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Trust-region subproblem solvers for nonlinear/nonconvex optimization

Project description

Build Status GNU GPL v3 License

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

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


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

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

  • g, the gradient of the objective (as a 1D NumPy array)

  • H, the symmetric Hessian matrix of the objective (as a 2D square NumPy array) - this can be None if the model is linear

  • delta, the trust-region radius (non-negative float)

  • sl, the lower bounds on the step (as a 1D NumPy array) - this can be None if not present, but sl and su must either be both None or both set

  • su, the upper bounds on the step (as a 1D NumPy array) - this can be None if not present, but sl and su must either be both None or both set

  • verbose_output, a flag indicating which outputs to return.

The outputs are:

  • s, an approximate minimizer of the subproblem (as a 1D NumPy array)

  • gnew, the gradient of the objective at the solution s (i.e. gnew = g +

  • crvmin, a float giving information about the curvature of the problem. If s is on the trust-region boundary (given by delta), then crvmin=0. If s is constrained in all directions by the box constraints, then crvmin=-1. Otherwise, crvmin>0 is the smallest curvature seen in the Hessian.

Example Usage

Examples for the use of trustregion.solve can be found in the examples directory on Github.


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

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.


trustregion requires the following software to be installed:

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

Installation using pip

For easy installation, use pip as root:

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

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

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

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

which will install trustregion in your home directory.

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

$ [sudo] pip install --upgrade trustregion

to upgrade trustregion to the latest version.

Manual installation

Alternatively, you can download the source code from Github and unpack as follows:

$ git clone
$ cd trust-region

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

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


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

$ python test

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


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

$ [sudo] pip uninstall trustregion

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


Please report any bugs using GitHub’s issue tracker.


This algorithm is released under the GNU GPL license.

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