This page is used to detail any known issues or unexpected behaviour within the software.
When comparing minimizer options from one software package (e.g., comparing all scipy_ls minimizers), we are not aware of any issues. However, the general problem of comparing minimizers from multiple software packages, and with different problem-formats, on truly equal terms is harder to achieve.
The following list details all cases where we are aware of a possible bias:
Using native FitBenchmarking problems with the Mantid software and fitting using Mantid.
With Mantid data, the function evaluation is slightly faster for Mantid minimizers than for all other minimizers. You should account for this when interpreting the results obtained in this case.
Using non-scalar ties in native FitBenchmarking problems with the Mantid software.
Mantid allows parameters to be tied to expressions - e.g. X0=5.0 or X0=X1*2. While scalar ties are now supported for all minimizers the more complicated expressions are not supported. If you need this feature please get in touch with the development team with your use case.
Running Mantid problems with Matlab fitting software.
To run problems with Matlab fitting software through FitBenchmarking, within the Matlab Controller the dynamically created cost_func.eval_model function is serialized and then loaded in the Matlab Engine workspace. However for Mantid problems, this function is not picklable resulting in the problem being skipped over.
In all cases, the stopping criterion of each minimizer is set to the default value. An experienced user can change this.
Specific Problem/Minimizer Combinations
CrystalField Example with Mantid - DampedGaussNewton Minimizer.
With this combination, GSL is known to crash during Mantid’s fitting. This causes python to exit without completing any remaining runs or generating output files. More information may be available via the issue on Mantid’s github page.