Class: Matrix::EigenvalueDecomposition

# File 'lib/matrix/eigenvalue_decomposition.rb', line 100

def cdiv(xr, xi, yr, yi)
  if (yr.abs > yi.abs)
    r = yi/yr
    d = yr + r*yi
    [(xr + r*xi)/d, (xi - r*xr)/d]
  else
    r = yr/yi
    d = yi + r*yr
    [(r*xr + xi)/d, (r*xi - xr)/d]
  end
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 75

alias d eigenvalue_matrix

# File 'lib/matrix/eigenvalue_decomposition.rb', line 233

def diagonalize
  #  This is derived from the Algol procedures tql2, by
  #  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
  #  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
  #  Fortran subroutine in EISPACK.

  1.upto(@size-1) do |i|
    @e[i-1] = @e[i]
  end
  @e[@size-1] = 0.0

  f = 0.0
  tst1 = 0.0
  eps = Float::EPSILON
  @size.times do |l|

    # Find small subdiagonal element

    tst1 = [tst1, @d[l].abs + @e[l].abs].max
    m = l
    while (m < @size) do
      if (@e[m].abs <= eps*tst1)
        break
      end
      m+=1
    end

    # If m == l, @d[l] is an eigenvalue,
    # otherwise, iterate.

    if (m > l)
      iter = 0
      begin
        iter = iter + 1  # (Could check iteration count here.)

        # Compute implicit shift

        g = @d[l]
        p = (@d[l+1] - g) / (2.0 * @e[l])
        r = Math.hypot(p, 1.0)
        if (p < 0)
          r = -r
        end
        @d[l] = @e[l] / (p + r)
        @d[l+1] = @e[l] * (p + r)
        dl1 = @d[l+1]
        h = g - @d[l]
        (l+2).upto(@size-1) do |i|
          @d[i] -= h
        end
        f += h

        # Implicit QL transformation.

        p = @d[m]
        c = 1.0
        c2 = c
        c3 = c
        el1 = @e[l+1]
        s = 0.0
        s2 = 0.0
        (m-1).downto(l) do |i|
          c3 = c2
          c2 = c
          s2 = s
          g = c * @e[i]
          h = c * p
          r = Math.hypot(p, @e[i])
          @e[i+1] = s * r
          s = @e[i] / r
          c = p / r
          p = c * @d[i] - s * g
          @d[i+1] = h + s * (c * g + s * @d[i])

          # Accumulate transformation.

          @size.times do |k|
            h = @v[k][i+1]
            @v[k][i+1] = s * @v[k][i] + c * h
            @v[k][i] = c * @v[k][i] - s * h
          end
        end
        p = -s * s2 * c3 * el1 * @e[l] / dl1
        @e[l] = s * p
        @d[l] = c * p

        # Check for convergence.

      end while (@e[l].abs > eps*tst1)
    end
    @d[l] = @d[l] + f
    @e[l] = 0.0
  end

  # Sort eigenvalues and corresponding vectors.

  0.upto(@size-2) do |i|
    k = i
    p = @d[i]
    (i+1).upto(@size-1) do |j|
      if (@d[j] < p)
        k = j
        p = @d[j]
      end
    end
    if (k != i)
      @d[k] = @d[i]
      @d[i] = p
      @size.times do |j|
        p = @v[j][i]
        @v[j][i] = @v[j][k]
        @v[j][k] = p
      end
    end
  end
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 72

def eigenvalue_matrix
  Matrix.diagonal(*eigenvalues)
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 58

def eigenvalues
  values = @d.dup
  @e.each_with_index{|imag, i| values[i] = Complex(values[i], imag) unless imag == 0}
  values
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 42

def eigenvector_matrix
  Matrix.send(:new, build_eigenvectors.transpose)
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 49

def eigenvector_matrix_inv
  r = Matrix.send(:new, build_eigenvectors)
  r = r.transpose.inverse unless @symmetric
  r
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 66

def eigenvectors
  build_eigenvectors.map{|ev| Vector.send(:new, ev)}
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 446

def hessenberg_to_real_schur

  #  This is derived from the Algol procedure hqr2,
  #  by Martin and Wilkinson, Handbook for Auto. Comp.,
  #  Vol.ii-Linear Algebra, and the corresponding
  #  Fortran subroutine in EISPACK.

  # Initialize

  nn = @size
  n = nn-1
  low = 0
  high = nn-1
  eps = Float::EPSILON
  exshift = 0.0
  p=q=r=s=z=0

  # Store roots isolated by balanc and compute matrix norm

  norm = 0.0
  nn.times do |i|
    if (i < low || i > high)
      @d[i] = @h[i][i]
      @e[i] = 0.0
    end
    ([i-1, 0].max).upto(nn-1) do |j|
      norm = norm + @h[i][j].abs
    end
  end

  # Outer loop over eigenvalue index

  iter = 0
  while (n >= low) do

    # Look for single small sub-diagonal element

    l = n
    while (l > low) do
      s = @h[l-1][l-1].abs + @h[l][l].abs
      if (s == 0.0)
        s = norm
      end
      if (@h[l][l-1].abs < eps * s)
        break
      end
      l-=1
    end

    # Check for convergence
    # One root found

    if (l == n)
      @h[n][n] = @h[n][n] + exshift
      @d[n] = @h[n][n]
      @e[n] = 0.0
      n-=1
      iter = 0

    # Two roots found

    elsif (l == n-1)
      w = @h[n][n-1] * @h[n-1][n]
      p = (@h[n-1][n-1] - @h[n][n]) / 2.0
      q = p * p + w
      z = Math.sqrt(q.abs)
      @h[n][n] = @h[n][n] + exshift
      @h[n-1][n-1] = @h[n-1][n-1] + exshift
      x = @h[n][n]

      # Real pair

      if (q >= 0)
        if (p >= 0)
          z = p + z
        else
          z = p - z
        end
        @d[n-1] = x + z
        @d[n] = @d[n-1]
        if (z != 0.0)
          @d[n] = x - w / z
        end
        @e[n-1] = 0.0
        @e[n] = 0.0
        x = @h[n][n-1]
        s = x.abs + z.abs
        p = x / s
        q = z / s
        r = Math.sqrt(p * p+q * q)
        p /= r
        q /= r

        # Row modification

        (n-1).upto(nn-1) do |j|
          z = @h[n-1][j]
          @h[n-1][j] = q * z + p * @h[n][j]
          @h[n][j] = q * @h[n][j] - p * z
        end

        # Column modification

        0.upto(n) do |i|
          z = @h[i][n-1]
          @h[i][n-1] = q * z + p * @h[i][n]
          @h[i][n] = q * @h[i][n] - p * z
        end

        # Accumulate transformations

        low.upto(high) do |i|
          z = @v[i][n-1]
          @v[i][n-1] = q * z + p * @v[i][n]
          @v[i][n] = q * @v[i][n] - p * z
        end

      # Complex pair

      else
        @d[n-1] = x + p
        @d[n] = x + p
        @e[n-1] = z
        @e[n] = -z
      end
      n -= 2
      iter = 0

    # No convergence yet

    else

      # Form shift

      x = @h[n][n]
      y = 0.0
      w = 0.0
      if (l < n)
        y = @h[n-1][n-1]
        w = @h[n][n-1] * @h[n-1][n]
      end

      # Wilkinson's original ad hoc shift

      if (iter == 10)
        exshift += x
        low.upto(n) do |i|
          @h[i][i] -= x
        end
        s = @h[n][n-1].abs + @h[n-1][n-2].abs
        x = y = 0.75 * s
        w = -0.4375 * s * s
      end

      # MATLAB's new ad hoc shift

      if (iter == 30)
         s = (y - x) / 2.0
         s *= s + w
         if (s > 0)
            s = Math.sqrt(s)
            if (y < x)
              s = -s
            end
            s = x - w / ((y - x) / 2.0 + s)
            low.upto(n) do |i|
              @h[i][i] -= s
            end
            exshift += s
            x = y = w = 0.964
         end
      end

      iter = iter + 1  # (Could check iteration count here.)

      # Look for two consecutive small sub-diagonal elements

      m = n-2
      while (m >= l) do
        z = @h[m][m]
        r = x - z
        s = y - z
        p = (r * s - w) / @h[m+1][m] + @h[m][m+1]
        q = @h[m1][m1] - z - r - s
        r = @h[m2][m1]
        s = p.abs + q.abs + r.abs
        p /= s
        q /= s
        r /= s
        if (m == l)
          break
        end
        if (@h[m][m-1].abs * (q.abs + r.abs) <
          eps * (p.abs * (@h[m-1][m-1].abs + z.abs +
          @h[m1][m1].abs)))
            break
        end
        m-=1
      end

      (m+2).upto(n) do |i|
        @h[i][i-2] = 0.0
        if (i > m+2)
          @h[i][i-3] = 0.0
        end
      end

      # Double QR step involving rows l:n and columns m:n

      m.upto(n-1) do |k|
        notlast = (k != n-1)
        if (k != m)
          p = @h[k][k-1]
          q = @h[k+1][k-1]
          r = (notlast ? @h[k+2][k-1] : 0.0)
          x = p.abs + q.abs + r.abs
          next if x == 0
          p /= x
          q /= x
          r /= x
        end
        s = Math.sqrt(p * p + q * q + r * r)
        if (p < 0)
          s = -s
        end
        if (s != 0)
          if (k != m)
            @h[k][k-1] = -s * x
          elsif (l != m)
            @h[k][k-1] = -@h[k][k-1]
          end
          p += s
          x = p / s
          y = q / s
          z = r / s
          q /= p
          r /= p

          # Row modification

          k.upto(nn-1) do |j|
            p = @h[k][j] + q * @h[k+1][j]
            if (notlast)
              p += r * @h[k+2][j]
              @h[k+2][j] = @h[k+2][j] - p * z
            end
            @h[k][j] = @h[k][j] - p * x
            @h[k+1][j] = @h[k+1][j] - p * y
          end

          # Column modification

          0.upto([n, k+3].min) do |i|
            p = x * @h[i][k] + y * @h[i][k+1]
            if (notlast)
              p += z * @h[i][k+2]
              @h[i][k+2] = @h[i][k+2] - p * r
            end
            @h[i][k] = @h[i][k] - p
            @h[i][k+1] = @h[i][k+1] - p * q
          end

          # Accumulate transformations

          low.upto(high) do |i|
            p = x * @v[i][k] + y * @v[i][k+1]
            if (notlast)
              p += z * @v[i][k+2]
              @v[i][k+2] = @v[i][k+2] - p * r
            end
            @v[i][k] = @v[i][k] - p
            @v[i][k+1] = @v[i][k+1] - p * q
          end
        end  # (s != 0)
      end  # k loop
    end  # check convergence
  end  # while (n >= low)

  # Backsubstitute to find vectors of upper triangular form

  if (norm == 0.0)
    return
  end

  (nn-1).downto(0) do |n|
    p = @d[n]
    q = @e[n]

    # Real vector

    if (q == 0)
      l = n
      @h[n][n] = 1.0
      (n-1).downto(0) do |i|
        w = @h[i][i] - p
        r = 0.0
        l.upto(n) do |j|
          r += @h[i][j] * @h[j][n]
        end
        if (@e[i] < 0.0)
          z = w
          s = r
        else
          l = i
          if (@e[i] == 0.0)
            if (w != 0.0)
              @h[i][n] = -r / w
            else
              @h[i][n] = -r / (eps * norm)
            end

          # Solve real equations

          else
            x = @h[i][i+1]
            y = @h[i+1][i]
            q = (@d[i] - p) * (@d[i] - p) + @e[i] * @e[i]
            t = (x * s - z * r) / q
            @h[i][n] = t
            if (x.abs > z.abs)
              @h[i+1][n] = (-r - w * t) / x
            else
              @h[i+1][n] = (-s - y * t) / z
            end
          end

          # Overflow control

          t = @h[i][n].abs
          if ((eps * t) * t > 1)
            i.upto(n) do |j|
              @h[j][n] = @h[j][n] / t
            end
          end
        end
      end

    # Complex vector

    elsif (q < 0)
      l = n-1

      # Last vector component imaginary so matrix is triangular

      if (@h[n][n-1].abs > @h[n-1][n].abs)
        @h[n-1][n-1] = q / @h[n][n-1]
        @h[n-1][n] = -(@h[n][n] - p) / @h[n][n-1]
      else
        cdivr, cdivi = cdiv(0.0, -@h[n-1][n], @h[n-1][n-1]-p, q)
        @h[n-1][n-1] = cdivr
        @h[n-1][n] = cdivi
      end
      @h[n][n-1] = 0.0
      @h[n][n] = 1.0
      (n-2).downto(0) do |i|
        ra = 0.0
        sa = 0.0
        l.upto(n) do |j|
          ra = ra + @h[i][j] * @h[j][n-1]
          sa = sa + @h[i][j] * @h[j][n]
        end
        w = @h[i][i] - p

        if (@e[i] < 0.0)
          z = w
          r = ra
          s = sa
        else
          l = i
          if (@e[i] == 0)
            cdivr, cdivi = cdiv(-ra, -sa, w, q)
            @h[i][n-1] = cdivr
            @h[i][n] = cdivi
          else

            # Solve complex equations

            x = @h[i][i+1]
            y = @h[i+1][i]
            vr = (@d[i] - p) * (@d[i] - p) + @e[i] * @e[i] - q * q
            vi = (@d[i] - p) * 2.0 * q
            if (vr == 0.0 && vi == 0.0)
              vr = eps * norm * (w.abs + q.abs +
              x.abs + y.abs + z.abs)
            end
            cdivr, cdivi = cdiv(x*r-z*ra+q*sa, x*s-z*sa-q*ra, vr, vi)
            @h[i][n-1] = cdivr
            @h[i][n] = cdivi
            if (x.abs > (z.abs + q.abs))
              @h[i+1][n-1] = (-ra - w * @h[i][n-1] + q * @h[i][n]) / x
              @h[i+1][n] = (-sa - w * @h[i][n] - q * @h[i][n-1]) / x
            else
              cdivr, cdivi = cdiv(-r-y*@h[i][n-1], -s-y*@h[i][n], z, q)
              @h[i+1][n-1] = cdivr
              @h[i+1][n] = cdivi
            end
          end

          # Overflow control

          t = [@h[i][n-1].abs, @h[i][n].abs].max
          if ((eps * t) * t > 1)
            i.upto(n) do |j|
              @h[j][n-1] = @h[j][n-1] / t
              @h[j][n] = @h[j][n] / t
            end
          end
        end
      end
    end
  end

  # Vectors of isolated roots

  nn.times do |i|
    if (i < low || i > high)
      i.upto(nn-1) do |j|
        @v[i][j] = @h[i][j]
      end
    end
  end

  # Back transformation to get eigenvectors of original matrix

  (nn-1).downto(low) do |j|
    low.upto(high) do |i|
      z = 0.0
      low.upto([j, high].min) do |k|
        z += @v[i][k] * @h[k][j]
      end
      @v[i][j] = z
    end
  end
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 352

def reduce_to_hessenberg
  #  This is derived from the Algol procedures orthes and ortran,
  #  by Martin and Wilkinson, Handbook for Auto. Comp.,
  #  Vol.ii-Linear Algebra, and the corresponding
  #  Fortran subroutines in EISPACK.

  low = 0
  high = @size-1

  (low+1).upto(high-1) do |m|

    # Scale column.

    scale = 0.0
    m.upto(high) do |i|
      scale = scale + @h[i][m-1].abs
    end
    if (scale != 0.0)

      # Compute Householder transformation.

      h = 0.0
      high.downto(m) do |i|
        @ort[i] = @h[i][m-1]/scale
        h += @ort[i] * @ort[i]
      end
      g = Math.sqrt(h)
      if (@ort[m] > 0)
        g = -g
      end
      h -= @ort[m] * g
      @ort[m] = @ort[m] - g

      # Apply Householder similarity transformation
      # @h = (I-u*u'/h)*@h*(I-u*u')/h)

      m.upto(@size-1) do |j|
        f = 0.0
        high.downto(m) do |i|
          f += @ort[i]*@h[i][j]
        end
        f = f/h
        m.upto(high) do |i|
          @h[i][j] -= f*@ort[i]
        end
      end

      0.upto(high) do |i|
        f = 0.0
        high.downto(m) do |j|
          f += @ort[j]*@h[i][j]
        end
        f = f/h
        m.upto(high) do |j|
          @h[i][j] -= f*@ort[j]
        end
      end
      @ort[m] = scale*@ort[m]
      @h[m][m-1] = scale*g
    end
  end

  # Accumulate transformations (Algol's ortran).

  @size.times do |i|
    @size.times do |j|
      @v[i][j] = (i == j ? 1.0 : 0.0)
    end
  end

  (high-1).downto(low+1) do |m|
    if (@h[m][m-1] != 0.0)
      (m+1).upto(high) do |i|
        @ort[i] = @h[i][m-1]
      end
      m.upto(high) do |j|
        g = 0.0
        m.upto(high) do |i|
          g += @ort[i] * @v[i][j]
        end
        # Double division avoids possible underflow
        g = (g / @ort[m]) / @h[m][m-1]
        m.upto(high) do |i|
          @v[i][j] += g * @ort[i]
        end
      end
    end
  end
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 82

alias_method :to_a, :to_ary

# File 'lib/matrix/eigenvalue_decomposition.rb', line 79

def to_ary
  [v, d, v_inv]
end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 115

def tridiagonalize

  #  This is derived from the Algol procedures tred2 by
  #  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
  #  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
  #  Fortran subroutine in EISPACK.

    @size.times do |j|
      @d[j] = @v[@size-1][j]
    end

    # Householder reduction to tridiagonal form.

    (@size-1).downto(0+1) do |i|

      # Scale to avoid under/overflow.

      scale = 0.0
      h = 0.0
      i.times do |k|
        scale = scale + @d[k].abs
      end
      if (scale == 0.0)
        @e[i] = @d[i-1]
        i.times do |j|
          @d[j] = @v[i-1][j]
          @v[i][j] = 0.0
          @v[j][i] = 0.0
        end
      else

        # Generate Householder vector.

        i.times do |k|
          @d[k] /= scale
          h += @d[k] * @d[k]
        end
        f = @d[i-1]
        g = Math.sqrt(h)
        if (f > 0)
          g = -g
        end
        @e[i] = scale * g
        h -= f * g
        @d[i-1] = f - g
        i.times do |j|
          @e[j] = 0.0
        end

        # Apply similarity transformation to remaining columns.

        i.times do |j|
          f = @d[j]
          @v[j][i] = f
          g = @e[j] + @v[j][j] * f
          (j+1).upto(i-1) do |k|
            g += @v[k][j] * @d[k]
            @e[k] += @v[k][j] * f
          end
          @e[j] = g
        end
        f = 0.0
        i.times do |j|
          @e[j] /= h
          f += @e[j] * @d[j]
        end
        hh = f / (h + h)
        i.times do |j|
          @e[j] -= hh * @d[j]
        end
        i.times do |j|
          f = @d[j]
          g = @e[j]
          j.upto(i-1) do |k|
            @v[k][j] -= (f * @e[k] + g * @d[k])
          end
          @d[j] = @v[i-1][j]
          @v[i][j] = 0.0
        end
      end
      @d[i] = h
    end

    # Accumulate transformations.

    0.upto(@size-1-1) do |i|
      @v[@size-1][i] = @v[i][i]
      @v[i][i] = 1.0
      h = @d[i+1]
      if (h != 0.0)
        0.upto(i) do |k|
          @d[k] = @v[k][i+1] / h
        end
        0.upto(i) do |j|
          g = 0.0
          0.upto(i) do |k|
            g += @v[k][i+1] * @v[k][j]
          end
          0.upto(i) do |k|
            @v[k][j] -= g * @d[k]
          end
        end
      end
      0.upto(i) do |k|
        @v[k][i+1] = 0.0
      end
    end
    @size.times do |j|
      @d[j] = @v[@size-1][j]
      @v[@size-1][j] = 0.0
    end
    @v[@size-1][@size-1] = 1.0
    @e[0] = 0.0
  end

# File 'lib/matrix/eigenvalue_decomposition.rb', line 45

alias v eigenvector_matrix

# File 'lib/matrix/eigenvalue_decomposition.rb', line 54

alias v_inv eigenvector_matrix_inv