute a convenient factorization of `A`, based upon the type of the input matrix.
`factorize` checks `A` to see if it is symmetric/triangular/etc. if `A` is passed
as a generic matrix. `factorize` checks every element of `A` to verify/rule out
each property. It will short-circuit as soon as it can rule out symmetry/triangular
structure. The return value can be reused for efficient solving of multiple
systems. For example: `A=factorize(A); x=A\\b; y=A\\C`.

| Properties of `A`          | type of factorization                          |
|:---------------------------|:-----------------------------------------------|
| Positive-definite          | Cholesky (see [`cholfact`](@ref))  |
| Dense Symmetric/Hermitian  | Bunch-Kaufman (see [`bkfact`](@ref)) |
| Sparse Symmetric/Hermitian | LDLt (see [`ldltfact`](@ref))      |
| Triangular                 | Triangular                                     |
| Diagonal                   | Diagonal                                       |
| Bidiagonal                 | Bidiagonal                                     |
| Tridiagonal                | LU (see [`lufact`](@ref))            |
| Symmetric real tridiagonal | LDLt (see [`ldltfact`](@ref))      |
| General square             | LU (see [`lufact`](@ref))            |
| General non-square         | QR (see [`qrfact`](@ref))            |

If `factorize` is called on a Hermitian positive-definite matrix, for instance, then `factorize`
will return a Cholesky factorization.

# Example

```jldoctest
julia> A = Array(Bidiagonal(ones(5, 5), true))
5×5 Array{Float64,2}:
 1.0  1.0  0.0  0.0  0.0
 0.0  1.0  1.0  0.0  0.0
 0.0  0.0  1.0  1.0  0.0
 0.0  0.0  0.0  1.0  1.0
 0.0  0.0  0.0  0.0  1.0

julia> factorize(A) # factorize will check to see that A is already factorized
5×5 Bidiagonal{Float64}:
 1.0  1.0   ⋅    ⋅    ⋅
  ⋅   1.0  1.0   ⋅    ⋅
  ⋅    ⋅   1.0  1.0   ⋅
  ⋅    ⋅    ⋅   1.0  1.0
  ⋅    ⋅    ⋅    ⋅   1.0
```
This returns a `5×5 Bidiagonal{Float64}`, which can now be passed to other linear algebra functions
(e.g. eigensolvers) which will use specialized methods for `Bidiagonal` types.
"""
function factorize{T}(A::StridedMatrix{T})
    m, n = size(A)
    if m == n
        if m == 1 return A[1] end
        utri    = true
        utri1   = true
        herm    = true
        sym     = true
        for j = 1:n-1, i = j+1:m
            if utri1
                if A[i,j] != 0
                    utri1 = i == j + 1
                    utri = false
                end
            end
            if sym
                sym &= A[i,j] == A[j,i]
            end
            if herm
                herm &= A[i,j] == conj(A[j,i])
            end
            if !(utri1|herm|sym) break end
        end
        ltri = true
        ltri1 = true
        for j = 3:n, i = 1:j-2
            ltri1 &= A[i,j] == 0
            if !ltri1 break end
        end
        if ltri1
            for i = 1:n-1
                if A[i,i+1] != 0
                    ltri &= false
                    break
                end
            end
            if ltri
                if utri
                    return Diagonal(A)
                end
                if utri1
                    return Bidiagonal(diag(A), diag(A, -1), false)
                end
                return LowerTriangular(A)
            end
            if utri
                return Bidiagonal(diag(A), diag(A, 1), true)
            end
            if utri1
                if (herm & (T <: Complex)) | sym
                    try
                        return ldltfact!(SymTridiagonal(diag(A), diag(A, -1)))
                    end
                end
                return lufact(Tridiagonal(diag(A, -1), diag(A), diag(A, 1)))
            end
        end
        if utri
            return UpperTriangular(A)
        end
        if herm
            try
                return cholfact(A)
            end
            return factorize(Hermitian(A))
        end
        if sym
            return factorize(Symmetric(A))
        end
        return lufact(A)
    end
    qrfact(A, Val{true})
end

## Moore-Penrose pseudoinverse

"""
    pinv(M[, tol::Real])

Computes the Moore-Penrose pseudoinverse.

For matrices `M` with floating point elements, it is convenient to compute
the pseudoinverse by inverting only singular values above a given threshold,
`tol`.

The optimal choice of `tol` varies both with the value of `M` and the intended application
of the pseudoinverse. The default value of `tol` is
`eps(real(float(one(eltype(M)))))*maximum(size(A))`, which is essentially machine epsilon
for the real part of a matrix element multiplied by the larger matrix dimension. For
inverting dense ill-conditioned matrices in a least-squares sense,
`tol = sqrt(eps(real(float(one(eltype(M))))))` is recommended.

For more information, see [^issue8859], [^B96], [^S84], [^KY88].

# Example

```jldoctest
julia> M = [1.5 1.3; 1.2 1.9]
2×2 Array{Float64,2}:
 1.5  1.3
 1.2  1.9

julia> N = pinv(M)
2×2 Array{Float64,2}:
  1.47287   -1.00775
 -0.930233   1.16279

julia> M * N
2×2 Array{Float64,2}:
 1.0          -2.22045e-16
 4.44089e-16   1.0
```

[^issue8859]: Issue 8859, "Fix least squares", https://github.com/JuliaLang/julia/pull/8859

[^B96]: Åke Björck, "Numerical Methods for Least Squares Problems",  SIAM Press, Philadelphia, 1996, "Other Titles in Applied Mathematics", Vol. 51. [doi:10.1137/1.9781611971484](http://epubs.siam.org/doi/book/10.1137/1.9781611971484)

[^S84]: G. W. Stewart, "Rank Degeneracy", SIAM Journal on Scientific and Statistical Computing, 5(2), 1984, 403-413. [doi:10.1137/0905030](http://epubs.siam.org/doi/abs/10.1137/0905030)

[^KY88]: Konstantinos Konstantinides and Kung Yao, "Statistical analysis of effective singular values in matrix rank determination", IEEE Transactions on Acoustics, Speech and Signal Processing, 36(5), 1988, 757-763. [doi:10.1109/29.1585](http://dx.doi.org/10.1109/29.1585)
"""
function pinv{T}(A::StridedMatrix{T}, tol::Real)
    m, n = size(A)
    Tout = typeof(zero(T)/sqrt(one(T) + one(T)))
    if m == 0 || n == 0
        return Array{Tout}(n, m)
    end
    if istril(A)
        if istriu(A)
            maxabsA = maximum(abs.(diag(A)))
            B = zeros(Tout, n, m)
            for i = 1:min(m, n)
                if abs(A[i,i]) > tol*maxabsA
                    Aii = inv(A[i,i])
                    if isfinite(Aii)
                        B[i,i] = Aii
                    end
                end
            end
            return B
        end
    end
    SVD         = svdfact(A, thin=true)
    Stype       = eltype(SVD.S)
    Sinv        = zeros(Stype, length(SVD.S))
    index       = SVD.S .> tol*maximum(SVD.S)
    Sinv[index] = one(Stype) ./ SVD.S[index]
    Sinv[find(!isfinite.(Sinv))] = zero(Stype)
    return SVD.Vt' * (Diagonal(Sinv) * SVD.U')
end
function pinv{T}(A::StridedMatrix{T})
    tol = eps(real(float(one(T))))*maximum(size(A))
    return pinv(A, tol)
end
pinv(a::StridedVector) = pinv(reshape(a, length(a), 1))
function pinv(x::Number)
    xi = inv(x)
    return ifelse(isfinite(xi), xi, zero(xi))
end

## Basis for null space

"""
    nullspace(M)

Basis for nullspace of `M`.

# Example

```jldoctest
julia> M = [1 0 0; 0 1 0; 0 0 0]
3×3 Array{Int64,2}:
 1  0  0
 0  1  0
 0  0  0

julia> nullspace(M)
3×1 Array{Float64,2}:
 0.0
 0.0
 1.0
```
"""
function nullspace{T}(A::StridedMatrix{T})
    m, n = size(A)
    (m == 0 || n == 0) && return eye(T, n)
    SVD = svdfact(A, thin = false)
    indstart = sum(SVD.S .> max(m,n)*maximum(SVD.S)*eps(eltype(SVD.S))) + 1
    return SVD.Vt[indstart:end,:]'
end
nullspace(a::StridedVector) = nullspace(reshape(a, length(a), 1))

"""
    cond(M, p::Real=2)

Condition number of the matrix `M`, computed using the operator `p`-norm. Valid values for
`p` are `1`, `2` (default), or `Inf`.
"""
function cond(A::AbstractMatrix, p::Real=2)
    if p == 2
        v = svdvals(A)
        maxv = maximum(v)
        return maxv == 0.0 ? oftype(real(A[1,1]),Inf) : maxv / minimum(v)
    elseif p == 1 || p == Inf
        checksquare(A)
        return cond(lufact(A), p)
    end
    throw(ArgumentError("p-norm must be 1, 2 or Inf, got $p"))
end

## Lyapunov and Sylvester equation

# AX + XB + C = 0

"""
    sylvester(A, B, C)

Computes the solution `X` to the Sylvester equation `AX + XB + C = 0`, where `A`, `B` and
`C` have compatible dimensions and `A` and `-B` have no eigenvalues with equal real part.
"""
function sylvester{T<:BlasFloat}(A::StridedMatrix{T},B::StridedMatrix{T},C::StridedMatrix{T})
    RA, QA = schur(A)
    RB, QB = schur(B)

    D = -Ac_mul_B(QA,C*QB)
    Y, scale = LAPACK.trsyl!('N','N', RA, RB, D)
    scale!(QA*A_mul_Bc(Y,QB), inv(scale))
end
sylvester{T<:Integer}(A::StridedMatrix{T},B::StridedMatrix{T},C::StridedMatrix{T}) = sylvester(float(A), float(B), float(C))

sylvester(a::Union{Real,Complex},b::Union{Real,Complex},c::Union{Real,Complex}) = -c / (a + b)

# AX + XA' + C = 0

"""
    lyap(A, C)

Computes the solution `X` to the continuous Lyapunov equation `AX + XA' + C = 0`, where no
eigenvalue of `A` has a zero real part and no two eigenvalues are negative complex
conjugates of each other.
"""
function lyap{T<:BlasFloat}(A::StridedMatrix{T},C::StridedMatrix{T})
    R, Q = schur(A)

    D = -Ac_mul_B(Q,C*Q)
    Y, scale = LAPACK.trsyl!('N', T <: Complex ? 'C' : 'T', R, R, D)
    scale!(Q*A_mul_Bc(Y,Q), inv(scale))
end
lyap{T<:Integer}(A::StridedMatrix{T},C::StridedMatrix{T}) = lyap(float(A), float(C))
lyap{T<:Number}(a::T, c::T) = -c/(2a)