Hi Subrat,
I understand you are working on dipole optimization problem and using fieldtrip to optimize the goodness of the fit. You want to apply simulated annealing for the optimization function and would like to know more about the arguments that it takes.
You can use the “simulannealbnd” function for applying the simulated annealing algorithm on the optimization function. The function signature for “simulannealbnd” is as follows:
x= simulannealbnd(fun,x0,lb,ub,options)
where x0 is an initial point for the simulated annealing algorithm, a real vector, and lb and ub are the lower bounds and upper bounds for the solution respectively. If no bounds exist, one can set lb = [] and/or ub=[]. So for your case, you can specify an anonymous function that includes all the additional parameters (dat, sens, headmodel etc.) and then pass it to “simulannealbnd” along with the bounds:
lb = [];
ub = [];
% Define an anonymous function that includes all the additional parameters
objFun = @(x) dipfit_error(x, dat, sens, headmodel, constr, metric, checkinside, mleweight, reducerank, normalize, normalizeparam, weight, backproject);
% Call simulannealbnd with the objective function and parameters
[param, fval, exitflag, output] = simulannealbnd(objFun, param, lb, ub, options);
You can read more about the syntax for “simulannealbnd” here:
I hope this helps!
