In
numerical analysis
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic computation, symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). It is the study of ...
and
linear algebra
Linear algebra is the branch of mathematics concerning linear equations such as
:a_1x_1+\cdots +a_nx_n=b,
linear maps such as
:(x_1, \ldots, x_n) \mapsto a_1x_1+\cdots +a_nx_n,
and their representations in vector spaces and through matrix (mathemat ...
, lower–upper (LU) decomposition or factorization factors a
matrix
Matrix (: matrices or matrixes) or MATRIX may refer to:
Science and mathematics
* Matrix (mathematics), a rectangular array of numbers, symbols or expressions
* Matrix (logic), part of a formula in prenex normal form
* Matrix (biology), the m ...
as the product of a lower
triangular matrix
In mathematics, a triangular matrix is a special kind of square matrix. A square matrix is called if all the entries ''above'' the main diagonal are zero. Similarly, a square matrix is called if all the entries ''below'' the main diagonal are z ...
and an upper triangular matrix (see
matrix multiplication
In mathematics, specifically in linear algebra, matrix multiplication is a binary operation that produces a matrix (mathematics), matrix from two matrices. For matrix multiplication, the number of columns in the first matrix must be equal to the n ...
and
matrix decomposition). The product sometimes includes a
permutation matrix
In mathematics, particularly in matrix theory, a permutation matrix is a square binary matrix that has exactly one entry of 1 in each row and each column with all other entries 0. An permutation matrix can represent a permutation of elements. ...
as well. LU decomposition can be viewed as the matrix form of
Gaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients. This method can a ...
. Computers usually solve square
systems of linear equations
In mathematics, a system of linear equations (or linear system) is a collection of two or more linear equations involving the same variables.
For example,
: \begin
3x+2y-z=1\\
2x-2y+4z=-2\\
-x+\fracy-z=0
\end
is a system of three equations in ...
using LU decomposition, and it is also a key step when inverting a matrix or computing the
determinant
In mathematics, the determinant is a Scalar (mathematics), scalar-valued function (mathematics), function of the entries of a square matrix. The determinant of a matrix is commonly denoted , , or . Its value characterizes some properties of the ...
of a matrix. It is also sometimes referred to as LR decomposition (factors into left and right triangular matrices). The LU decomposition was introduced by the Polish astronomer
Tadeusz Banachiewicz in 1938, who first wrote product equation
(The last form in his alternate yet equivalent matrix notation appears as
)
Definitions

Let ''A'' be a square matrix. An LU factorization refers to expression of ''A'' into product of two factors – a lower triangular matrix ''L'' and an upper triangular matrix ''U'':
Sometimes factorization is impossible without prior
reordering of ''A'' to prevent division by zero or uncontrolled growth of rounding errors hence alternative expression becomes:
, where in formal notation
permutation matrices
In mathematics, particularly in Matrix (mathematics), matrix theory, a permutation matrix is a square binary matrix that has exactly one entry of 1 in each row and each column with all other entries 0. An permutation matrix can represent a permu ...
factors ''P'' and ''Q'' indicate permutation of rows (or columns) of ''A''. In theory ''P'' (or ''Q'') are obtained by permutations of rows (or columns) of the
identity matrix
In linear algebra, the identity matrix of size n is the n\times n square matrix with ones on the main diagonal and zeros elsewhere. It has unique properties, for example when the identity matrix represents a geometric transformation, the obje ...
, in practice the corresponding permutations are applied directly to rows (or columns) of ''A''.
Matrix ''A'' of side
has
coefficients while two triangle matrices combined contain
coefficients, therefore
coefficients of matrices ''LU'' are not independent. Usual convention is to set ''L'' unitriangular, i.e. with all
main diagonal elements equal one. However, setting instead ''U'' matrix unitriangular reduces
to the same procedure after
transpose
In linear algebra, the transpose of a Matrix (mathematics), matrix is an operator which flips a matrix over its diagonal;
that is, it switches the row and column indices of the matrix by producing another matrix, often denoted by (among other ...
of matrix product:
, (cf. properties of
transpose
In linear algebra, the transpose of a Matrix (mathematics), matrix is an operator which flips a matrix over its diagonal;
that is, it switches the row and column indices of the matrix by producing another matrix, often denoted by (among other ...
). Now
is lower triangle while
is upper unitriangular factor of ''B''. This demonstrates also, that operations on rows (e.g. pivoting) are equivalent to those on columns of a transposed matrix, and in general choice of row or column algorithm offers no advantage.
In the lower triangular matrix all elements above the main diagonal are zero, in the upper triangular matrix, all the elements below the diagonal are zero. For example, for a 3 × 3 matrix ''A'', its LU decomposition looks like this:
:
Without a proper ordering or permutations in the matrix, the factorization may fail to materialize. For example, it is easy to verify (by expanding the
matrix multiplication
In mathematics, specifically in linear algebra, matrix multiplication is a binary operation that produces a matrix (mathematics), matrix from two matrices. For matrix multiplication, the number of columns in the first matrix must be equal to the n ...
) that
. If
, then at least one of
and
has to be zero, which implies that either ''L'' or ''U'' is
singular
Singular may refer to:
* Singular, the grammatical number that denotes a unit quantity, as opposed to the plural and other forms
* Singular or sounder, a group of boar, see List of animal names
* Singular (band), a Thai jazz pop duo
*'' Singula ...
. This is impossible if ''A'' is nonsingular (invertible). In terms of operations, zeroing/elimination of remaining elements of first column of ''A'' involves division of
with
, impossible if it is 0. This is a procedural problem. It can be removed by simply reordering the rows of ''A'' so that the first element of the permuted matrix is nonzero. The same problem in subsequent factorization steps can be removed the same way. For numerical stability against rounding errors/division by small numbers it is important to select
of large absolute value (cf. pivoting).
LU Through recursion
The above example of
matrices demonstrates that matrix product of top row and leftmost columns of involved matrices plays special role for
to succeed. Let us mark consecutive versions of matrices with
and then let us write matrix product
in such way that these rows and columns are separated from the rest. In doing so we shall use
block matrix
In mathematics, a block matrix or a partitioned matrix is a matrix that is interpreted as having been broken into sections called blocks or submatrices.
Intuitively, a matrix interpreted as a block matrix can be visualized as the original matrix w ...
notation, such that e.g.
is an ordinary number,
is a row vector and
is a column vector and
is sub-matrix of matrix
without top row and leftmost column. Then we can replace
with a block
matrix product
In mathematics, specifically in linear algebra, matrix multiplication is a binary operation that produces a matrix from two matrices. For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the s ...
. Namely it turns out that one can multiply matrix blocks in such way as if they were ordinary numbers, i.e. row times column, except that now their components are sub-matrices, sometimes reduced to scalars or vectors. Thus
denotes a vector obtained from
after multiplication of each component by a number
,
is an
outer product
In linear algebra, the outer product of two coordinate vectors is the matrix whose entries are all products of an element in the first vector with an element in the second vector. If the two coordinate vectors have dimensions ''n'' and ''m'', the ...
of vectors
, i.e. a matrix which first column is
, next is
and so on for all components of
and
is a product of sub-matrices of
:
:
From equality of first and last matrices follow final
,
,
while matrix
becomes updated/replaced with
. Now comes the crucial observation: ''nothing prevents us to treat
the same way as we did with
'', and again, and again... If dimension of
is
, after
such steps all columns
form sub-diagonal part of triangle matrix
and all pivots
combined with rows
form upper
triangle matrix
, as required. In the above example
so 2 steps suffice.
The above procedure demonstrates that at no step the top diagonal pivot element
of consecutive sub-matrices can be zero. To avoid it columns or rows may be swapped so that
becomes nonzero.
Such procedure involving permutation is called
, decomposition with pivoting.
Permutation of columns corresponds to matrix product
where
is a permutation matrix, i.e. the identity matrix
after the same column permutation. After all steps such LUP decomposition applies to
.
Present computation scheme and similar in Cormen et al. are examples of ''recurrence algorithms''. They demonstrate two general properties of
: (i) need for pivoting at each step and (ii) that final values of
matrices are obtained gradually, one row or column per step.
Recurrence algorithms are not overly costly in terms of algebraic operations yet they suffer from practical disadvantage due to need to update and store most elements of
at each step.
It will be seen that by reordering calculations it is possible to dispose with storage of intermediate values.
LU factorization with partial pivoting
It turns out that a proper permutation of rows (or columns) to select column (or row)
absolute maximal pivot
is sufficient for numerically stable LU factorization, except for known pathological cases. It is called LU factorization with partial pivoting (LUP):
where ''L'' and ''U'' are again lower and upper triangular matrices, and ''P'' and ''Q'' are corresponding
permutation matrices
In mathematics, particularly in Matrix (mathematics), matrix theory, a permutation matrix is a square binary matrix that has exactly one entry of 1 in each row and each column with all other entries 0. An permutation matrix can represent a permu ...
, which, when left/right-multiplied to ''A'', reorder the rows/columns of ''A''. It turns out that all square matrices can be factorized in this form,
[.] and the factorization is numerically stable in practice. This makes LUP decomposition a useful technique in practice.
A variant called rook pivoting at each step involves search of maximum element the way rook moves on a chessboard, along column, row, column again and so on till reaching a pivot maximal in both its row and column. It can be proven that for large matrices of random elements its cost of operations at each step is similarly to partial pivoting proportional to the length of matrix side unlike its square for full pivoting.
LU factorization with full pivoting
An LU factorization with full pivoting involves both row and column permutations to find absolute maximum element in the whole submatrix:
:
where ''L'', ''U'' and ''P'' are defined as before, and ''Q'' is a permutation matrix that reorders the columns of ''A''.
Lower-diagonal-upper (LDU) decomposition
A Lower-diagonal-upper (LDU) decomposition is a decomposition of the form
:
where ''D'' is a
diagonal matrix
In linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero; the term usually refers to square matrices. Elements of the main diagonal can either be zero or nonzero. An example of a 2×2 diagon ...
, and ''L'' and ''U'' are
unitriangular matrices, meaning that all the entries on the diagonals of ''L'' and ''U'' are one.
Rectangular matrices
Above we required that ''A'' be a square matrix, but these decompositions can all be generalized to rectangular matrices as well., In that case, ''L'' and ''D'' are square matrices both of which have the same number of rows as ''A'', and ''U'' has exactly the same dimensions as ''A''. ''Upper triangular'' should be interpreted as having only zero entries below the main diagonal, which starts at the upper left corner. Similarly, the more precise term for ''U'' is that it is the
row echelon form
In linear algebra, a matrix is in row echelon form if it can be obtained as the result of Gaussian elimination. Every matrix can be put in row echelon form by applying a sequence of elementary row operations. The term ''echelon'' comes from the F ...
of the matrix ''A''.
Example
We factor the following 2-by-2 matrix:
:
One way to find the LU decomposition of this simple matrix would be to simply solve the linear equations by inspection. Expanding the
matrix multiplication
In mathematics, specifically in linear algebra, matrix multiplication is a binary operation that produces a matrix (mathematics), matrix from two matrices. For matrix multiplication, the number of columns in the first matrix must be equal to the n ...
gives
:
This system of equations is
underdetermined. In this case any two nonzero elements of ''L'' and ''U'' matrices are parameters of the solution and can be set arbitrarily to any nonzero value. Therefore, to find the unique LU decomposition, it is necessary to put some restriction on ''L'' and ''U'' matrices. For example, we can conveniently require the lower triangular matrix ''L'' to be a unit triangular matrix, so that all the entries of its main diagonal are set to one. Then the system of equations has the following solution:
:
Substituting these values into the LU decomposition above yields
:
Existence and uniqueness
Square matrices
Any square matrix
admits ''LUP'' and ''PLU'' factorizations.
[ If is ]invertible
In mathematics, the concept of an inverse element generalises the concepts of opposite () and reciprocal () of numbers.
Given an operation denoted here , and an identity element denoted , if , one says that is a left inverse of , and that ...
, then it admits an ''LU'' (or ''LDU'') factorization if and only if
In logic and related fields such as mathematics and philosophy, "if and only if" (often shortened as "iff") is paraphrased by the biconditional, a logical connective between statements. The biconditional is true in two cases, where either bo ...
all its leading principal minors are nonzero (for example does not admit an ''LU'' or ''LDU'' factorization). If is a singular matrix of rank , then it admits an ''LU'' factorization if the first leading principal minors are nonzero, although the converse is not true.
If a square, invertible matrix has an ''LDU'' (factorization with all diagonal entries of ''L'' and ''U'' equal to 1), then the factorization is unique.[ In that case, the ''LU'' factorization is also unique if we require that the diagonal of (or ) consists of ones.
In general, any square matrix could have one of the following:
# a unique LU factorization (as mentioned above);
# infinitely many LU factorizations if any of the first (''n''−1) columns are linearly dependent;
# no LU factorization if the first (''n''−1) columns are linearly independent and at least one leading principal minor is zero.
In Case 3, one can approximate an LU factorization by changing a diagonal entry to to avoid a zero leading principal minor.
]
Symmetric positive-definite matrices
If ''A'' is a symmetric (or Hermitian, if ''A'' is complex) positive-definite matrix
In mathematics, a symmetric matrix M with real entries is positive-definite if the real number \mathbf^\mathsf M \mathbf is positive for every nonzero real column vector \mathbf, where \mathbf^\mathsf is the row vector transpose of \mathbf.
Mo ...
, we can arrange matters so that ''U'' is the conjugate transpose
In mathematics, the conjugate transpose, also known as the Hermitian transpose, of an m \times n complex matrix \mathbf is an n \times m matrix obtained by transposing \mathbf and applying complex conjugation to each entry (the complex conjugate ...
of ''L''. That is, we can write ''A'' as
:
This decomposition is called the Cholesky decomposition. If is positive definite, then the Cholesky decomposition exists and is unique. Furthermore, computing the Cholesky decomposition is more efficient and numerically more stable than computing some other LU decompositions.
General matrices
For a (not necessarily invertible) matrix over any field, the exact necessary and sufficient conditions under which it has an LU factorization are known. The conditions are expressed in terms of the ranks of certain submatrices. The Gaussian elimination algorithm for obtaining LU decomposition has also been extended to this most general case.
Algorithms
Closed formula
When an LDU factorization exists and is unique, there is a closed (explicit) formula for the elements of ''L'', ''D'', and ''U'' in terms of ratios of determinants of certain submatrices of the original matrix ''A''. In particular, , and for , is the ratio of the -th principal submatrix to the -th principal submatrix. Computation of the determinants is computationally expensive, so this explicit formula is not used in practice.
Using Gaussian elimination
The following algorithm is essentially a modified form of Gaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients. This method can a ...
. Computing an LU decomposition using this algorithm requires floating-point operations, ignoring lower-order terms. Partial pivoting adds only a quadratic term; this is not the case for full pivoting.
Generalized explanation
= Notation
=
Given an ''N'' × ''N'' matrix , define as the original, unmodified version of the matrix . The parenthetical superscript (e.g., ) of the matrix is the version of the matrix. The matrix is the matrix in which the elements below the main diagonal have already been eliminated to 0 through Gaussian elimination for the first columns.
Below is a matrix to observe to help us remember the notation (where each represents any real number
In mathematics, a real number is a number that can be used to measure a continuous one- dimensional quantity such as a duration or temperature. Here, ''continuous'' means that pairs of values can have arbitrarily small differences. Every re ...
in the matrix):
= Procedure
=
During this process, we gradually modify the matrix using row operations until it becomes the matrix in which all the elements below the main diagonal are equal to zero. During this, we will simultaneously create two separate matrices and , such that .
We define the final permutation matrix
In mathematics, particularly in matrix theory, a permutation matrix is a square binary matrix that has exactly one entry of 1 in each row and each column with all other entries 0. An permutation matrix can represent a permutation of elements. ...
as the identity matrix which has all the same rows swapped in the same order as the matrix while it transforms into the matrix . For our matrix , we may start by swapping rows to provide the desired conditions for the n-th column. For example, we might swap rows to perform partial pivoting, or we might do it to set the pivot element on the main diagonal to a nonzero number so that we can complete the Gaussian elimination.
For our matrix , we want to set every element below to zero (where is the element in the n-th column of the main diagonal). We will denote each element below as (where ). To set to zero, we set for each row . For this operation, . Once we have performed the row operations for the first columns, we have obtained an upper triangular matrix which is denoted by .
We can also create the lower triangular matrix denoted as , by directly inputting the previously calculated values of via the formula below.
:
Example
If we are given the matrixwe will choose to implement partial pivoting and thus swap the first and second row so that our matrix and the first iteration of our matrix respectively becomeOnce we have swapped the rows, we can eliminate the elements below the main diagonal on the first column by performing such that,Once these rows have been subtracted, we have derived from the matrix Because we are implementing partial pivoting, we swap the second and third rows of our derived matrix and the current version of our matrix respectively to obtainNow, we eliminate the elements below the main diagonal on the second column by performing such that . Because no nonzero elements exist below the main diagonal in our current iteration of after this row subtraction, this row subtraction derives our final matrix (denoted as ) and final matrix:After also switching the corresponding rows, we obtain our final matrix:Now these matrices have a relation such that .
Relations when no rows are swapped
If we did not swap rows at all during this process, we can perform the row operations simultaneously for each column by setting where is the ''N'' × ''N'' identity matrix with its ''n''-th column replaced by the transpose
In linear algebra, the transpose of a Matrix (mathematics), matrix is an operator which flips a matrix over its diagonal;
that is, it switches the row and column indices of the matrix by producing another matrix, often denoted by (among other ...
d vector In other words, the lower triangular matrix
:
Performing all the row operations for the first columns using the formula is equivalent to finding the decomposition
Denote so that .
Now let's compute the sequence of . We know that has the following formula.
:
If there are two lower triangular matrices with 1s in the main diagonal, and neither have a nonzero item below the main diagonal in the same column as the other, then we can include all nonzero items at their same location in the product of the two matrices. For example:
Finally, multiply together and generate the fused matrix denoted as (as previously mentioned). Using the matrix , we obtain
It is clear that in order for this algorithm to work, one needs to have at each step (see the definition of ). If this assumption fails at some point, one needs to interchange ''n''-th row with another row below it before continuing. This is why an LU decomposition in general looks like .
LU Banachiewicz decomposition
Although Banachiewicz (1938) ''LU'' decomposition algorithm preceded the advent of programmed electronic computers, it was
ready made for direct implementation into code as index swapping, transpose and column by column multiplication
remain native built capabilities of the most programming languages and are handled by compilers alone with little delay of actual execution. The peculiar matrix notation used by Banachiewicz enabled him to multiply matrices column by column, a convenient feature for mechanical calculations as he could reveal consecutive factors by sliding a ruler to next rows of matrices. For human readers however, his equations are best transformed into standard matrix notation. To obtain from a full matrix ''A'' triangle matrices ''U'' and ''L'' calculations start by copying top row and leftmost column of ''A'' respectively into corresponding positions of matrices ''U'' and ''L''. The known unit diagonal elements
of ''L'' are not stored neither used throughout the whole process.
Next calculations continue for the subsequent rows and columns till the bottom right corner
of ''A''.
The figure illustrates calculations for 3-rd row and column, assuming previous stages were already completed. Involved matrices are named above squares marking their content.
Matrix products and subtractions are applied only to elements in the thick frame boxes.
Green filled thin frame boxes indicate values already known, from previous stages. Blue boxes indicate places in ''U'' and ''L'' matrices for storing of results. Note that at each stage
the result elements of ''L'' need to be divided by the corresponding pivot element
on the main diagonal of ''U''. This applies to the leftmost column of ''L'' too.
Note that after completion of 3-rd stage the involved elements of matrix
''A'' are no longer used and neither those from previous stages. This enables replacement of these elements with the result values of ''U'' and ''L'', i.e. execution of ''LU'' decomposition ''in place'', so that the whole ''A'' is replaced with ''U'' and ''L'' except for the unit diagonal of ''L''. Banachiewicz ''LU'' algorithm is well suited for partial pivoting by choosing the absolute maximum pivot from the newly calculated row of ''U'' and subsequently swapping its columns so that it lands on the main diagonal. More details
can be figured out from inspection of the enclosed Fortran90 code. All partial pivoting ''LU'' algorithms
cost roughly the same amount, of order operations, where
is number of rows or columns of ''A''.
LU Crout decomposition
Note that the decomposition obtained through this procedure is a ''Doolittle decomposition'': the main diagonal of ''L'' is composed solely of ''1''s. If one would proceed by removing elements ''above'' the main diagonal by adding multiples of the ''columns'' (instead of removing elements ''below'' the diagonal by adding multiples of the ''rows''), we would obtain a '' Crout decomposition'', where the main diagonal of ''U'' is of ''1''s.
Another (equivalent) way of producing a Crout decomposition of a given matrix ''A'' is to obtain a Doolittle decomposition of the transpose of ''A''. Indeed, if is the LU-decomposition obtained through the algorithm presented in this section, then by taking and , we have that is a Crout decomposition.
Randomized algorithm
It is possible to find a low rank approximation to an LU decomposition using a randomized algorithm. Given an input matrix and a desired low rank , the randomized LU returns permutation matrices and lower/upper trapezoidal matrices of size and respectively, such that with high probability , where is a constant that depends on the parameters of the algorithm and is the -th singular value of the input matrix .
Theoretical complexity
If two matrices of order ''n'' can be multiplied in time ''M''(''n''), where ''M''(''n'') ≥ ''n''''a'' for some ''a'' > 2, then an LU decomposition can be computed in time O(''M''(''n'')). This means, for example, that an O(''n''2.376) algorithm exists based on the Coppersmith–Winograd algorithm.
Sparse-matrix decomposition
Special algorithms have been developed for factorizing large sparse matrices
In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix (mathematics), matrix in which most of the elements are zero. There is no strict definition regarding the proportion of zero-value elements for a matrix ...
. These algorithms attempt to find sparse factors ''L'' and ''U''. Ideally, the cost of computation is determined by the number of nonzero entries, rather than by the size of the matrix.
These algorithms use the freedom to exchange rows and columns to minimize fill-in (entries that change from an initial zero to a nonzero value during the execution of an algorithm).
General treatment of orderings that minimize fill-in can be addressed using graph theory
In mathematics and computer science, graph theory is the study of ''graph (discrete mathematics), graphs'', which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of ''Vertex (graph ...
.
Applications
Solving linear equations
Given a system of linear equations in matrix form
:
we want to solve the equation for x, given ''A'' and b. Suppose we have already obtained the LUP decomposition of ''A'' such that , so .
In this case the solution is done in two logical steps:
# First, we solve the equation for y.
# Second, we solve the equation for x.
In both cases we are dealing with triangular matrices (''L'' and ''U''), which can be solved directly by forward and backward substitution without using the Gaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients. This method can a ...
process (however we do need this process or equivalent to compute the ''LU'' decomposition itself).
The above procedure can be repeatedly applied to solve the equation multiple times for different b. In this case it is faster (and more convenient) to do an LU decomposition of the matrix ''A'' once and then solve the triangular matrices for the different b, rather than using Gaussian elimination each time. The matrices ''L'' and ''U'' could be thought to have "encoded" the Gaussian elimination process.
The cost of solving a system of linear equations is approximately floating-point operations if the matrix has size . This makes it twice as fast as algorithms based on QR decomposition, which costs about floating-point operations when Householder reflections are used. For this reason, LU decomposition is usually preferred.
Inverting a matrix
When solving systems of equations, ''b'' is usually treated as a vector with a length equal to the height of matrix ''A''. In matrix inversion however, instead of vector ''b'', we have matrix ''B'', where ''B'' is an ''n''-by-''p'' matrix, so that we are trying to find a matrix ''X'' (also a ''n''-by-''p'' matrix):
:
We can use the same algorithm presented earlier to solve for each column of matrix ''X''. Now suppose that ''B'' is the identity matrix of size ''n''. It would follow that the result ''X'' must be the inverse of ''A''.
Computing the determinant
Given the LUP decomposition of a square matrix ''A'', the determinant of ''A'' can be computed straightforwardly as
:
The second equation follows from the fact that the determinant of a triangular matrix is simply the product of its diagonal entries, and that the determinant of a permutation matrix is equal to (−1)''S'' where ''S'' is the number of row exchanges in the decomposition.
In the case of LU decomposition with full pivoting, also equals the right-hand side of the above equation, if we let ''S'' be the total number of row and column exchanges.
The same method readily applies to LU decomposition by setting ''P'' equal to the identity matrix.
History
The LU decomposition is related to elimination of linear systems of equations, as e.g. described by Ralston. The solution of ''N'' linear equations in ''N'' unknowns by elimination was already known to ancient Chinese. Before Gauss many mathematicians in Eurasia were performing and perfecting it yet as the method became relegated to school grade, few of them left any detailed descriptions. Thus the name Gaussian elimination is only a convenient abbreviation of a complex history.
The Polish astronomer Tadeusz Banachiewicz introduced the LU decomposition in 1938. To quote: "It appears that Gauss and Doolittle applied the method
f eliminationonly to symmetric equations. More recent authors, for example, Aitken, Banachiewicz, Dwyer, and Crout … have emphasized the use of the method, or variations of it, in connection with non-symmetric problems … Banachiewicz … saw the point … that the basic problem is really one of matrix factorization, or “decomposition” as he called it."
Banachiewicz was the first to consider elimination in terms of matrices and in this way formulated LU decomposition, as demonstrated by his graphic illustration. His calculations follow ordinary matrix ones, yet notation deviates in that he preferred to write one factor transposed, to be able to multiply them mechanically column by column, by sliding ruler down consecutive rows of both (using arithmometer
The arithmometer () was the first digital data, digital mechanical calculator strong and reliable enough to be used daily in an office environment. This calculator could add and subtract two numbers directly and perform Multiplication algorithm, ...
). Combined with swapped order of indices his formulae in modern notation read ,, where ,
Code examples
Fortran90 code example
Module mlu
Implicit None
Integer, Parameter :: SP = Kind(1d0) ! set I/O real precision
Private
Public luban, lusolve
Contains
Subroutine luban (a, tol, g, h, ip, condinv, detnth)
! By Banachiewicz (1938, hereafter B38) LU decomposition method calculates such
! triangles L=G^T, and U=H that square B=A^T=G^TH=LU. Partial pivoting
! by column permutation IP(:) is modern addition.
! Within the code a, g correspond to B38 A^T and G^T, so that a=gh holds.
!
! Normal use is for square A, however for RHS l already known
! input of (A, l)^T yields (L, y^T)^T where x in L^Tx=y is solution of Ax=l.
Real (SP), Intent (In) :: a (:, :)! input matrix A(m,n), n<=m
Real (SP), Intent (In) :: tol ! tolerance for near zero pivot
Real (SP), Intent (Out) :: g (size(a,dim=1), size(a,dim=2)) ! L(m,n)
Real (SP), Intent (Out) :: h (size(a,dim=2), size(a,dim=2)) ! U(n,n)
! note U columns are permuted
Real (SP), Intent (Out) :: condinv ! 1/cond(A), 0 for singular A
Real (SP), Intent (Out) :: detnth ! sign*Abs(det(A))**(1/n)
Integer, Intent (Out) :: ip (size(a, dim=2)) ! columns permutation
!
Integer :: k, n, j, l, isig
Real (SP) :: tol0, pivmax, pivmin, piv
!
n = size (a, dim=2)
tol0 = Max (tol, 3._SP*epsilon(tol0))! use default for tol=0
!
! Rectangular A and G are permitted under condition:
If (n > size(a, dim=1) .Or. n < 1) Stop 91
Forall (k=1:n) ip (k) = k
h = 0._SP
g = 0._SP
isig = 1
detnth = 0._SP
pivmax = Maxval (Abs (a(1, :)))
pivmin = pivmax
!
Do k = 1, n
! Banachiewicz (1938) Eq. (2.3)
h(k, ip(k:)) = a(k, ip(k:)) - Matmul(g(k, :k-1), h(:k-1, ip(k:)))
!
! Find row pivot
j = (Maxloc(Abs(h(k, ip(k:))), dim=1) + k-1)
If (j /= k) Then ! Swap columns j and k
isig = - isig ! Change Det(A) sign because of permutation
l = ip (k)
ip (k) = ip (j)
ip (j) = l
End If
piv = Abs (h(k, ip(k)))
pivmax = Max (piv, pivmax) ! Adjust condinv
pivmin = Min (piv, pivmin)
If (piv < tol0) Then ! singular matrix
isig = 0
pivmax = 1._SP
Exit
Else ! Account for pivot contribution to Det(A) sign and value
If (h(k, ip(k)) < 0._SP) isig = - isig
detnth = detnth + Log (piv)
End If
!
! Transposed Banachiewicz (1938) Eq. (2.4)
g (k+1:, k) = (a(k+1:, ip(k)) - &
Matmul(g(k+1:, :k-1), h(:k-1, ip(k)))) / h (k, ip(k))
g (k, k) = 1._SP
End Do
!
detnth = isig * Exp (detnth/n)
condinv = Abs (isig) * pivmin / pivmax
! Test for square A(n,n) by uncommenting below
! Print *, ', AQ-LU, ',Maxval (Abs(a(:,ip(:))-Matmul(g, h(:,ip(:)))))
End Subroutine luban
Subroutine lusolve(l,u,ip,x)
! Solves Ax=a system using triangle factors LU=A
Real (SP), Intent (In) :: l (:, :) ! lower triangle matrix L(n,n)
Real (SP), Intent (In) :: u (:, :) ! upper triangle matrix U(n,n)
Integer, Intent (In) :: ip (:) ! columns permutation IP(n)
Real (SP), Intent (InOut) :: x (:, :) ! X(n,m) for m sets of input
! right hand sides replaced with output unknowns
Integer :: n, m, i, j
n = size(ip)
m = size(x, dim=2)
If (n<1.Or.m<1.Or.Any( ,n=shape(l)).Or.Any(shape(l)/=shape(u)).Or. &
n/=size(x,dim=1)) Stop 91
Do i = 1, m
Do j = 1, n
x(j,i) = x(j,i)-dot_product(x(:j-1,i),l(j,:j-1))
End Do
Do j = n, 1, -1
x(j,i) = (x(j,i)-dot_product(x(j+1:,i),u(j,ip(j+1:)))) / &
u(j,ip(j))
End Do
End Do
End Subroutine lusolve
End Module mlu
C code example
/* INPUT: A - array of pointers to rows of a square matrix having dimension N
* Tol - small tolerance number to detect failure when the matrix is near degenerate
* OUTPUT: Matrix A is changed, it contains a copy of both matrices L-E and U as A=(L-E)+U such that P*A=L*U.
* The permutation matrix is not stored as a matrix, but in an integer vector P of size N+1
* containing column indexes where the permutation matrix has "1". The last element P S+N,
* where S is the number of row exchanges needed for determinant computation, det(P)=(-1)^S
*/
int LUPDecompose(double **A, int N, double Tol, int *P)
/* INPUT: A,P filled in LUPDecompose; b - rhs vector; N - dimension
* OUTPUT: x - solution vector of A*x=b
*/
void LUPSolve(double **A, int *P, double *b, int N, double *x)
/* INPUT: A,P filled in LUPDecompose; N - dimension
* OUTPUT: IA is the inverse of the initial matrix
*/
void LUPInvert(double **A, int *P, int N, double **IA)
/* INPUT: A,P filled in LUPDecompose; N - dimension.
* OUTPUT: Function returns the determinant of the initial matrix
*/
double LUPDeterminant(double **A, int *P, int N)
C# code example
public class SystemOfLinearEquations
MATLAB code example
function LU = LUDecompDoolittle(A)
n = length(A);
LU = A;
for k = 2:n
for i = 1 : k-1
lamda = LU(k,i) / LU (i, i);
LU(k,i) = lamda;
LU(k,i+1:n) = LU(k,i+1:n) - LU(i,i+1:n) * lamda;
end
end
end
function x = SolveLinearSystem(LU, B)
n = length(LU);
y = zeros(size(B));
% find solution of Ly = B
for i = 1:n
y(i,:) = B(i,:) - LU(i,1:i)*y(1:i,:);
end
% find solution of Ux = y
x = zeros(size(B));
for i = n:(-1):1
x(i,:) = (y(i,:) - LU(i,(i + 1):n)*x((i + 1):n,:))/LU(i, i);
end
end
A = 4 3 3; 6 3 3; 3 4 3 LU = LUDecompDoolittle(A)
B = 1 2 3; 4 5 6; 7 8 9; 10 11 12
x = SolveLinearSystem(LU, B)
A * x
See also
* Block LU decomposition
* Bruhat decomposition
* Cholesky decomposition
* Crout matrix decomposition
* Incomplete LU factorization
* LU Reduction
* Matrix decomposition
* QR decomposition
Notes
References
* .
* .
* .
* .
* .
* .
* . See Section 3.5. ''N'' − 1
* .
* .
* .
* .
* .
* .
* .
*
External links
References
LU decomposition
on ''MathWorld''.
LU decomposition
on ''Math-Linux''.
at ''Holistic Numerical Methods Institute''
MATLAB reference.
Computer code
LAPACK
is a collection of FORTRAN subroutines for solving dense linear algebra problems
ALGLIB
includes a partial port of the LAPACK to C++, C#, Delphi, etc.
Prof. J. Loomis, University of Dayton
The University of Dayton (UD) is a Private university, private, Catholic research university in Dayton, Ohio, United States. Founded in 1850 by the Society of Mary (Marianists), Society of Mary, it is one of three Marianist universities in the U ...
C code
Mathematics Source Library
Rust code
LU in X10
Online resources
WebApp descriptively solving systems of linear equations with LU Decomposition
Matrix Calculator with steps, including LU decomposition
LU Decomposition Tool
uni-bonn.de
LU Decomposition
by Ed Pegg, Jr., The Wolfram Demonstrations Project
The Wolfram Demonstrations Project is an open-source collection of interactive programmes called Demonstrations. It is hosted by Wolfram Research. At its launch, it contained 1300 demonstrations but has grown to over 10,000. The site won a Pa ...
, 2007.
{{Numerical linear algebra
Matrix decompositions
Numerical linear algebra
Articles with example C code
Articles with example C Sharp code
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