Optimiser
Optimiser
has the form:
optimiser_type parametersThe optimiser type identifier is listed below as the name.
Dumb_optimiser
dumb
.
Data format:
maximum_iterations (0 for no limit) diagnostic_output_level (0 for none)
Steep_optimiser
steepest_descent
.
Data format:
double tol; // tolerance of minimum (spatial) double stepscale; // scaling factor for gradient Vector double stepfac; // factor for increasing step size (greater than 1) double stepmin; // minimum stepsize allowed int maxits; // maximum iterations (0 for no limit) int verbose; // 0/1 for diagnostics off/onDefault values are
tol = 1.0e-7; stepscale = 1.0; stepfac = 1.5; stepmin = 1.0e-7;
CG_optimiser
conjugate_gradient
.
Data as for Steep_optimiser
.
MC_optimiser
MC_optimiser
has the form:
MC_optimiser_type parametersThe MC optimiser type identifier is listed below as the name. Unless stated otherwise, MC optimisers use a default data format:
double pseudotemp; // pseudo-temperature int nsteps; // global steps to take int verbose; // diagnostic options (0=off)with default values
pseudotemp = 0.0; nsteps = 1; verbose = 0;
MC_optimiser_each
monte-carlo_each
.
MC_optimiser_all
monte-carlo_all
.
MC_optimiser_random
monte-carlo_random
.
MC_optimiser_weighted
Name: weighted_monte-carlo
.
Data format: as for MC_optimiser
with in addition
double decay; // decay rate for memory of previous resultsDefault:
decay = 0.1;
.
MC_optimiser_random_subset
monte-carlo_random_subset
.
Data format: as for MC_optimiser
with in addition
int nset; // number in random set