# Linear Algebra

This chapter documents the linear algebra functions of Octave. Reference material for many of these options may be found in Golub and Van Loan, Matrix Computations, 2nd Ed., Johns Hopkins, 1989, and in LAPACK Users' Guide, SIAM, 1992.

## Basic Matrix Functions

Loadable Function: aa = balance (a, opt)
Loadable Function: [dd, aa] = balance (a, opt)
Loadable Function: [cc, dd, aa, bb] = balance (a, b, opt)

`[dd, aa] = balance (a)` returns `aa = dd \ a * dd`. `aa` is a matrix whose row/column norms are roughly equal in magnitude, and `dd` = `p * d`, where `p` is a permutation matrix and `d` is a diagonal matrix of powers of two. This allows the equilibration to be computed without roundoff. Results of eigenvalue calculation are typically improved by balancing first.

`[cc, dd, aa, bb] = balance (a, b)` returns `aa` (`bb`) `= cc*a*dd (cc*b*dd)`), where `aa` and `bb` have non-zero elements of approximately the same magnitude and `cc` and `dd` are permuted diagonal matrices as in `dd` for the algebraic eigenvalue problem.

The eigenvalue balancing option `opt` is selected as follows:

`"N"`, `"n"`
No balancing; arguments copied, transformation(s) set to identity.
`"P"`, `"p"`
Permute argument(s) to isolate eigenvalues where possible.
`"S"`, `"s"`
Scale to improve accuracy of computed eigenvalues.
`"B"`, `"b"`
Permute and scale, in that order. Rows/columns of a (and b) that are isolated by permutation are not scaled. This is the default behavior.

Algebraic eigenvalue balancing uses standard LAPACK routines.

Generalized eigenvalue problem balancing uses Ward's algorithm (SIAM Journal on Scientific and Statistical Computing, 1981).

: cond (a)
Compute the (two-norm) condition number of a matrix. `cond (a)` is defined as `norm (a) * norm (inv (a))`, and is computed via a singular value decomposition.

Compute the determinant of a using LINPACK.

Loadable Function: lambda = eig (a)
Loadable Function: [v, lambda] = eig (a)
The eigenvalues (and eigenvectors) of a matrix are computed in a several step process which begins with a Hessenberg decomposition (see `hess`), followed by a Schur decomposition (see `schur`), from which the eigenvalues are apparent. The eigenvectors, when desired, are computed by further manipulations of the Schur decomposition.

See also: `hess`, `schur`.

Loadable Function: G = givens (x, y)
Loadable Function: [c, s] = givens (x, y)
`G = givens(x, y)` returns a orthogonal matrix `G = [c s; -s' c]` such that `G [x; y] = [*; 0]` (x, y scalars)

Compute the inverse of the square matrix a.

Function File: norm (a, p)
Compute the p-norm of the matrix a. If the second argument is missing, `p = 2` is assumed.

If a is a matrix:

p = `1`
1-norm, the largest column sum of a.
p = `2`
Largest singular value of a.
p = `Inf`
Infinity norm, the largest row sum of a.
p = `"fro"`
Frobenius norm of a, `sqrt (sum (diag (a' * a)))`.

If a is a vector or a scalar:

p = `Inf`
`max (abs (a))`.
p = `-Inf`
`min (abs (a))`.
other
p-norm of a, `(sum (abs (a) .^ p)) ^ (1/p)`.

Function File: null (a, tol)
Returns an orthonormal basis of the null space of a.

The dimension of the null space is taken as the number of singular values of a not greater than tol. If the argument tol is missing, it is computed as

```max (size (a)) * max (svd (a)) * eps
```

Function File: orth (a, tol)
Returns an orthonormal basis of the range of a.

The dimension of the range space is taken as the number of singular values of a greater than tol. If the argument tol is missing, it is computed as

```max (size (a)) * max (svd (a)) * eps
```

Function File: pinv (X, tol)
Returns the pseudoinverse of X. Singular values less than tol are ignored.

If the second argument is omitted, it is assumed that

```tol = max (size (X)) * sigma_max (X) * eps,
```

where `sigma_max (X)` is the maximal singular value of X.

Function File: rank (a, tol)
Compute the rank of a, using the singular value decomposition. The rank is taken to be the number of singular values of a that are greater than the specified tolerance tol. If the second argument is omitted, it is taken to be

```tol = max (size (a)) * sigma (1) * eps;
```

where `eps` is machine precision and `sigma` is the largest singular value of `a`.

Function File: trace (a)
Compute the trace of a, `sum (diag (a))`.

## Matrix Factorizations

Compute the Cholesky factor, r, of the symmetric positive definite matrix a, where

Loadable Function: h = hess (a)
Loadable Function: [p, h] = hess (a)
Compute the Hessenberg decomposition of the matrix a.

The Hessenberg decomposition is usually used as the first step in an eigenvalue computation, but has other applications as well (see Golub, Nash, and Van Loan, IEEE Transactions on Automatic Control, 1979. The Hessenberg decomposition is `p * h * p' = a` where `p` is a square unitary matrix (`p' * p = I`, using complex-conjugate transposition) and `h` is upper Hessenberg (`i >= j+1 => h (i, j) = 0`).

Loadable Function: [l, u, p] = lu (a)
Compute the LU decomposition of a, using subroutines from LAPACK. The result is returned in a permuted form, according to the optional return value p. For example, given the matrix `a = [1, 2; 3, 4]`,

```[l, u, p] = lu (a)
```

returns

```l =

1.00000  0.00000
0.33333  1.00000

u =

3.00000  4.00000
0.00000  0.66667

p =

0  1
1  0
```

Loadable Function: [q, r] = qr (a)
Compute the QR factorization of a, using standard LAPACK subroutines. For example, given the matrix `a = [1, 2; 3, 4]`,

```[q, r] = qr (a)
```

returns

```q =

-0.31623  -0.94868
-0.94868   0.31623

r =

-3.16228  -4.42719
0.00000  -0.63246
```

The `qr` factorization has applications in the solution of least squares problems for overdetermined systems of equations (i.e., is a tall, thin matrix). The `qr` factorization is `q * r = a` where `q` is an orthogonal matrix and `r` is upper triangular.

The permuted `qr` factorization `[q, r, pi] = qr (a)` forms the `qr` factorization such that the diagonal entries of `r` are decreasing in magnitude order. For example, given the matrix `a = [1, 2; 3, 4]`,

```[q, r, pi] = qr(a)
```

returns

```q =

-0.44721  -0.89443
-0.89443   0.44721

r =

-4.47214  -3.13050
0.00000   0.44721

p =

0  1
1  0
```

The permuted `qr` factorization `[q, r, pi] = qr (a)` factorization allows the construction of an orthogonal basis of `span (a)`.

Loadable Function: [u, s] = schur (a, opt)
The Schur decomposition is used to compute eigenvalues of a square matrix, and has applications in the solution of algebraic Riccati equations in control (see `are` and `dare`). `schur` always returns where is a unitary matrix and is upper triangular. The eigenvalues of are the diagonal elements of If the matrix is real, then the real Schur decomposition is computed, in which the matrix is orthogonal and is block upper triangular with blocks of size at most blocks along the diagonal. The diagonal elements of (or the eigenvalues of the blocks, when appropriate) are the eigenvalues of and

The eigenvalues are optionally ordered along the diagonal according to the value of `opt`. `opt = "a"` indicates that all eigenvalues with negative real parts should be moved to the leading block of (used in `are`), `opt = "d"` indicates that all eigenvalues with magnitude less than one should be moved to the leading block of (used in `dare`), and `opt = "u"`, the default, indicates that no ordering of eigenvalues should occur. The leading columns of always span the subspace corresponding to the leading eigenvalues of

Loadable Function: s = svd (a)
Loadable Function: [u, s, v] = svd (a)
Compute the singular value decomposition of a

The function `svd` normally returns the vector of singular values. If asked for three return values, it computes For example,

```svd (hilb (3))
```

returns

```ans =

1.4083189
0.1223271
0.0026873
```

and

```[u, s, v] = svd (hilb (3))
```

returns

```u =

-0.82704   0.54745   0.12766
-0.45986  -0.52829  -0.71375
-0.32330  -0.64901   0.68867

s =

1.40832  0.00000  0.00000
0.00000  0.12233  0.00000
0.00000  0.00000  0.00269

v =

-0.82704   0.54745   0.12766
-0.45986  -0.52829  -0.71375
-0.32330  -0.64901   0.68867
```

If given a second argument, `svd` returns an economy-sized decomposition, eliminating the unnecessary rows or columns of u or v.

## Functions of a Matrix

Returns the exponential of a matrix, defined as the infinite Taylor series The Taylor series is not the way to compute the matrix exponential; see Moler and Van Loan, Nineteen Dubious Ways to Compute the Exponential of a Matrix, SIAM Review, 1978. This routine uses Ward's diagonal approximation method with three step preconditioning (SIAM Journal on Numerical Analysis, 1977). Diagonal approximations are rational polynomials of matrices whose Taylor series matches the first terms of the Taylor series above; direct evaluation of the Taylor series (with the same preconditioning steps) may be desirable in lieu of the approximation when is ill-conditioned.

Compute the matrix logarithm of the square matrix a. Note that this is currently implemented in terms of an eigenvalue expansion and needs to be improved to be more robust.

Compute the matrix square root of the square matrix a. Note that this is currently implemented in terms of an eigenvalue expansion and needs to be improved to be more robust.

Function File: kron (a, b)
Form the kronecker product of two matrices, defined block by block as

```x = [a(i, j) b]
```

Function File: qzhess (a, b)
Compute the Hessenberg-triangular decomposition of the matrix pencil `(a, b)`. This function returns `aa = q * a * z`, `bb = q * b * z`, `q`, `z` orthogonal. For example,

```[aa, bb, q, z] = qzhess (a, b)
```

The Hessenberg-triangular decomposition is the first step in Moler and Stewart's QZ decomposition algorithm. (The QZ decomposition will be included in a later release of Octave.)

Algorithm taken from Golub and Van Loan, Matrix Computations, 2nd edition.