Sparse symmetric random matrix

`R = sprandsym(S)`

R = sprandsym(n,density)

R = sprandsym(n,density,rc)

R = sprandsym(n,density,rc,kind)

R = sprandsym(S,[],rc,3)

`R = sprandsym(S)`

returns
a symmetric random matrix whose lower triangle and diagonal have the
same structure as `S`

. Its elements are normally
distributed, with mean `0`

and variance `1`

.

`R = sprandsym(n,density)`

returns
a symmetric random, `n`

-by-`n`

,
sparse matrix with approximately `density*n*n`

nonzeros;
each entry is the sum of one or more normally distributed random samples,
and (`0 <= `

).`density`

<= 1

`R = sprandsym(n,density,rc)`

returns
a matrix with a reciprocal condition number equal to `rc`

.
The distribution of entries is nonuniform; it is roughly symmetric
about 0; all are in [−1,1].

If `rc`

is a vector of length `n`

,
then `R`

has eigenvalues `rc`

. Thus,
if `rc`

is a positive (nonnegative) vector then `R`

is
a positive (nonnegative) definite matrix. In either case, `R`

is
generated by random Jacobi rotations applied to a diagonal matrix
with the given eigenvalues or condition number. It has a great deal
of topological and algebraic structure.

`R = sprandsym(n,density,rc,kind)`

is positive definite.

If kind = 1,

`R`

is generated by random Jacobi rotation of a positive definite diagonal matrix.`R`

has the desired condition number exactly.If kind = 2,

`R`

is a shifted sum of outer products.`R`

has the desired condition number only approximately, but has less structure.

`R = sprandsym(S,[],rc,3)`

has the same structure
as the matrix `S`

and approximate condition number `1/rc`

.

Was this topic helpful?