Changes

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 Anaconda.org.

0.3.1

• #349 fixes multiple small bugs and adds test cases for all of them (@mpetrosian, @janosg and @timmens)

0.3.0

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).

0.2.5

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

0.2.4

• #304 Removes the chaospy dependency (@segsell).

0.2.3

• #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)

0.2.2

• #276 Add parallel Nelder-Mead algorithm by @jacekb95

• #267 Update fides by @roecla

• #265 Refactor pounders algorithm by @segsell and @janosg.

• #261 Add pure Python pounders algorithm by @segsell.

0.2.1

• #260 Update MSM and ML notebooks by @timmens.

• #259 Several small fixes and improvements by @janosg and @roecla.

0.2.0

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

• #251 Allows the loading, running and visualization of benchmarks (@janosg, @mpetrosian and @roecla)

• #196 Adds support for multistart optimizations (@asouther4 and @janosg)

• #248 Adds the fides optimizer (@roecla)

• #146 Adds estimate_ml functionality (@janosg, @LuisCald and @s6soverd).

• #235 Improves the documentation (@roecla)

• #216 Adds the ipopt optimizer (@roecla)

• #215 Adds optimizers from the pygmo library (@roecla and @janosg)

• #212 Adds optimizers from the nlopt library (@mpetrosian)

• #228 Restructures testing and makes changes to log_options.

• #149 Adds estimate_msm functionality (@janosg and @loikein)

• #219 Several enhancements by (@tobiasraabe)

• #218 Improve documentation by (@sofyaakimova) and (@effieHan)

• #214 Fix bug with overlapping “fixed” and “linear” constraints (@janosg)

• #211 Improve error handling of log reading functions by (@janosg)

• #210 Automatically drop empty constraints by (@janosg)

• #192 Add option to scale optimization problems by (@janosg)

• #202 Refactoring of bootstrap code (@janosg)

• #148 Add bootstrap functionality (@RobinMusolff)

• #208 Several small improvements (@janosg)

• #206 Improve latex and html tables (@mpetrosian)

• #205 Add scipy’s least squares optimizers (based on #197 by (@yradeva93)

• #198 More unit tests for optimizers (@mchandra12)

• #200 Plot intermediate outputs of first_derivative (@timmens)

0.1.3 - 2021-06-25

• #195 Illustrate optimizers in documentation (@sofyaakimova), (@effieHan) and (@janosg)

• #201 More stable covariance matrix calculation (@janosg)

• #199 Return intermediate outputs of first_derivative (@timmens)

0.1.2 - 2021-02-07

• #189 Improve documentation and logging (@roecla)

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.

• #187 Implement dot notation in algo_options (@roecla)

• #183 Improve documentation (@SofiaBadini)

• #182 Allow for constraints in likelihood inference (@janosg)

• #181 Add DF-OLS optimizer from Numerical Algorithm Group (@roecla)

• #180 Add pybobyqa optimizer from Numerical Algorithm Group (@roecla)

• #179 Allow base_steps and min_steps to be scalars (@tobiasraabe)

• #178 Refactoring of dashboard code (@roecla)

• #177 Add stride as a new dashboard argument (@roecla)

• #176 Minor fix of plot width in dashboard (@janosg)

• #174 Various dashboard improvements (@roecla)

• #173 Add new color palettes and use them in dashboard (@janosg)

• #172 Add high level log reading functions (@janosg)

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

• #130 Improve wrapping of POUNDERS algorithm (@mo2561057)

• #159 Add Richardson Extrapolation to first_derivative (@timmens)

0.0.30 - 2020-04-22

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

0.0.29 - 2020-04-16

• #154 Version restrictions for pygmo (@janosg)

• #153 adds documentation for the CLI (@tobiasraabe)

• #152 makes estimagic work with pandas 1.0 (@SofiaBadini)

0.0.28 - 2020-03-17

• #151 estimagic becomes a noarch package. (@janosg).

• #150 adds command line interface to the dashboard (@tobiasraabe)