Chain growth Rosenbluth

One chain can then be accepted based on its Rosenbluth weight. This strategy of multiple chain growth has been tried on a scalar machine, and has been shown to improve sampling efficiency within configuration bias MC [37]. It should also translate to parallel machines because a reasonable amount of computational time is required to generate a chain (or multiple chains) on each processor, relative to the inter-processor communication required at the end of chain regrowth. 

Lattice polymers Monte Carlo samplingvs. Rosenbluth chain growth... 

The performance can be improved with the Rosenbluth chain growth method [33], where first the free nearest neighbors of the ( — l)th monomer are determined and then the new monomer is placed to one of the unoccupied sites. Since the probability for the next monomer to be set varies with the number of free neighbors, this impUes a bias given by... 

If a segment has a zero Rosenbluth weight then growth of the chain is terminated. However, such chains must stiU be included in the averaging used to determine the excess chemical potential. 

An important disadvantage of CBMC is that in the growth of the chain, only one step ahead is examined, which means that one cannot avoid that the growth of the chain may lead into a dead-end street . To compensate for this high attrition rate in the chain construction, one is forced to use a large number of trial directions. For hard-core interactions, this leads to a broad distribution of the Rosenbluth weight and therefore low acceptcmce rates [111]. 

---