This is a record of all past estimagic releases and what went into them in reverse chronological order. We follow semantic versioning and all releases are available on

Following the scientific python guidelines we drop the official support for Python 3.8.


This release drastically improves the optimizer benchmarking capabilities, especially with noisy functions and parallel optimizers. It makes tranquilo and numba optional dependencies and is the first version of estimagic to be compatible with Python 3.11.









  • #357 Adds jax support (@janosg)

  • #359 Improves error handling with violated constaints (@timmens)

  • #358 Improves cartis roberts set of test functions and improves the default latex rendering of MultiIndex tables (@mpetrosian)




Fist release with pytree support in optimization, estimation and differentiation and much better result objects in optimization and estimation.

Breaking changes

  • New OptimizeResult object is returned by maximize and minimize. This breaks all code that expects the old result dictionary. Usage of the new result is explained in the getting started tutorial on optimization.

  • New internal optimizer interface that can break optimization with custom optimizers

  • The inferface of process_constraints changed quite drastically. This breaks code that used process_constraints to get the number of free parameters or check if constraints are valid. There are new high level functions estimagic.check_constraints and estimagic.count_free_params instead.

  • Some functions from estimagic.logging.read_log are removed and replaced by estimagic.OptimizeLogReader.

  • Convenience functions to create namedtuples are removed from estimagic.utilities.

  • #346 Add option to use nonlinear constraints (@timmens)

  • #345 Moves estimation_table to new latex functionality of pandas (@mpetrosian)

  • #344 Adds pytree support to slice_plot (@janosg)

  • #343 Improves the result object of estimation functions and makes msm estimation pytree compatible (@janosg)

  • #342 Improves default options of the fides optimizer, allows single constraints and polishes the documentation (@janosg)

  • #340 Enables history collection for optimizers that evaluate the criterion function in parallel (@janosg)

  • #339 Incorporates user feedback and polishes the documentation.

  • #338 Improves log reading functions (@janosg)

  • #336 Adds pytree support to the dashboard (@roecla).

  • #335 Introduces an OptimizeResult object and functionality for history plotting (@janosg).

  • #333 Uses new history collection feature to speed up benchmarking (@segsell).

  • #330 Is a major rewrite of the estimation code (@timmens).

  • #328 Improves quadratic surrogate solvers used in pounders and tranquilo (@segsell).

  • #326 Improves documentation of numerical derivatives (@timmens).

  • #325 Improves the slice_plot (@mpetrosian)

  • #324 Adds ability to collect optimization histories without logging (@janosg).

  • #311 and #288 rewrite all plotting code in plotly (@timmens and @aidatak97).

  • #306 improves quadratic surrogate solvers used in pounders and tranquilo (@segsell).

  • #305 allows pytrees during optimization and rewrites large parts of the constraints processing (@janosg).

  • #303 introduces a new optimizer interface that makes it easier to add optimizers and makes it possible to access optimizer specific information outside of the intrenal_criterion_and_derivative (@janosg and @roecla).


  • #302 Drastically improves error handling during optimization (@janosg).



  • #295 Fixes a small bug in estimation_table (@mpetrosian).

  • #286 Adds pytree support for first and second derivative (@timmens).

  • #285 Allows to use estimation functions with external optimization (@janosg).

  • #283 Adds fast solvers for quadratic trustregion subproblems (@segsell).

  • #282 Vastly improves estimation tables (@mpetrosian).

  • #281 Adds some tools to work with pytrees (@janosg and @timmens).

  • #278 adds Estimagic Enhancement Proposal 1 for the use of Pytrees in Estimagic (@janosg)




Add a lot of new functionality with a few minor breaking changes. We have more optimizers, better error handling, bootstrap and inference for method of simulated moments. The breaking changes are: - logging is disabled by default during optimization. - the log_option “if_exists” was renamed to “if_table_exists” - The comparison plot function is removed. - first_derivative now returns a dictionary, independent of arguments. - structure of the logging database has changed - there is an additional boolean flag named scaling in minimize and maximize

0.1.3 - 2021-06-25#

0.1.2 - 2021-02-07#

0.1.1 - 2021-01-13#

This release greatly expands the set of available optimization algorithms, has a better and prettier dashboard and improves the documentation.

0.1.0dev1 - 2020-09-08#

This release entails a complete rewrite of the optimization code with many breaking changes. In particular, some optimizers that were available before are not anymore. Those will be re-introduced soon. The breaking changes include:

  • The database is restructured. The new version simplifies the code, makes logging faster and avoids the sql column limit.

  • Users can provide closed form derivative and/or criterion_and_derivative where the latter one can exploit synergies in the calculation of criterion and derivative. This is also compatible with constraints.

  • Our own (parallelized) first_derivative function is used to calculate gradients during the optimization when no closed form gradients are provided.

  • Optimizer options like convergence criteria and optimization results are harmonized across optimizers.

  • Users can choose from several batch evaluators whenever we parallelize (e.g. for parallel optimizations or parallel function evaluations for numerical derivatives) or pass in their own batch evaluator function as long as it has a compatible interface. The batch evaluator interface also standardizes error handling.

  • There is a well defined internal optimizer interface. Users can select the pre-implemented optimizers by algorithm=”name_of_optimizer” or their own optimizer by algorithm=custom_minimize_function

  • Optimizers from pygmo and nlopt are no longer supported (will be re-introduced)

  • Greatly improved error handling.

  • #169 Add additional dashboard arguments

  • #168 Rename lower and upper to lower_bound and upper_bound (@ChristianZimpelmann)

  • #167 Improve dashboard styling (@roecla)

  • #166 Re-add POUNDERS from TAO (@tobiasraabe)

  • #165 Re-add the scipy optimizers with harmonized options (@roecla)

  • #164 Closed form derivatives for parameter transformations (@timmens)

  • #163 Complete rewrite of optimization with breaking changes (@janosg)

  • #162 Improve packaging and relax version constraints (@tobiasraabe)

  • #160 Generate parameter tables in tex and html (@mpetrosian)

0.0.31 - 2020-06-20#

0.0.30 - 2020-04-22#

  • #158 allows to specify a gradient in maximize and minimize (@janosg)

0.0.29 - 2020-04-16#

0.0.28 - 2020-03-17#