v0.4.1

This is a minor polishing release.

• #307 Adopts a code of condact and governance model
• #384 384 Polish documentation @janosg and @mpetrosian
• #374 Moves the documentation to MyST @baharcos
• #365 Adds copybuttos to documentation @amageh
• #371 Refactors the pounders algorithm @segsell
• #369 Fixes CI @janosg
• #367 Fixes the linux environment @timmens
• #294 Adds the very first experimental version of tranquilo @janosg, @timmens, @segsell, @mpetrosian

v0.4.0

• #366 prepares changes log for next release (@segsell)
• #362 polishes documentation and adds copy button for code-snippets (@segsell)

v0.3.4

• #361 fixes multiple bugs in estimate_msm and estimate_ml that only happened in constrained estimations (@timmens, @janosg, @segsell)
• #347 adds a BootstrapResult object similar to other estimation results (@segsell)

v0.3.3

First release with full jax support (i.e. jax arrays do not have to be hidden from estimagic)

• #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

v0.3.2

Polishing and multiple small bugfixes.

v0.3.1

This is the first version of estimagic with experimental support for nonlinear constraints. Moreover, we fix some bugs and improve test coverage.

• #349 fixes bugs introduced in the transition to pytrees and custom results objects. Details are described in the PR comments.
• #346 Adds experimental support for nonlinear constraints

v0.3.0

Summary

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.

PRs

• #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 (janosg)
• #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).

v0.2.4

Remove chaospy as dependency

v0.2.3

• Pytree support for first and second derivative
• Add fast solvers for quadratic trustregion subproblems
• Improved estimation tables
• Various small bugfixes

v0.2.2

First release with own optimization algorithms.

• parallel Nelder-Mead by Jacek Barszczewski
• parallel Pounders by Sebastian Gsell

The algorithms are still work in progress and will get more convergence criteria, support for bounds, etc. in the future.