using iterative minimizer routine to improve getting stuck in a local minimum

[M-X-Lin]

I notice "minimize" can easily get stuck in a local minimum if the theory model is complicated even with the covmat provided by MCMC chains.

One piece of evidence is that if I run "minimize" again with exactly the same setting (rhoend=0.01), but the starting point from the last minimum, it can return a better result (sometimes chi2 improvement can be more than 1).

I found one way to improve it is to use the iterative minimizer routine: repeat "minimize" by setting the starting point as the last minimum until the chi2 difference between the current and last round is less than a certain threshold, e.g. 0.1. This iterative routine can significantly improve the result, sometimes by Dchi2~3 (e.g. in Class Early Dark Energy scalar field model).

[cmbant]

If you run with MPI it should try multiple random points are report the lowest, with a warning if it doesn't look well converged. You can of course also play with the accuracy parameters

[M-X-Lin]

If you run with MPI it should try multiple random points are report the lowest, with a warning if it doesn't look well converged. You can of course also play with the accuracy parameters

Thanks for your reply, Antony. My tests are always with MPI (np=6) in a single minimize round, and it can still finish successfully without any warning, although the result is definitely not good enough (since I found a better one using the iterative routine).