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directsearch-1.0
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مشاهده بیشترتوضیحات
A derivative-free solver for unconstrained minimization
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | directsearch-1.0 |
| نام | directsearch |
| نسخه کتابخانه | 1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lindon Roberts |
| ایمیل نویسنده | lindon.roberts@anu.edu.au |
| آدرس صفحه اصلی | https://github.com/lindonroberts/directsearch |
| آدرس اینترنتی | https://pypi.org/project/directsearch/ |
| مجوز | GNU GPL |
==============================================================
directsearch: derivative-free optimization using direct search
==============================================================
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:alt: GNU GPL v3 License
``directsearch`` is a package for solving unconstrained minimization, without requiring derivatives of the objective. It is particularly useful when evaluations of the objective function are expensive and/or noisy.
It implements a family of direct search methods.
For general references on these methods, please consult:
1. A R Conn, K Scheinberg, and L N Vicente. *Introduction to derivative-free optimization*. SIAM, 2009.
2. C Audet, and W. Hare. Derivative-Free and Blackbox Optimization. Springer, 2017.
3. T G Kolda, R M Lewis, and V Torczon. Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods. *SIAM Review*, 45(3), 2003, 385-482.
This package extends general direct search methods to use randomized methods for improved practical performance and scalability.
Citation
--------
If you use this package, please cite:
L Roberts, and C W Royer. Direct search based on probabilistic descent in reduced spaces, *In preparation*, (2022).
Installation
------------
Please install using pip:
.. code-block:: bash
$ pip install [--user] directsearch
To instead install from source run:
.. code-block:: bash
$ git clone git@github.com:lindonroberts/directsearch.git
$ cd directsearch
$ pip install -e .
The ``-e`` option to pip allows you to modify the source code and for your Python installation to recognize this.
Usage
-----
This package can solve unconstrained nonlinear optimization problems of the form: ``min_{x in R^n} f(x)``.
The simplest usage of ``directsearch`` is
.. code-block:: python
soln = directsearch.solve(f, x0)
where
* ``f`` is a callable objective function, taking in a ``numpy.ndarray`` the same shape as ``x0`` and returing a single ``float``.
* ``x0`` is a one-dimensional ``numpy.ndarray`` (i.e. ``len(x0.shape)==1``), the starting point for the algorithm. It should be the best available guess of the minimizer.
The output is an object with fields:
* ``soln.x``: the approximate minimizer, the best ``x`` value found (a ``numpy.ndarray`` the same shape as ``x0``).
* ``soln.f``: the minimum value equal to ``f(soln.x)``.
* ``soln.nf``: the number of evaluations of ``f`` required by the solve routine.
* ``soln.flag``: an integer indicating the reason for termination.
* ``soln.msg``: a string with a human-readable termination message.
The possible values of ``soln.flag`` are:
* ``soln.EXIT_MAXFUN_REACHED``: termination on maximum number of objective evaluations.
* ``soln.EXIT_ALPHA_MIN_REACHED``: termination on small step size (success).
You can print information about the solution using ``print(soln)``.
The ``examples`` directory has several scripts showing the usage of ``directsearch``.
**Interfaces to solver instances**
There are many configurable options for the solver in `directsearch` and several ways to call specific direct search algorithm implementations.
The full set of available functions is:
* ``directsearch.solve()`` applies a direct-search method to a given optimization problem. It is the most flexible available routine.
* ``directsearch.solve_directsearch()`` applies regular direct-search techniques without sketching [1,2,3].
* ``directsearch.solve_probabilistic_directsearch()`` applies direct search based on probabilistic descent without sketching [4].
* ``directsearch.solve_subspace_directsearch()`` applies direct-search schemes based on polling directions in random subspaces [5].
* ``directsearch.solve_stp()`` applies the stochastic three points method, a particular direct-search technique [6].
**Optional parameters and more information**
See ``usage.txt`` for full details on how to call these functions.
The most commonly used optional inputs (to all functions) are:
* ``maxevals``: the maximum number of allowed evaluations of ``f`` during the solve.
* ``verbose``: a ``bool`` for whether or not to print progress information.
* ``print_freq``: an ``int`` indicating how frequently to print progress information (1 is at every iteration).
**Choosing a solver instance**
As a rule of thumb, if ``len(x0)`` is not too large (e.g. less than 50), then ``solve_directsearch()`` or ``solve_probabilistic_directsearch()`` are suitable choices.
Of these, generally ``solve_probabilistic_directsearch()`` will solve with fewer evaluations of ``f``, but ``solve_directsearch()`` is a deterministic algorithm.
If ``len(x0)`` is larger, then ``directsearch.solve_subspace_directsearch()`` may be a better option.
Note that ``solve_directsearch()`` is the only deterministic algorithm (i.e. reproducible without setting the numpy random seed).
**References**
1. A R Conn, K Scheinberg, and L N Vicente. *Introduction to derivative-free optimization*. SIAM, 2009.
2. C Audet, and W. Hare. Derivative-Free and Blackbox Optimization. Springer, 2017.
3. T G Kolda, R M Lewis, and V Torczon. Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods. *SIAM Review*, 45(3), 2003, 385-482.
4. S Gratton, C W Royer, L N Vicente, and Z Zhang. Direct Search Based on Probabilistic Descent. *SIAM J. Optimization*, 25(3), 2015, 1515-1541.
5. L Roberts, and C W Royer. Direct search based on probabilistic descent in reduced spaces, *In preparation*, (2022).
6. E H Bergou, E Gorbunov, and P Richtarik. Stochastic Three Points Method for Unconstrained Smooth Minimization. *SIAM J. Optimization*, 30(4), 2020, 2726-2749.
Bugs
----
Please report any bugs using GitHub's issue tracker.
License
-------
This algorithm is released under the GNU GPL license.
نحوه نصب
نصب پکیج whl directsearch-1.0:
pip install directsearch-1.0.whl
نصب پکیج tar.gz directsearch-1.0:
pip install directsearch-1.0.tar.gz