NAG Library Chapter Introduction
F07 – Linear Equations (LAPACK)
1 Scope of the Chapter
This chapter provides routines for the solution of systems of simultaneous linear equations, and associated computations. It provides routines for
- – matrix factorizations;
- – solution of linear equations;
- – estimating matrix condition numbers;
- – computing error bounds for the solution of linear equations;
- – matrix inversion;
- – computing scaling factors to equilibrate a matrix.
Routines are provided for both
real and
complex data.
For a general introduction to the solution of systems of linear equations, you should turn first to the
F04 Chapter Introduction. The decision trees, in
Section 4 in the F04 Chapter Introduction, direct you to the most appropriate routines in
Chapters F04 or
F07 for solving your particular problem. In particular,
Chapters F04 and
F07 contain
Black Box (or
driver) routines which enable some standard types of problem to be solved by a call to a single routine. Where possible, routines in
Chapter F04 call
Chapter F07 routines to perform the necessary computational tasks.
There are two types of driver routines in this chapter: simple drivers which just return the solution to the linear equations; and expert drivers which also return condition and error estimates and, in many cases, also allow equilibration. The simple drivers for real matrices have names of the form
F07_AF (D__SV)
and for complex matrices have names of the form
F07_NF (Z__SV).
The expert drivers for real matrices have names of the form
F07_BF (D__SVX)
and for complex matrices have names of the form
F07_PF (Z__SVX).
The routines in this chapter (
Chapter F07) handle only
dense and
band matrices (not matrices with more specialized structures, or general sparse matrices).
The routines in this chapter have all been derived from the LAPACK project (see
Anderson et al. (1999)). They have been designed to be efficient on a wide range of high-performance computers, without compromising efficiency on conventional serial machines.
2 Background to the Problems
This section is only a brief introduction to the numerical solution of systems of linear equations. Consult a standard textbook, for example
Golub and Van Loan (1996) for a more thorough discussion.
2.1 Notation
We use the standard notation for a system of simultaneous linear equations:
where
A is the
coefficient matrix,
b is the
right-hand side, and
x is the
solution.
A is assumed to be a square matrix of order
n.
If there are several right-hand sides, we write
where the columns of
B are the individual right-hand sides, and the columns of
X are the corresponding solutions.
We also use the following notation, both here and in the routine documents:
| x^ |
a computed solution to Ax=b, (which usually differs from the exact solution x because of round-off error) |
|
r
=
b-Ax^
|
the residual corresponding to the computed solution x^ |
|
x∞=
maxi
xi
|
the ∞-norm of the vector x |
|
x1
=
∑
j=1
n
xj
|
the 1-norm of the vector x |
|
A∞
=
maxi
∑j
aij
|
the ∞-norm of the matrix A |
|
A1
=
maxj
∑
i=1
n
aij
|
the 1-norm of the matrix A |
| x |
the vector with elements xi |
| A |
the matrix with elements aij |
Inequalities of the form A≤B are interpreted component-wise, that is aij≤bij for all i,j.
2.2 Matrix Factorizations
If A is upper or lower triangular, Ax=b can be solved by a straightforward process of backward or forward substitution.
Otherwise, the solution is obtained after first factorizing A, as follows.
General matrices (LU factorization with partial pivoting)
where
P is a permutation matrix,
L is lower-triangular with diagonal elements equal to
1, and
U is upper-triangular; the permutation matrix
P (which represents row interchanges) is needed to ensure numerical stability.
Symmetric positive definite matrices (Cholesky factorization)
where
U is upper triangular and
L is lower triangular.
Symmetric positive semidefinite matrices (pivoted Cholesky factorization)
where
P is a permutation matrix,
U is upper triangular and
L is lower triangular. The permutation matrix
P (which represents row-and-column interchanges) is needed to ensure numerical stability and to reveal the numerical rank of
A.
Symmetric indefinite matrices (Bunch–Kaufman factorization)
where
P is a permutation matrix,
U is upper triangular,
L is lower triangular, and
D is a block diagonal matrix with diagonal blocks of order
1 or
2;
U and
L have diagonal elements equal to
1, and have
2 by
2 unit matrices on the diagonal corresponding to the
2 by
2 blocks of
D. The permutation matrix
P (which represents symmetric row-and-column interchanges) and the
2 by
2 blocks in
D are needed to ensure numerical stability. If
A is in fact positive definite, no interchanges are needed and the factorization reduces to
A=UDUT or
A=LDLT with diagonal
D, which is simply a variant form of the Cholesky factorization.
2.3 Solution of Systems of Equations
Given one of the above matrix factorizations, it is straightforward to compute a solution to Ax=b by solving two subproblems, as shown below, first for y and then for x. Each subproblem consists essentially of solving a triangular system of equations by forward or backward substitution; the permutation matrix P and the block diagonal matrix D introduce only a little extra complication:
General matrices ( LU
factorization)Symmetric positive definite matrices (Cholesky factorization)Symmetric indefinite matrices (Bunch–Kaufman factorization) 2.4 Sensitivity and Error Analysis
2.4.1 Normwise error bounds
Frequently, in practical problems the data A and b are not known exactly, and it is then important to understand how uncertainties or perturbations in the data can affect the solution.
If
x is the exact solution to
Ax=b, and
x+δx is the exact solution to a perturbed problem
A+δAx+δx=b+δb, then
where
κA is the
condition number of
A defined by
In other words, relative errors in
A or
b may be amplified in
x by a factor
κA.
Section 2.4.2 discusses how to compute or estimate
κA.
Similar considerations apply when we study the effects of rounding errors introduced by computation in finite precision. The effects of rounding errors can be shown to be equivalent to perturbations in the original data, such that
δA
A
and
δb
b
are usually at most pnε, where ε is the machine precision and pn is an increasing function of n which is seldom larger than 10n (although in theory it can be as large as 2n-1).
In other words, the computed solution x^ is the exact solution of a linear system A+δAx^=b+δb which is close to the original system in a normwise sense.
2.4.2 Estimating condition numbers
The previous section has emphasised the usefulness of the quantity
κA in understanding the sensitivity of the solution of
Ax=b. To compute the value of
κA from equation
(3) is more expensive than solving
Ax=b in the first place. Hence it is standard practice to
estimate κA, in either the
1-norm or the
∞-norm, by a method which only requires
On2 additional operations, assuming that a suitable factorization of
A is available.
The method used in this chapter is Higham's modification of Hager's method (see
Higham (1988)). It yields an estimate which is never larger than the true value, but which seldom falls short by more than a factor of
3 (although artificial examples can be constructed where it is much smaller). This is acceptable since it is the order of magnitude of
κA which is important rather than its precise value.
Because κA is infinite if A is singular, the routines in this chapter actually return the reciprocal of κA.
2.4.3 Scaling and Equilibration
The condition of a matrix and hence the accuracy of the computed solution, may be improved by scaling; thus if
D1 and
D2 are diagonal matrices with positive diagonal elements, then
is the scaled matrix. A general matrix is said to be
equilibrated if it is scaled so that the lengths of its rows and columns have approximately equal magnitude. Similarly a general matrix is said to be
row-equilibrated (column-equilibrated) if it is scaled so that the lengths of its rows (columns) have approximately equal magnitude. Note that row scaling can affect the choice of pivot when partial pivoting is used in the factorization of
A.
A symmetric or Hermitian positive definite matrix is said to be equilibrated if the diagonal elements are all approximately equal to unity.
For further information on scaling and equilibration see Section 3.5.2 of
Golub and Van Loan (1996), Section 7.2, 7.3 and 9.8 of
Higham (1988) and Section 5 of Chapter 4 of
Wilkinson (1965).
Routines are provided to return the scaling factors that equilibrate a matrix for general, general band, symmetric and Hermitian positive definite and symmetric and Hermitian positive definite band matrices.
2.4.4 Componentwise error bounds
A disadvantage of normwise error bounds is that they do not reflect any special structure in the data A and b – that is, a pattern of elements which are known to be zero – and the bounds are dominated by the largest elements in the data.
Componentwise error bounds overcome these limitations. Instead of the normwise relative error, we can bound the relative error in
each component of
A and
b:
where the
component-wise backward error bound ω is given by
Routines are provided in this chapter which compute
ω, and also compute a
forward error bound which is sometimes much sharper than the normwise bound given earlier:
Care is taken when computing this bound to allow for rounding errors in computing r. The norm A-1.r∞ is estimated cheaply (without computing A-1) by a modification of the method used to estimate κA.
2.4.5 Iterative refinement of the solution
If
x^ is an approximate computed solution to
Ax=b, and
r is the corresponding residual, then a procedure for
iterative refinement of
x^ can be defined as follows, starting with
x0=x^:
- for i=0,1,…, until convergence
| compute |
ri=b-Axi |
| solve |
Adi=ri |
| compute |
xi+1=xi+di |
In
Chapter F04, routines are provided which perform this procedure using
additional precision to compute
r, and are thus able to reduce the
forward error to the level of
machine precision.
The routines in this chapter do not use additional precision to compute r, and cannot guarantee a small forward error, but can guarantee a small backward error (except in rare cases when A is very ill-conditioned, or when A and x are sparse in such a way that A.x has a zero or very small component). The iterations continue until the backward error has been reduced as much as possible; usually only one iteration is needed.
2.5 Matrix Inversion
It is seldom necessary to compute an explicit inverse of a matrix. In particular, do
not attempt to solve
Ax=b by first computing
A-1 and then forming the matrix-vector product
x=A-1b; the procedure described in
Section 2.3 is more efficient and more accurate.
However, routines are provided for the rare occasions when an inverse is needed, using one of the factorizations described in
Section 2.2.
2.6 Packed Storage Formats
Routines which handle symmetric matrices are usually designed so that they use either the upper or lower triangle of the matrix; it is not necessary to store the whole matrix. If the upper or lower triangle is stored conventionally in the upper or lower triangle of a two-dimensional array, the remaining elements of the array can be used to store other useful data.
However, that is not always convenient, and if it is important to economize on storage, the upper or lower triangle can be stored in a one-dimensional array of length nn+1/2 or a two-dimensional array with nn+1/2 elements; in other words, the storage is almost halved.
The one-dimensional array storage format is referred to as packed storage; it is described in
Section 3.3.2. The two-dimensional array storage format is referred to as Rectangular Full Packed (RFP) format; it is described in
Section 3.3.3. They may also be used for triangular matrices.
Routines designed for these packed storage formats perform the same number of arithmetic operations as routines which use conventional storage. Those using a packed one-dimensional array are usually less efficient, especially on high-performance computers, so there is then a trade-off between storage and efficiency. The RFP routines are as efficient as for conventional storage, although only a small subset of routines use this format.
2.7 Band and Tridiagonal Matrices
A
band matrix is one whose nonzero elements are confined to a relatively small number of subdiagonals or superdiagonals on either side of the main diagonal. A
tridiagonal matrix is a special case of a band matrix with just one subdiagonal and one superdiagonal. Algorithms can take advantage of bandedness to reduce the amount of work and storage required. The storage scheme used for band matrices is described in
Section 3.3.4.
The LU factorization for general matrices, and the Cholesky factorization for symmetric and Hermitian positive definite matrices both preserve bandedness. Hence routines are provided which take advantage of the band structure when solving systems of linear equations.
The Cholesky factorization preserves bandedness in a very precise sense: the factor U or L has the same number of superdiagonals or subdiagonals as the original matrix. In the LU factorization, the row-interchanges modify the band structure: if A has kl subdiagonals and ku superdiagonals, then L is not a band matrix but still has at most kl nonzero elements below the diagonal in each column; and U has at most kl+ku superdiagonals.
The Bunch–Kaufman factorization does not preserve bandedness, because of the need for symmetric row-and-column permutations; hence no routines are provided for symmetric indefinite band matrices.
The inverse of a band matrix does not in general have a band structure, so no routines are provided for computing inverses of band matrices.
2.8 Block Partitioned Algorithms
Many of the routines in this chapter use what is termed a
block partitioned algorithm. This means that at each major step of the algorithm a
block of rows or columns is updated, and most of the computation is performed by matrix-matrix operations on these blocks. The matrix-matrix operations are performed by calls to the Level 3 BLAS
(see
Chapter F06),
which are the key to achieving high performance on many modern computers. See
Golub and Van Loan (1996) or
Anderson et al. (1999) for more about block partitioned algorithms.
The performance of a block partitioned algorithm varies to some extent with the block size – that is, the number of rows or columns per block. This is a machine-dependent parameter, which is set to a suitable value when the library is implemented on each range of machines. You do not normally need to be aware of what value is being used. Different block sizes may be used for different routines. Values in the range 16 to 64 are typical.
On some machines there may be no advantage from using a block partitioned algorithm, and then the routines use an
unblocked algorithm (effectively a block size of
1), relying solely on calls to the Level 2 BLAS
(see
Chapter F06
again).
The only situation in which you need some awareness of the block size is when it affects the amount of workspace to be supplied to a particular routine. This is discussed in
Section 3.4.3.
2.9 Mixed Precision LAPACK Routines
Some LAPACK routines use mixed precision arithmetic in an effort to solve problems more efficiently on modern hardware. They work by converting a double precision problem into an equivalent single precision problem, solving it and then using iterative refinement in double precision to find a full precision solution to the original problem. The method may fail if the problem is too ill-conditioned to allow the initial single precision solution, in which case the routines fall back to solve the original problem entirely in double precision. The vast majority of problems are not so ill-conditioned, and in those cases the technique can lead to significant gains in speed without loss of accuracy. This is particularly true on machines where double precision arithmetic is significantly slower than single precision.
3 Recommendations on Choice and Use of Available Routines
3.1 Available Routines
Tables 1 to
8 in
Section 3.5 show the routines which are provided for performing different computations on different types of matrices.
Tables 1 to
4 show routines for real matrices;
Tables 5 to
8 show routines for complex matrices. Each entry in the table gives the NAG routine name and the LAPACK double precision name (see
Section 3.2).
Routines are provided for the following types of matrix:
- general
- general band
- general tridiagonal
- symmetric or Hermitian positive definite
- symmetric or Hermitian positive definite (packed storage)
- symmetric or Hermitian positive definite (RFP storage)
- symmetric or Hermitian positive definite band
- symmetric or Hermitian positive definite tridiagonal
- symmetric or Hermitian indefinite
- symmetric or Hermitian indefinite (packed storage)
- triangular
- triangular (packed storage)
- triangular (RFP storage)
- triangular band
For each of the above types of matrix (except where indicated), routines are provided to perform the following computations:
| (a) |
(except for RFP matrices) solve a system of linear equations (driver routines); |
| (b) |
(except for RFP matrices) solve a system of linear equations with condition and error estimation (expert drivers); |
| (c) |
(except for triangular matrices) factorize the matrix (see Section 2.2); |
| (d) |
solve a system of linear equations, using the factorization (see Section 2.3); |
| (e) |
(except for RFP matrices) estimate the condition number of the matrix, using the factorization (see Section 2.4.2); these routines also require the norm of the original matrix (except when the matrix is triangular) which may be computed by a routine in
Chapter F06; |
| (f) |
(except for RFP matrices) refine the solution and compute forward and backward error bounds (see Sections 2.4.4 and 2.4.5); these routines require the original matrix and right-hand side, as well as the factorization returned from (a) and the solution returned from (b); |
| (g) |
(except for band and tridiagonal matrices) invert the matrix, using the factorization (see Section 2.5); |
| (h) |
(except for tridiagonal, symmetric indefinite, triangular and RFP matrices) compute scale factors to equilibrate the matrix (see Section 2.4.3). |
Thus, to solve a particular problem, it is usually only necessary to call a single driver routine, but alternatively two or more routines may be called in succession. This is illustrated in the example programs in the routine documents.
3.2 NAG Names and LAPACK Names
As well as the NAG routine name (beginning F07),
Tables 1 to
8 show the LAPACK routine names in double precision.
The routines may be called either by their NAG names or by their LAPACK names. When using the NAG Library, the double precision form of the LAPACK name must be used (beginning with D- or Z-).
References to
Chapter F07 routines in the manual normally include the LAPACK double precision names, for example,
F07ADF (DGETRF).
The LAPACK routine names follow a simple
scheme (which is similar to that used for the BLAS in
Chapter F06).
Most names have the structure
XYYZZZ, where the components have the following meanings:
| – |
the initial letter X indicates the data type (real or complex) and precision:
| S |
– real, single precision (in Fortran 77, REAL) |
| D |
– real, double precision (in Fortran 77, DOUBLE PRECISION) |
| C |
– complex, single precision (in Fortran 77, COMPLEX) |
| Z |
– complex, double precision (in Fortran 77, COMPLEX*16 or DOUBLE COMPLEX) |
|
| – |
exceptionally, the mixed precision LAPACK routines described in Section 2.9 replace the initial first letter by a pair of letters, as:
| DS |
– double precision routine using single precision internally |
| ZC |
– double complex routine using single precision complex internally |
|
| – |
the letters YY indicate the type of the matrix A (and in some cases its storage scheme):
| GE |
– general |
| GB |
– general band |
| PO |
– symmetric or Hermitian positive definite |
| PF |
– symmetric or Hermitian positive definite (RFP storage) |
| PP |
– symmetric or Hermitian positive definite (packed storage) |
| PB |
– symmetric or Hermitian positive definite band |
| SY |
– symmetric indefinite |
| SF |
– symmetric indefinite (RFP storage) |
| SP |
– symmetric indefinite (packed storage) |
| HE |
– (complex) Hermitian indefinite |
| HF |
– (complex) Hermitian indefinite (RFP storage) |
| HP |
– (complex) Hermitian indefinite (packed storage) |
| GT |
– general tridiagonal |
| PT |
– symmetric or Hermitian positive definite tridiagonal |
| TR |
– triangular |
| TF |
– triangular (RFP storage) |
| TP |
– triangular (packed storage) |
| TB |
– triangular band |
|
| – |
the last two or three letters ZZ or ZZZ indicate the computation performed. Examples are:
| TRF |
– triangular factorization |
| TRS |
– solution of linear equations, using the factorization |
| CON |
– estimate condition number |
| RFS |
– refine solution and compute error bounds |
| TRI |
– compute inverse, using the factorization |
|
Thus the routine
DGETRF performs a triangular factorization of a real general matrix in double precision; the corresponding routine for a complex general matrix is
ZGETRF.
3.3 Matrix Storage Schemes
In this chapter the following different storage schemes are used for matrices:
- – conventional storage in a two-dimensional array;
- – rectangular full packed (RFP) storage for symmetric, Hermitian or triangular matrices;
- – packed and RFP storage for symmetric, Hermitian or triangular matrices;
- – band storage for band matrices.
These storage schemes are compatible with those used in
Chapter F06
(especially in the BLAS) and
Chapter F08, but different schemes for packed or band storage are used in a few older routines in
Chapters F01,
F02,
F03 and
F04.
In the examples below, * indicates an array element which need not be set and is not referenced by the routines.
The examples illustrate only the relevant part of the arrays; array arguments may of course have additional rows or columns, according to the usual rules for passing array arguments in Fortran 77.
3.3.1 Conventional storage
The default scheme for storing matrices is the obvious one: a matrix A is stored in a two-dimensional array A, with matrix element aij stored in array element Ai,j .
If a matrix is triangular (upper or lower, as specified by the parameter UPLO), only the elements of the relevant triangle are stored; the remaining elements of the array need not be set. Such elements are indicated by * in the examples below.
For example, when
n=4:
| UPLO |
Triangular matrix A |
Storage in array A |
| 'U' |
a11
a12
a13
a14
a22
a23
a24
a33
a34
a44
|
a11
a12
a13
a14
*
a22
a23
a24
*
*
a33
a34
*
*
*
a44
|
| 'L' |
a11
a21
a22
a31
a32
a33
a41
a42
a43
a44
|
a11
*
*
*
a21
a22
*
*
a31
a32
a33
*
a41
a42
a43
a44
|
Routines which handle symmetric or Hermitian matrices allow for either the upper or lower triangle of the matrix (as specified by UPLO) to be stored in the corresponding elements of the array; the remaining elements of the array need not be set.
For example, when
n=4:
| UPLO |
Hermitian matrix A |
Storage in array A |
| 'U' |
a11
a12
a13
a14
a-12
a22
a23
a24
a-13
a-23
a33
a34
a-14
a-24
a-34
a44
|
a11
a12
a13
a14
*
a22
a23
a24
*
*
a33
a34
*
*
*
a44
|
| 'L' |
a11
a-21
a-31
a-41
a21
a22
a-32
a-42
a31
a32
a33
a-43
a41
a42
a43
a44
|
a11
*
*
*
a21
a22
*
*
a31
a32
a33
*
a41
a42
a43
a44
|
3.3.2 Packed storage
Symmetric, Hermitian or triangular matrices may be stored more compactly, if the relevant triangle (again as specified by
UPLO)
is packed by columns in a one-dimensional array. In this chapter, as in
Chapters F06 and
F08,
arrays which hold matrices in packed storage, have names ending in P. For a matrix of order
n, the array must have at least
nn+1/2 elements. So:
- if UPLO='U', aij is stored in APi+jj-1/2 for i≤j;
- if UPLO='L', aij is stored in APi+2n-jj-1/2 for j≤i.
For example:
| |
Triangle of matrix A |
Packed storage in array AP |
| UPLO='U' |
a11
a12
a13
a14
a22
a23
a24
a33
a34
a44
|
a11
a12
a22
a13
a23
a33
a14
a24
a34
a44
|
| UPLO='L' |
a11
a21
a22
a31
a32
a33
a41
a42
a43
a44
|
a11
a21
a31
a41
a22
a32
a42
a33
a43
a44 |
Note that for real symmetric matrices, packing the upper triangle by columns is equivalent to packing the lower triangle by rows; packing the lower triangle by columns is equivalent to packing the upper triangle by rows. (For complex Hermitian matrices, the only difference is that the off-diagonal elements are conjugated.)
3.3.3 Rectangular Full Packed (RFP) Storage
The rectangular full packed (RFP) storage format offers the same savings in storage as the packed storage format (described in
Section 3.3.2), but is likely to be much more efficient in general since the block structure of the matrix is maintained. This structure can be exploited using block partition algorithms (see
Section 2.8 in a similar way to matrices that use conventional storage.
Figure 1
Figure 1 gives a graphical representation of the key idea of RFP for the particular case of a lower triangular matrix of even dimensions. In all cases the original triangular matrix of stored elements is separated into a trapezoidal part and a triangular part. The number of columns in these two parts is equal when the dimension of the matrix is even,
n=2k, while the trapezoidal part has
k+1 columns when
n=2k+1. The smaller part is then transposed and fitted onto the trapezoidal part forming a rectangle. The rectangle has dimensions
2k+1 and
q, where
q=k when
n is even and
q=k+1 when
n is odd.
For routines using RFP there is the option of storing the rectangle as described above (TRANSR='N') or its transpose (TRANSR='T').
As an example, we first consider RFP for the case n=2k with k=3.
If
TRANSR='N', then AF holds A as follows:
- For UPLO='U' the upper trapezoid
AF1:61:3
consists of the last three columns of A upper. The lower triangle
AF5:71:3
consists of the transpose of the first three columns of A upper.
- For UPLO='L' the lower trapezoid
AF2:71:3
consists of the first three columns of A lower. The upper triangle
AF1:31:3
consists of the transpose of the last three columns of A lower.
If TRANSR='T', then AF in both UPLO cases is just the transpose of AF as defined when TRANSR='N'.
| UPLO |
Triangle of matrix A |
Rectangular Full Packed matrix AF |
| TRANSR='N' |
TRANSR='T' |
| 'U' |
00
01
02
03
04
05
11
12
13
14
15
22
23
24
25
33
34
35
44
45
55
|
03
04
05
13
14
15
23
24
25
33
34
35
00
44
45
01
11
55
02
12
22
|
03
13
23
33
00
01
02
04
1424
3444
11
12
05
1525
35
4555
22
|
|
'L'
|
00
10
11
20
21
22
30
31
32
33
40
41
42
43
44
50
51
52
53
54
55
|
33
43
53
00
44
54
10
11
55
20
21
22
30
31
32
40
41
42
50
51
52
|
33
00
10
20
30
40
50
43
44
11
21
31
41
51
53
54
55
22
32
42
52
|
Now we consider RFP for the case n=2k+1 and k=2.
If
TRANSR='N'. AF holds A as follows:
- if UPLO='U' the upper trapezoid
AF1:51:3
consists of the last three columns of A upper. The lower triangle
AF4:51:2
consists of the transpose of the first two columns of A upper;
- if UPLO='L' the lower trapezoid
AF1:51:3
consists of the first three columns of A lower. The upper triangle
AF1:22:3
consists of the transpose of the last two columns of A lower.
If TRANSR='T'. AF in both UPLO cases is just the transpose of AF as defined when TRANSR='N'.
| UPLO |
Triangle of matrix A |
Rectangular Full Packed matrix AF |
| TRANSR='N' |
TRANSR='T' |
| 'U' |
00
01
02
03
04
11
12
13
14
22
23
24
33
34
44
|
02
03
04
12
13
14
22
23
24
00
33
34
01
11
44
|
02
12
22
00
01
03
13
23
33
11
04
14
24
34
44
|
| 'L' |
00
10
11
20
21
22
30
31
32
33
40
41
42
43
44
|
00
33
43
10
11
44
20
21
22
30
31
32
40
41
42
|
00
10
20
30
40
50
33
11
21
31
41
51
43
44
22
32
42
52
|
Explicitly, AF is a one-dimensional array of length
nn+1/2 and contains the elements of A as follows:
- for UPLO='U', aij (or aji for TRANSR='T') is stored in
AF
2k+1
i-1
+
k
+
1
+
j
, for i=1,2,…,k and j=i,…,k, or otherwise in
AF
2k+1
j-1
+
i
-
k
, for i=k+1,n and j=i,…,q;
- for UPLO='L', aij (or aji for TRANSR='T') is stored in
AF
2k+1
j-1
+
i
+
q
-
k
, for j=1,2,…,q and i=j,…,n, or otherwise in
AF
2k+1
i-q
+
j
-
q
+
1
, for j=q+1,…,n and i=j,…,n.
3.3.4 Band storage
A band matrix with
kl subdiagonals and
ku superdiagonals may be stored compactly in a two-dimensional array with
kl+ku+1 rows and
n columns. Columns of the matrix are stored in corresponding columns of the array, and diagonals of the matrix are stored in rows of the array. This storage scheme should be used in practice only if
kl,
ku≪n, although the routines in
Chapters F07 and
F08 work correctly for all values of
kl and
ku. In
Chapters F07 and
F08 arrays which hold matrices in band storage have names ending in
B.
To be precise, elements of matrix elements aij are stored as follows:
- aij is stored in
ABku+1+i-jj
for
max1,j-ku
≤
i
≤
minn,j+kl
.
For example, when
n=5,
kl=2 and
ku=1:
| Band matrix A |
Band storage in array AB |
|
a11
a12
a21
a22
a23
a31
a32
a33
a34
a42
a43
a44
a45
a53
a54
a55
|
*
a12
a23
a34
a45
a11
a22
a33
a44
a55
a21
a32
a43
a54
*
a31
a42
a53
*
*
|
The elements marked * in the upper left and lower right corners of the array AB need not be set, and are not referenced by the routines.
Note: when a general band matrix is supplied for LU factorization, space must be allowed to store an additional kl superdiagonals, generated by fill-in as a result of row interchanges. This means that the matrix is stored according to the above scheme, but with kl+ku superdiagonals.
Triangular band matrices are stored in the same format, with either kl=0 if upper triangular, or ku=0 if lower triangular.
For symmetric or Hermitian band matrices with k subdiagonals or superdiagonals, only the upper or lower triangle (as specified by UPLO) need be stored:
- if UPLO='U', aij is stored in ABk+1+i-j,j for max1,j-k≤i≤j;
- if UPLO='L', aij is stored in AB1+i-j,j for j≤i≤minn,j+k.
For example, when
n=5 and
k=2:
| UPLO |
Hermitian band matrix A |
Band storage in array AB |
| 'U' |
a11
a12
a13
a-12
a22
a23
a24
a-13
a-23
a33
a34
a35
a-24
a-34
a44
a45
a-35
a-45
a55
|
*
*
a13
a24
a35
*
a12
a23
a34
a45
a11
a22
a33
a44
a55
|
| 'L' |
a11
a-21
a-31
a21
a22
a-32
a-42
a31
a32
a33
a-43
a-53
a42
a43
a44
a-54
a53
a54
a55
|
a11
a22
a33
a44
a55
a21
a32
a43
a54
*
a31
a42
a53
*
*
|
Note that different storage schemes for band matrices are used by some routines in
Chapters F01,
F02,
F03 and
F04.
3.3.5 Unit triangular matrices
Some routines in this chapter have an option to handle unit triangular matrices (that is, triangular matrices with diagonal elements =1). This option is specified by an argument DIAG. If DIAG='U' (Unit triangular), the diagonal elements of the matrix need not be stored, and the corresponding array elements are not referenced by the routines. The storage scheme for the rest of the matrix (whether conventional, packed or band) remains unchanged.
3.3.6 Real diagonal elements of complex matrices
Complex Hermitian matrices have diagonal elements that are by definition purely real. In addition, complex triangular matrices which arise in Cholesky factorization are defined by the algorithm to have real diagonal elements.
If such matrices are supplied as input to routines in
Chapters F07 and
F08, the imaginary parts of the diagonal elements are not referenced, but are assumed to be zero. If such matrices are returned as output by the routines, the computed imaginary parts are explicitly set to zero.
3.4 Parameter Conventions
3.4.1 Option parameters
Most routines in this chapter have one or more option parameters, of type CHARACTER. The descriptions in Section 5 of the routine documents refer only to upper-case values (for example UPLO='U' or 'L'); however, in every case, the corresponding lower-case characters may be supplied (with the same meaning). Any other value is illegal.
A longer character string can be passed as the actual parameter, making the calling program more readable, but only the first character is significant.
(This is a feature of Fortran 77.) For example:
CALL DGETRS('Transpose',...)
3.4.2 Problem dimensions
It is permissible for the problem dimensions (for example, M in
F07ADF (DGETRF), N or NRHS in
F07AEF (DGETRS)) to be passed as zero, in which case the computation (or part of it) is skipped. Negative dimensions are regarded as an error.
3.4.3 Length of work arrays
A few routines implementing block partitioned algorithms require workspace sufficient to hold one block of rows or columns of the matrix if they are to achieve optimum levels of performance — for example, workspace of size n×nb, where nb is the optimum block size. In such cases, the actual declared length of the work array must be passed as a separate parameter LWORK, which immediately follows WORK in the parameter-list.
The routine will still perform correctly when less workspace is provided: it uses the largest block size allowed by the amount of workspace supplied, as long as this is likely to give better performance than the unblocked algorithm. On exit, WORK1 contains the minimum value of LWORK which would allow the routine to use the optimum block size; this value of LWORK may be used for subsequent runs.
If LWORK indicates that there is insufficient workspace to perform the unblocked algorithm, this is regarded as an illegal value of LWORK, and is treated like any other illegal parameter value (see
Section 3.4.4), though
WORK1 will still be set as described above.
If you are in doubt how much workspace to supply and are concerned to achieve optimum performance, supply a generous amount (assume a block size of 64, say), and then examine the value of WORK1 on exit.
3.4.4 Error-handling and the diagnostic parameter INFO
Routines in this chapter do not use the usual NAG Library error-handling mechanism, involving the parameter IFAIL. Instead they have a diagnostic parameter INFO. (Thus they preserve complete compatibility with the LAPACK specification.)
Whereas IFAIL is an Input/Output parameter and must be set before calling a routine, INFO is purely an Output parameter and need not be set before entry.
INFO indicates the success or failure of the computation, as follows:
- INFO=0: successful termination
- INFO>0: failure in the course of computation, control returned to the calling program
If the routine document specifies that the routine may terminate with INFO>0, then it is essential to test INFO on exit from the routine. (This corresponds to a soft failure in terms of the usual NAG error-handling terminology.) No error message is output.
All routines check that input parameters such as N or LDA or option parameters of type CHARACTER have permitted values. If an illegal value of the ith parameter is detected, INFO is set to -i, a message is output, and execution of the program is terminated. (This corresponds to a hard failure in the usual NAG terminology.)
3.5 Tables of Available Computational Routines
3.5.1 Real matrices
Each entry in the following tables, listing real matrices, gives:
- the NAG routine name and
- the double precision LAPACK routine name.
Table 1
Routines for real general matrices
Table 2
Routines for real symmetric positive definite matrices
Table 3
Routines for real symmetric indefinite matrices
Table 4
Routines for real triangular matrices
3.5.2 Complex matrices
Each entry in the following tables, listing complex matrices, gives:
- the NAG routine name and
- the double precision LAPACK routine name.
Table 5
Routines for complex general matrices
Table 6
Routines for complex Hermitian positive definite matrices
Table 7
Routines for complex Hermitian and symmetric indefinite matrices
Table 8
Routines for complex triangular matrices
4 Functionality Index
| Apply iterative refinement to the solution and compute error estimates: | | |
| after factorizing the matrix of coefficients: | | |
| Compute row and column scalings, | | |
| Condition number estimation: | | |
| after factorizing the matrix of coefficients: | | |
| LLH or UHU factorization: | | |
| LLT or UTU factorization: | | |
| after factorizing the matrix of coefficients: | | |
| complex triangular matrix, RFP storage, | | |
| real triangular matrix, RFP storage, | | |
| PLDLHPH or PUDUHPH factorization: | | |
| PLDLTPT or PUDUTPT factorization: | | |
| Solution of simultaneous linear equations: | | |
| after factorizing the matrix of coefficients: | | |
| expert drivers (with condition and error estimation): | | |
| complex Hermitian positive definite matrix, packed storage | | F07GNF (ZPPSV) |
| complex Hermitian positive definite matrix, using mixed precision | | F07FQF (ZCPOSV) |
| real symmetric positive definite matrix, packed storage | | F07GAF (DPPSV) |
| real symmetric positive definite matrix, using mixed precision | | F07FCF (DSPOSV) |
5 Auxiliary Routines Associated with Library Routine Parameters
None.
6 Routines Withdrawn or Scheduled for Withdrawal
None.
7 References
Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999)
LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia
Golub G H and Van Loan C F (1996)
Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore
Higham N J (1988) Algorithm 674: Fortran codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation
ACM Trans. Math. Software 14 381–396
Wilkinson J H (1965)
The Algebraic Eigenvalue Problem Oxford University Press, Oxford
