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On the recordRead 11 Oct 202628 facts
FitBenchmarking
Plans and prices
FitBenchmarking
Free
Open source tool · installable with pip · optional fitting packages may require separate installation
Read 11 Oct 2026 · github.com
On the record
- Audience
- The project identifies scientists, scientific software developers, and mathematicians or numerical software developers as communities interested in fitting software.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Benchmark data
- Shipped problem data includes NIST nonlinear regression, CUTEst, Mantid, and SASView examples.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Comparison caveat
- The documentation cautions that comparing minimizers across different software packages and problem formats may not be on truly equal terms.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Comparisons
- It compares fitting methods by runtime and accuracy on benchmark problems.github.com · read 11 Oct 2026
- Custom datasets
- Users can add custom datasets in supported file formats and run them with available fitting methods.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Custom problems
- Users can analyze problems in supported file formats by passing their paths, and can add custom datasets to the benchmark.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Data import formats
- CUTEst, Native, Mantid, NIST, Horace, HOGBEN, BALfitbenchmarking.readthedocs.io · read 23 Sep 2026
- Dependency limit
- The Gradient-Free-Optimizers integration requires finite bounds on all parameters.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Deployment
- desktopfitbenchmarking.readthedocs.io · read 23 Sep 2026
- External dependencies
- Some benchmark datasets require external packages such as Mantid, CUTEst, pycutest, or sasmodels.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Fitting integrations
- Supported fitting software includes SciPy, Bumps, DFO-LS, GALAHAD, GOFit, Mantid, MATLAB, Minuit, and NLopt.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Installation
- The package can be installed with pip, and the documentation also describes installing from source.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Integrations
- The documented benchmark data includes problems from NIST, CUTEst, Mantid, and SASView examples and tests.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Interactive results
- By default, running FitBenchmarking starts a Dash app to provide interactive plots.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Large problems
- Bundle Adjustment in the Large problems can currently only be run with scipy_ls, which supports sparse Jacobians.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Maker collaboration
- The package is described as a collaboration between STFC’s Scientific Computing Department, ISIS Neutron and Muon Facility, and Diamond Light Source.github.com · read 11 Oct 2026
- Optional dependencies
- Some fitting packages are optional dependencies, and external software that cannot be installed using pip must be installed separately.fitbenchmarking.readthedocs.io · read 11 Oct 2026
- Problem inputs
- Each test problem includes data points, a fitting function definition, and at least one set of initial parameter values.fitbenchmarking.readthedocs.io · read 11 Oct 2026
