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.
Returns the pivoting indices
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 154
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
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 79
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
Returns the determinant of A
, calculated efficiently from the factorization.
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 22
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
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 48
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 the permutation matrix P
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 67
def singular?
@column_count.times do |j|
if (@lu[j][j] == 0)
return true
end
end
false
end
Returns true
if U
, and hence A
, is singular.
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 95
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
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 56
def to_ary
[l, u, p]
end
Returns L
, U
, P
in an array
# File tmp/rubies/ruby-3.0.5/lib/matrix/lup_decomposition.rb, line 36
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 upper triangular factor U