trustregion

=========================================== trustregion: Trust-region subproblem solver

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This package provides Python routines for solving the trust-region subproblem from nonlinear, nonconvex optimization. For more details on trust-region methods, see the book: A. R. Conn, N. I. M. Gould and Ph. L. Toint (2000), Trust-Region Methods, MPS-SIAM Series on Optimization.

The trust-region subproblem we solve is

.. code-block::

min_{s in R^n} g^T s + 0.5 s^T H s, subject to ||s||_2 <= delta (and sl <= s <= su)

Quick install

.. code-block:: bash

$ sudo apt-get install gfortran
$ pip install --user numpy
$ pip install --user trustregion

For more details, see below. Note that NumPy must be installed first, as it is used to compile the Fortran-linked modules.

Interface

The Python package :code:trustregion provides one routine, :code:solve, with interface:

.. code-block:: python

import trustregion
s               = trustregion.solve(g, H, delta, sl=None, su=None, verbose_output=False)
s, gnew, crvmin = trustregion.solve(g, H, delta, sl=None, su=None, verbose_output=True)

where the inputs are

• :code:g, the gradient of the objective (as a 1D NumPy array)
• :code:H, the symmetric Hessian matrix of the objective (as a 2D square NumPy array) - this can be :code:None if the model is linear
• :code:delta, the trust-region radius (non-negative float)
• :code:sl, the lower bounds on the step (as a 1D NumPy array) - this can be :code:None if not present, but :code:sl and :code:su must either be both :code:None or both set
• :code:su, the upper bounds on the step (as a 1D NumPy array) - this can be :code:None if not present, but :code:sl and :code:su must either be both :code:None or both set
• :code:verbose_output, a flag indicating which outputs to return.

The outputs are:

• :code:s, an approximate minimizer of the subproblem (as a 1D NumPy array)
• :code:gnew, the gradient of the objective at the solution :code:s (i.e. :code:gnew = g + H.dot(s))
• :code:crvmin, a float giving information about the curvature of the problem. If :code:s is on the trust-region boundary (given by :code:delta), then :code:crvmin=0. If :code:s is constrained in all directions by the box constraints, then :code:crvmin=-1. Otherwise, :code:crvmin>0 is the smallest curvature seen in the Hessian.

Example Usage

Examples for the use of :code:trustregion.solve can be found in the examples <https://github.com/lindonroberts/trust-region/tree/master/examples>_ directory on Github.

Algorithms

:code:trustregion implements three different methods for solving the subproblem, based on the problem class (in Fortran 90, wrapped to Python):

• :code:trslin.f90 solves the linear objective case (where :code:H=None or :code:H=0), using Algorithm B.1 from: L. Roberts (2019), Derivative-Free Algorithms for Nonlinear Optimisation Problems <https://ora.ox.ac.uk/objects/uuid:ec76e895-6eee-491a-88ed-b4ed10fa6003>_, PhD Thesis, University of Oxford.
• :code:trsapp.f90 solves the quadratic case without box constraints. It is a minor modification of the routine of the same name in :code:NEWUOA [M. J. D. Powell (2004), The NEWUOA software for unconstrained optimization without derivatives <http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2004_08.pdf>_, technical report DAMTP 2004/NA05, University of Cambridge].
• :code:trsbox.f90 solves the quadratic case with box constraints. It is a minor modification of the routine of the same name in :code:BOBYQA [M. J. D. Powell (2009), The BOBYQA algorithm for bound constrained optimization without derivatives <http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf>_, technical report DAMTP 2009/NA06, University of Cambridge].

In the linear case, an active-set method is used to solve the resulting convex problem. In the quadratic cases, a modification of the Steihaug-Toint/conjugate gradient method is used. For more details, see the relevant references above.

Requirements

:code:trustregion requires the following software to be installed:

• Fortran compiler (e.g. gfortran)
• Python 3.8 or higher (http://www.python.org/)

Additionally, the following python packages should be installed (these will be installed automatically if using pip, see Installation using pip_):

• NumPy (http://www.numpy.org/)

Installation using pip

For easy installation, use pip <http://www.pip-installer.org/>_ as root:

.. code-block:: bash

$ [sudo] pip install numpy
$ [sudo] pip install trustregion

Note that NumPy should be installed before :code:trustregion, as it is used to compile the Fortran modules.

If you do not have root privileges or you want to install :code:trustregion for your private use, you can use:

.. code-block:: bash

$ pip install --user numpy
$ pip install --user trustregion

which will install :code:trustregion in your home directory.

Note that if an older install of :code:trustregion is present on your system you can use:

.. code-block:: bash

$ [sudo] pip install --upgrade trustregion

to upgrade :code:trustregion to the latest version.

Manual installation

Alternatively, you can download the source code from Github <https://github.com/lindonroberts/trust-region>_ and unpack as follows:

.. code-block:: bash

$ git clone https://github.com/lindonroberts/trust-region
$ cd trust-region

To upgrade :code:trustregion to the latest version, navigate to the top-level directory (i.e. the one containing :code:setup.py) and rerun the installation using :code:pip, as above:

.. code-block:: bash

$ git pull
$ [sudo] pip install .  # with admin privileges

Testing

If you installed :code:trustregion manually, you can test your installation by running:

.. code-block:: bash

$ pip install pytest
$ cd trustregion/tests
$ python -m pytest

Alternatively, the documentation provides some simple examples of how to run :code:trustregion.

Uninstallation

If :code:trustregion was installed using pip you can uninstall as follows:

.. code-block:: bash

$ [sudo] pip uninstall trustregion

If :code:trustregion was installed manually you have to remove the installed files by hand (located in your python site-packages directory).

Bugs

Please report any bugs using GitHub's issue tracker.

License

This algorithm is released under the GNU GPL license.