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Edit File : lup_decomposition.rb \| Size : 5.37 KB
# frozen_string_literal: false class Matrix # Adapted from JAMA: http://math.nist.gov/javanumerics/jama/ # # For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n # unit lower triangular matrix L, an n-by-n upper triangular matrix U, # and a m-by-m permutation matrix P so that LU = PA. # If m < n, then L is m-by-m and U is m-by-n. # # The LUP decomposition with pivoting always exists, even if the matrix is # singular, so the constructor will never fail. The primary use of the # LU decomposition is in the solution of square systems of simultaneous # linear equations. This will fail if singular? returns true. # class LUPDecomposition # Returns the lower triangular factor +L+ include Matrix::ConversionHelper def l Matrix.build(@row_count, [@column_count, @row_count].min) do \|i, j\| if (i > j) @lu[i][j] elsif (i == j) 1 else 0 end end end # Returns the upper triangular factor +U+ def u Matrix.build([@column_count, @row_count].min, @column_count) do \|i, j\| if (i <= j) @lu[i][j] else 0 end end end # Returns the permutation matrix +P+ def p rows = Array.new(@row_count){Array.new(@row_count, 0)} @pivots.each_with_index{\|p, i\| rows[i][p] = 1} Matrix.send :new, rows, @row_count end # Returns +L+, +U+, +P+ in an array def to_ary [l, u, p] end alias_method :to_a, :to_ary # Returns the pivoting indices attr_reader :pivots # Returns +true+ if +U+, and hence +A+, is singular. def singular? @column_count.times do \|j\| if (@lu[j][j] == 0) return true end end false end # Returns the determinant of +A+, calculated efficiently # from the factorization. def det if (@row_count != @column_count) raise Matrix::ErrDimensionMismatch end d = @pivot_sign @column_count.times do \|j\| d = @lu[j][j] end d end alias_method :determinant, :det # Returns +m+ so that <tt>Am = b</tt>, # or equivalently so that <tt>LUm = Pb</tt> # +b+ can be a Matrix or a Vector def solve b if (singular?) raise Matrix::ErrNotRegular, "Matrix is singular." end if b.is_a? Matrix if (b.row_count != @row_count) raise Matrix::ErrDimensionMismatch end # Copy right hand side with pivoting nx = b.column_count m = @pivots.map{\|row\| b.row(row).to_a} # Solve LY = Pb @column_count.times do \|k\| (k+1).upto(@column_count-1) do \|i\| nx.times do \|j\| m[i][j] -= m[k][j]@lu[i][k] end end end # Solve Um = Y (@column_count-1).downto(0) do \|k\| nx.times do \|j\| m[k][j] = m[k][j].quo(@lu[k][k]) end k.times do \|i\| nx.times do \|j\| m[i][j] -= m[k][j]@lu[i][k] end end end Matrix.send :new, m, nx else # same algorithm, specialized for simpler case of a vector b = convert_to_array(b) if (b.size != @row_count) raise Matrix::ErrDimensionMismatch end # Copy right hand side with pivoting m = b.values_at(@pivots) # Solve LY = Pb @column_count.times do \|k\| (k+1).upto(@column_count-1) do \|i\| m[i] -= m[k]@lu[i][k] end end # Solve Um = Y (@column_count-1).downto(0) do \|k\| m[k] = m[k].quo(@lu[k][k]) k.times do \|i\| m[i] -= m[k]@lu[i][k] end end Vector.elements(m, false) end end def initialize a raise TypeError, "Expected Matrix but got #{a.class}" unless a.is_a?(Matrix) # Use a "left-looking", dot-product, Crout/Doolittle algorithm. @lu = a.to_a @row_count = a.row_count @column_count = a.column_count @pivots = Array.new(@row_count) @row_count.times do \|i\| @pivots[i] = i end @pivot_sign = 1 lu_col_j = Array.new(@row_count) # Outer loop. @column_count.times do \|j\| # Make a copy of the j-th column to localize references. @row_count.times do \|i\| lu_col_j[i] = @lu[i][j] end # Apply previous transformations. @row_count.times do \|i\| lu_row_i = @lu[i] # Most of the time is spent in the following dot product. kmax = [i, j].min s = 0 kmax.times do \|k\| s += lu_row_i[k]*lu_col_j[k] end lu_row_i[j] = lu_col_j[i] -= s end # Find pivot and exchange if necessary. p = j (j+1).upto(@row_count-1) do \|i\| if (lu_col_j[i].abs > lu_col_j[p].abs) p = i end end if (p != j) @column_count.times do \|k\| t = @lu[p][k]; @lu[p][k] = @lu[j][k]; @lu[j][k] = t end k = @pivots[p]; @pivots[p] = @pivots[j]; @pivots[j] = k @pivot_sign = -@pivot_sign end # Compute multipliers. if (j < @row_count && @lu[j][j] != 0) (j+1).upto(@row_count-1) do \|i\| @lu[i][j] = @lu[i][j].quo(@lu[j][j]) end end end end end end Back

Edit File : lup_decomposition.rb | Size : 5.37 KB

# frozen_string_literal: false
class Matrix
  # Adapted from JAMA: http://math.nist.gov/javanumerics/jama/

#
  # For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n
  # unit lower triangular matrix L, an n-by-n upper triangular matrix U,
  # and a m-by-m permutation matrix P so that L*U = P*A.
  # If m < n, then L is m-by-m and U is m-by-n.
  #
  # The LUP decomposition with pivoting always exists, even if the matrix is
  # singular, so the constructor will never fail.  The primary use of the
  # LU decomposition is in the solution of square systems of simultaneous
  # linear equations.  This will fail if singular? returns true.
  #

class LUPDecomposition
    # Returns the lower triangular factor +L+

include Matrix::ConversionHelper

def l
      Matrix.build(@row_count, [@column_count, @row_count].min) do |i, j|
        if (i > j)
          @lu[i][j]
        elsif (i == j)
          1
        else
          0
        end
      end
    end

# Returns the upper triangular factor +U+

def u
      Matrix.build([@column_count, @row_count].min, @column_count) do |i, j|
        if (i <= j)
          @lu[i][j]
        else
          0
        end
      end
    end

# Returns the permutation matrix +P+

def p
      rows = Array.new(@row_count){Array.new(@row_count, 0)}
      @pivots.each_with_index{|p, i| rows[i][p] = 1}
      Matrix.send :new, rows, @row_count
    end

# Returns +L+, +U+, +P+ in an array

def to_ary
      [l, u, p]
    end
    alias_method :to_a, :to_ary

# Returns the pivoting indices

attr_reader :pivots

# Returns +true+ if +U+, and hence +A+, is singular.

def singular?
      @column_count.times do |j|
        if (@lu[j][j] == 0)
          return true
        end
      end
      false
    end

# Returns the determinant of +A+, calculated efficiently
    # from the factorization.

def det
      if (@row_count != @column_count)
        raise Matrix::ErrDimensionMismatch
      end
      d = @pivot_sign
      @column_count.times do |j|
        d *= @lu[j][j]
      end
      d
    end
    alias_method :determinant, :det

# Returns +m+ so that <tt>A*m = b</tt>,
    # or equivalently so that <tt>L*U*m = P*b</tt>
    # +b+ can be a Matrix or a Vector

def solve b
      if (singular?)
        raise Matrix::ErrNotRegular, "Matrix is singular."
      end
      if b.is_a? Matrix
        if (b.row_count != @row_count)
          raise Matrix::ErrDimensionMismatch
        end

# Copy right hand side with pivoting
        nx = b.column_count
        m = @pivots.map{|row| b.row(row).to_a}

# Solve L*Y = P*b
        @column_count.times do |k|
          (k+1).upto(@column_count-1) do |i|
            nx.times do |j|
              m[i][j] -= m[k][j]*@lu[i][k]
            end
          end
        end
        # Solve U*m = Y
        (@column_count-1).downto(0) do |k|
          nx.times do |j|
            m[k][j] = m[k][j].quo(@lu[k][k])
          end
          k.times do |i|
            nx.times do |j|
              m[i][j] -= m[k][j]*@lu[i][k]
            end
          end
        end
        Matrix.send :new, m, nx
      else # same algorithm, specialized for simpler case of a vector
        b = convert_to_array(b)
        if (b.size != @row_count)
          raise Matrix::ErrDimensionMismatch
        end

# Copy right hand side with pivoting
        m = b.values_at(*@pivots)

# Solve L*Y = P*b
        @column_count.times do |k|
          (k+1).upto(@column_count-1) do |i|
            m[i] -= m[k]*@lu[i][k]
          end
        end
        # Solve U*m = Y
        (@column_count-1).downto(0) do |k|
          m[k] = m[k].quo(@lu[k][k])
          k.times do |i|
            m[i] -= m[k]*@lu[i][k]
          end
        end
        Vector.elements(m, false)
      end
    end

def initialize a
      raise TypeError, "Expected Matrix but got #{a.class}" unless a.is_a?(Matrix)
      # Use a "left-looking", dot-product, Crout/Doolittle algorithm.
      @lu = a.to_a
      @row_count = a.row_count
      @column_count = a.column_count
      @pivots = Array.new(@row_count)
      @row_count.times do |i|
         @pivots[i] = i
      end
      @pivot_sign = 1
      lu_col_j = Array.new(@row_count)

# Outer loop.

@column_count.times do |j|

# Make a copy of the j-th column to localize references.

@row_count.times do |i|
          lu_col_j[i] = @lu[i][j]
        end

# Apply previous transformations.

@row_count.times do |i|
          lu_row_i = @lu[i]

# Most of the time is spent in the following dot product.

kmax = [i, j].min
          s = 0
          kmax.times do |k|
            s += lu_row_i[k]*lu_col_j[k]
          end

lu_row_i[j] = lu_col_j[i] -= s
        end

# Find pivot and exchange if necessary.

p = j
        (j+1).upto(@row_count-1) do |i|
          if (lu_col_j[i].abs > lu_col_j[p].abs)
            p = i
          end
        end
        if (p != j)
          @column_count.times do |k|
            t = @lu[p][k]; @lu[p][k] = @lu[j][k]; @lu[j][k] = t
          end
          k = @pivots[p]; @pivots[p] = @pivots[j]; @pivots[j] = k
          @pivot_sign = -@pivot_sign
        end

# Compute multipliers.

if (j < @row_count && @lu[j][j] != 0)
          (j+1).upto(@row_count-1) do |i|
            @lu[i][j] = @lu[i][j].quo(@lu[j][j])
          end
        end
      end
    end
  end
end

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