### 20.1 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 and 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 = cc*a*dd` and `bb = 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).

— Function File: 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.

> See also: norm, svd, rank.

— Loadable Function: [d, rcond] = det (a)

> Compute the determinant of a using Lapack. Return an estimate of the reciprocal condition number if requested.

— Function File: dmult (a, b)

> If a is a vector of length `rows (`b`)`, return `diag (`a`) *` b (but computed much more efficiently).

— Function File: dot (x, y, dim)

> Computes the dot product of two vectors. If x and y are matrices, calculate the dot-product along the first non-singleton dimension. If the optional argument dim is given, calculate the dot-product along this dimension.

— 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, followed by a Schur decomposition, from which the eigenvalues are apparent. The eigenvectors, when desired, are computed by further manipulations of the Schur decomposition.

> The eigenvalues returned by `eig` are not ordered.

— Loadable Function: g = givens (x, y)
— Loadable Function: [c, s] = givens (x, y)

> Return a 2 by 2 orthogonal matrix g`= [`c s`; -`s`'` c`]` such that g`[`x`;` y`] = [*; 0]` with x and y scalars.

> For example,

>               givens (1, 1)
>                    =>   0.70711   0.70711
>                        -0.70711   0.70711

— Loadable Function: [x, rcond] = inv (a)
— Loadable Function: [x, rcond] = inverse (a)

> Compute the inverse of the square matrix a. Return an estimate of the reciprocal condition number if requested, otherwise warn of an ill-conditioned matrix if the reciprocal condition number is small.

— 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 the absolute values of a.
> p = `2`
>   Largest singular value of a.
> p = `Inf`
>   Infinity norm, the largest row sum of the absolute values 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`)`.

> See also: cond, svd.

— Function File: null (a, tol)

> Return 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)

> Return an orthonormal basis of the range space 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

— Loadable Function: pinv (x, tol)

> Return 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(1)` is the largest singular value of a.

— Function File: trace (a)

> Compute the trace of a, `sum (diag (`a`))`.