v0.5.1

Summary

This is a minor release that introduces the new algorithm selection tool and several
small improvements.

To learn more about the algorithm selection feature check out the following resources:

• How to specify and configure algorithms
• How to select local optimizers

Pull Requests

• #549 Add support for Python 3.13 (@timmens)
• #550 and #534 implement the new algorithm selection tool (@janosg)
• #548 and #531 improve the documentation (@ChristianZimpelmann)
• #544 Adjusts the results processing of the nag optimizers to be compatible
  with the latest releases (@timmens)
• #543 Adds support for numpy 2.x (@timmens)
• #536 Adds a how-to guide for choosing local optimizers (@mpetrosian)
• #535 Allows algorithm classes and instances in estimation functions
  (@timmens)
• #532 Makes several small improvements to the documentation (@janosg)

v0.5.0

Summary

This is a major release with several breaking changes and deprecations. In this
release we started implementing two major enhancement proposals and renamed the package
from estimagic to optimagic (while keeping the estimagic namespace for the estimation
capabilities).

• EP-02: Static typing
• EP-03: Alignment with SciPy

The implementation of the two enhancement proposals is not complete and will likely
take until version 0.6.0. However, all breaking changes and deprecations (with the
exception of a minor change in benchmarking) are already implemented such that updating
to version 0.5.0 is future proof.

Pull Requests

• #500 removes the dashboard, the support for simopt optimizers and the
  derivative_plot (@janosg)
• #502 renames estimagic to optimagic (@janosg)
• #504 aligns maximize and minimize more closely with scipy. All related
  deprecations and breaking changes are listed below. As a result, scipy code that uses
  minimize with the arguments x0, fun, jac and method will run without changes
  in optimagic. Similarly, to OptimizeResult gets some aliases so it behaves more
  like SciPy's.
• #506 introduces the new Bounds object and deprecates lower_bounds,
  upper_bounds, soft_lower_bounds and soft_upper_bounds (@janosg)
• #507 updates the infrastructure so we can make parallel releases under the names
  optimagic and estimagic (@timmens)
• #508 introduces the new ScalingOptions object and deprecates the
  scaling_options argument of maximize and minimize (@timmens)
• #512 implements the new interface for objective functions and derivatives
  (@janosg)
• #513 implements the new optimagic.MultistartOptions object and deprecates the
  multistart_options argument of maximize and minimize (@timmens)
• #514 and #516 introduce the NumdiffResult object that is returned from
  first_derivative and second_derivative. It also fixes several bugs in the
  pytree handling in first_derivative and second_derivative and deprecates
  Richardson Extrapolation and the key (@timmens)
• #517 introduces the new NumdiffOptions object for configuring numerical
  differentiation during optimization or estimation (@timmens)
• #519 rewrites the logging code and introduces new LogOptions objects
  (@schroedk)
• #521 introduces the new internal algorithm interface.
  (@janosg and @mpetrosian)
• #522 introduces the new Constraint objects and deprecates passing
  dictionaries or lists of dictionaries as constraints (@timmens)

Breaking changes

• When providing a path for the argument logging of the functions
  maximize and minimize and the file already exists, the default
  behavior is to raise an error now. Replacement or extension
  of an existing file must be explicitly configured.
• The argument if_table_exists in log_options has no effect anymore and a
  corresponding warning is raised.
• OptimizeResult.history is now a optimagic.History object instead of a
  dictionary. Dictionary style access is implemented but deprecated. Other dictionary
  methods might not work.
• The result of first_derivative and second_derivative is now a
  optimagic.NumdiffResult object instead of a dictionary. Dictionary style access is
  implemented but other dictionary methods might not work.
• The dashboard is removed
• The derivative_plot is removed.
• Optimizers from Simopt are removed.
• Passing callables with the old internal algorithm interface as algorithm to
  minimize and maximize is not supported anymore. Use the new
  Algorithm objects instead. For examples see: https://tinyurl.com/24a5cner

Deprecations

• The criterion argument of maximize and minimize is renamed to fun (as in
  SciPy).
• The derivative argument of maximize and minimize is renamed to jac (as
  in SciPy)
• The criterion_and_derivative argument of maximize and minimize is renamed
  to fun_and_jac to align it with the other names.
• The criterion_kwargs argument of maximize and minimize is renamed to
  fun_kwargs to align it with the other names.
• The derivative_kwargs argument of maximize and minimize is renamed to
  jac_kwargs to align it with the other names.
• The criterion_and_derivative_kwargs argument of maximize and minimize is
  renamed to fun_and_jac_kwargs to align it with the other names.
• Algorithm specific convergence and stopping criteria are renamed to align them more
  with NlOpt and SciPy names.
  • convergence_relative_criterion_tolerance -> convergence_ftol_rel
  • convergence_absolute_criterion_tolerance -> convergence_ftol_abs
  • convergence_relative_params_tolerance -> convergence_xtol_rel
  • convergence_absolute_params_tolerance -> convergence_xtol_abs
  • convergence_relative_gradient_tolerance -> convergence_gtol_rel
  • convergence_absolute_gradient_tolerance -> convergence_gtol_abs
  • convergence_scaled_gradient_tolerance -> convergence_gtol_scaled
  • stopping_max_criterion_evaluations -> stopping_maxfun
  • stopping_max_iterations -> stopping_maxiter
• The arguments lower_bounds, upper_bounds, soft_lower_bounds and
  soft_upper_bounds are deprecated and replaced by optimagic.Bounds. This affects
  maximize, minimize, estimate_ml, estimate_msm, slice_plot and several
  other functions.
• The log_options argument of minimize and maximize is deprecated. Instead,
  LogOptions objects can be passed under the logging argument.
• The class OptimizeLogReader is deprecated and redirects to
  SQLiteLogReader.
• The scaling_options argument of maximize and minimize is deprecated. Instead a
  ScalingOptions object can be passed under the scaling argument that was previously
  just a bool.
• Objective functions that return a dictionary with the special keys "value",
  "contributions" and "root_contributions" are deprecated. Instead, likelihood and
  least-squares functions are marked with a mark.likelihood or mark.least_squares
  decorator. There is a detailed how-to guide that shows the new behavior. This affects
  maximize, minimize, slice_plot and other functions that work with objective
  functions.
• The multistart_options argument of minimize and maximize is deprecated. Instead,
  a MultistartOptions object can be passed under the multistart argument.
• Richardson Extrapolation is deprecated in first_derivative and second_derivative
• The key argument is deprecated in first_derivative and second_derivative
• Passing dictionaries or lists of dictionaries as constraints to maximize or
  minimize is deprecated. Use the new Constraint objects instead.

v0.5.0rc2

Second release candidate for optimagic 0.5.0

This removes nlopt as mandatory pip dependency because the pip installation of nlopt is unstable.

v0.5.0rc1

First release candidate for version 0.5.0

Summary

This is a major release with several breaking changes and deprecations. In this
release we started implementing two major enhancement proposals and renamed the package
from estimagic to optimagic (while keeping the estimagic namespace for the estimation
capabilities).

• EP-02: Static typing
• EP-03: Alignment with SciPy

The implementation of the two enhancement proposals is not complete and will likely
take until version 0.6.0. However, all breaking changes and deprecations (with the
exception of a minor change in benchmarking) are already implemented such that updating
to version 0.5.0 is future proof.

Pull Requests

• #500 removes the dashboard, the support for simopt optimizers and the
  derivative_plot (@janosg)
• #502 renames estimagic to optimagic (@janosg)
• #504 aligns maximize and minimize more closely with scipy. All related
  deprecations and breaking changes are listed below. As a result, scipy code that uses
  minimize with the arguments x0, fun, jac and method will run without changes
  in optimagic. Similarly, to OptimizeResult gets some aliases so it behaves more
  like SciPy's.
• #506 introduces the new Bounds object and deprecates lower_bounds,
  upper_bounds, soft_lower_bounds and soft_upper_bounds (@janosg)
• #507 updates the infrastructure so we can make parallel releases under the names
  optimagic and estimagic (@timmens)
• #508 introduces the new ScalingOptions object and deprecates the
  scaling_options argument of maximize and minimize (@timmens)
• #512 implements the new interface for objective functions and derivatives
  (@janosg)
• #513 implements the new optimagic.MultistartOptions object and deprecates the
  multistart_options argument of maximize and minimize (@timmens)
• #514 and #516 introduce the NumdiffResult object that is returned from
  first_derivative and second_derivative. It also fixes several bugs in the
  pytree handling in first_derivative and second_derivative and deprecates
  Richardson Extrapolation and the key (@timmens)
• #517 introduces the new NumdiffOptions object for configuring numerical
  differentiation during optimization or estimation (@timmens)
• #519 rewrites the logging code and introduces new LogOptions objects
  ({ghuser}schroedk)
• #521 introduces the new internal algorithm interface.
  (@janosg and @mpetrosian)
• #522 introduces the new Constraint objects and deprecates passing
  dictionaries or lists of dictionaries as constraints (@timmens)

Breaking changes

• When providing a path for the argument logging of the functions
  maximize and minimize and the file already exists, the default
  behavior is to raise an error now. Replacement or extension
  of an existing file must be explicitly configured.
• The argument if_table_exists in log_options has no effect anymore and a
  corresponding warning is raised.
• OptimizeResult.history is now a optimagic.History object instead of a
  dictionary. Dictionary style access is implemented but deprecated. Other dictionary
  methods might not work.
• The result of first_derivative and second_derivative is now a
  optimagic.NumdiffResult object instead of a dictionary. Dictionary style access is
  implemented but other dictionary methods might not work.
• The dashboard is removed
• The derivative_plot is removed.
• Optimizers from Simopt are removed.
• Passing callables with the old internal algorithm interface as algorithm to
  minimize and maximize is not supported anymore. Use the new
  Algorithm objects instead. For examples see: https://tinyurl.com/24a5cner

Deprecations

• The criterion argument of maximize and minimize is renamed to fun (as in
  SciPy).
• The derivative argument of maximize and minimize is renamed to jac (as
  in SciPy)
• The criterion_and_derivative argument of maximize and minimize is renamed
  to fun_and_jac to align it with the other names.
• The criterion_kwargs argument of maximize and minimize is renamed to
  fun_kwargs to align it with the other names.
• The derivative_kwargs argument of maximize and minimize is renamed to
  jac_kwargs to align it with the other names.
• The criterion_and_derivative_kwargs argument of maximize and minimize is
  renamed to fun_and_jac_kwargs to align it with the other names.
• Algorithm specific convergence and stopping criteria are renamed to align them more
  with NlOpt and SciPy names.
  • convergence_relative_criterion_tolerance -> convergence_ftol_rel
  • convergence_absolute_criterion_tolerance -> convergence_ftol_abs
  • convergence_relative_params_tolerance -> convergence_xtol_rel
  • convergence_absolute_params_tolerance -> convergence_xtol_abs
  • convergence_relative_gradient_tolerance -> convergence_gtol_rel
  • convergence_absolute_gradient_tolerance -> convergence_gtol_abs
  • convergence_scaled_gradient_tolerance -> convergence_gtol_scaled
  • stopping_max_criterion_evaluations -> stopping_maxfun
  • stopping_max_iterations -> stopping_maxiter
• The arguments lower_bounds, upper_bounds, soft_lower_bounds and
  soft_upper_bounds are deprecated and replaced by optimagic.Bounds. This affects
  maximize, minimize, estimate_ml, estimate_msm, slice_plot and several
  other functions.
• The log_options argument of minimize and maximize is deprecated. Instead,
  LogOptions objects can be passed under the logging argument.
• The class OptimizeLogReader is deprecated and redirects to
  SQLiteLogReader.
• The scaling_options argument of maximize and minimize is deprecated. Instead a
  ScalingOptions object can be passed under the scaling argument that was previously
  just a bool.
• Objective functions that return a dictionary with the special keys "value",
  "contributions" and "root_contributions" are deprecated. Instead, likelihood and
  least-squares functions are marked with a mark.likelihood or mark.least_squares
  decorator. There is a detailed how-to guide that shows the new behavior. This affects
  maximize, minimize, slice_plot and other functions that work with objective
  functions.
• The multistart_options argument of minimize and maximize is deprecated. Instead,
  a MultistartOptions object can be passed under the multistart argument.
• Richardson Extrapolation is deprecated in first_derivative and second_derivative
• The key argument is deprecated in first_derivative and second_derivative
• Passing dictionaries or lists of dictionaries as constraints to maximize or
  minimize is deprecated. Use the new Constraint objects instead.

v0.4.7

v0.4.7

This release contains minor improvements and bug fixes. It is the last release before
the package will be renamed to optimagic and two large enhancement proposals will be
implemented.

• #490 adds the attribute optimize_result to the MomentsResult class (@timmens)
• #483 fixes a bug in the handling of keyword arguments in bootstrap (@alanlujan91)
• #477 allows to use an identity weighting matrix in MSM estimation (@sidd3888)
• #473 fixes a bug where bootstrap keyword arguments were ignored get_moments_cov (@timmens)
• #467, #478, #479 and #480 improve the documentation (@mpetrosian, @segsell, and @timmens)

v0.4.6

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.

• #464 Makes tranquilo and numba optional dependencies (@janosg)
• #461 Updates docstrings for procss_benchmark_results (@segsell)
• #460 Fixes several bugs in the processing of benchmark results with noisy
  functions (@janosg)
• #459 Prepares benchmarking functionality for parallel optimizers
  (@mpetrosian and @janosg)
• #457 Removes some unused files (@segsell)
• #455 Improves a local pre-commit hook (@ChristianZimpelmann)

v0.4.5

• #379 Improves the estimation table (@ChristianZimpelmann)
• #445 fixes line endings in local pre-commit hook (@ChristianZimpelmann)
• #443, #444, #445, #446, #448 and #449 are a major
  refactoring of tranquilo (@timmens and @janosg)
• #441 Adds an aggregated convergence plot for benchmarks (@mpetrosian)
• #435 Completes the cartis-roberts benchmark set (@segsell)

v0.4.4

• #437 removes fuzzywuzzy as dependency (@aidatak97)
• #432 makes logging compatible with sqlalchemy 2.x (@janosg)
• #430 refactors the getter functions in Tranquilo (@janosg)
• #427 improves pre-commit setup (@timmens and @hmgaudecker)
• #425 improves handling of notebooks in documentation (@baharcos)
• #423 and #399 add code to calculate poisdeness constants (@segsell)
• #420 improve CI infrastructure (@hmgaudecker, @janosg)
• #407 adds global optimizers from scipy (@baharcos)

v0.4.3

This is a minor release that mainly improves the installation

• #416 pins a minimum scipy version and adds numba as pip dependency. It also adds bounds support for scipy neldermead (@janosg)

v0.4.2

This realease contains a bugfix and several improvements

If you have used multistart optimizations with a least squares optimizer you should update as quickly as possible.

#412 Improves the output of the fides optimizer among other small changes (@janosg)
#411 Fixes a bug in multistart optimizations with least squares optimizers. See #410 for details (@janosg)
#404 speeds up the gqtpar subsolver (@mpetrosian )
#400 refactors subsolvers (@mpetrosian)
#398, #397, #395, #390, #389, #388 continue with the implementation of tranquilo (@segsell, @timmens, @mpetrosian, @janosg)
#391 speeds up the bntr subsolver