RECURSIVE SAMPLING OF RANDOM-WALKS - SELF-AVOIDING WALKS IN DISORDERED MEDIA

We present a new algorithm for simulating random walks which is simple, versatile and efficient. It uses recursive function calls and can be used to obtain unbiased samples with any given length distribution. This makes it particularly useful in disordered geometries where the effective connectivity constant is not known a priori. When applying it to self-avoiding random walks on two-dimensional media with quenched randomness, we find evidence for at least two new renormalization group fixed points.