Concepts
Introduction & Motivations
Purpose:
The purpose of this little class is too facilitate the writing and maintenance of Metropolis MonteCarlo algorithms, with several advantages :
Easy to add new moves and measures.
For complex MC algorithms, with 6 or 7 different moves, it helps to clearly separate the code for each move.
Parallelism is automatic.
The random generator is a simple parameter, it can be chosen dynamically.
Principle
The mc_generic class is a generic version of the algorithms, with moves and measures. The user :
writes move classes, modelling the Move concept.
writes measure classes, modelling the Measure concepts.
register them in a mc_generic object MC
call MC.warmup_and_accumulate(…) … and that is (almost) it !
The Move concept
Elements
Comment
mc_sign_type attempt()
First part of the Move.
Returns the probability to accept the move. If :
the move is \(x\rightarrow x'\), proposed with proba \(T_{x\rightarrow x'}\)
the probability of the \(x\) config is denoted \(p_x\)
then attempt should return the Metropolis ratio:
\[\frac{p_{x'} T_{x'\rightarrow x}}{p_x T_{x\rightarrow x'}}\]with the sign. (The sign will be separated from the absolute value and kept by the MonteCarlo class). In other words: attempt() = move_sign * move_rate with abs(move_sign) = 1
mc_sign_type accept()
Called iif the Move is accepted.
Returns a number r such that \(|r| =1\)
There is a possibility to update the sign here as well, so that the final sign is: move_sign * accept()
CAREFUL that we need the ratio new_sign / old_sign here just like we needed a ratio in attempt()
void reject()
Called iif the Move is rejected (for cleaning).
The Measure concept
Elements
Comment
void accumulate(std::complex<double> sign)
Accumulation with the sign
void collect_results ( boost::mpi::communicator const & c)
Collects the results over the communicator, and finalize the calculation (compute average, error).