Allow computing functions of matrices with methods beyond eigenvalues

[pnkraemer]

Currently, all Lanczos-based functions of matrices use an eigenvalue decomposition to evaluate f(H)v.

For some functions, e.g. log and exp, alternatives may be more reliable.

I propose either providing a matfun_dense: Callable option to matrix-function-vector products, or hard-code log, exp, sqrt, and perhaps others in funm.
To decide which ones to hard-code, we could refer to what jax.scipy.linalg offers in terms of dense matrix-functions.

[pnkraemer]

#201 takes care of most of this, but leaves some open todos for applications in trace estimation (which is why this issue remains open for a bit longer).

Edit: the open todo is extending e.g. integrand_funm_sym to expect different dense matrix functions. Currently, this is not the case.