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README.md

	SNLS -- small non-linear solver library

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BACKGROUND

SNLS (small non-linear solver) is a C++ library for solving non-linear systems of equations. The emphasis is on small systems of equations for which direct factorization of the Jacobian is appropriate. The systems of equations may be numerically ill-conditioned. Implementation is amenable to GPU usage, with the idea that class instantiation would occur within device functions.

Examples of work that has made use of SNLS or substantially equivalent algorithms:

Solvers currently in the library:

  • SNLSTrDlDenseG Dogleg approximation to the trust-region sub-problem for multi-dimensional nonlinear systems of equations. This method reduces to a Newton-Raphson approach near the solution. These methods are sometimes described as "restricted step" approaches instead of trust-region approaches. See, for example, Practical Methods of Optimization. As compared to general trust-region approaches in scalar minimization, the approach here for solving non-linear systems amounts to assuming that the Hessian matrix can be approximated using information from the Jacobian of the system.
  • NewtonBB Simple 1D Newton solver with a fallback to bisection. If the zero of the function can be bounded then this solver can return a result even if the function is badly behaved.

BUILDING

The build system is cmake-based.

Dependencies:

TESTING

Run cmake with -DENABLE_TESTS=ON and do make test

DEVELOPMENT

The develop branch is the main development branch for snls. Changes to develop are by pull request.

AUTHORS

The principal devleoper of SNLS is Nathan Barton, nrbarton@llnl.gov. Brett Wayne has also made contributions.

CITATION

SNLS may be cited using the following bibtex entry:

@Misc{snls,
title = {SNLS},
author = {Wayne, Brett M. and Barton, Nathan R. and USDOE National Nuclear Security Administration},
abstractNote = {{SNLS} (small non-linear solver) is a C++ library for solving non-linear systems. The emphasis is on small systems of equations for which direct factorization of the Jacobian is appropriate. The systems of equations may be numerically ill-conditioned. Implementation is amenable to {GPU} usage, with the idea that class instantiation would occur within device functions.},
doi = {10.11578/dc.20181217.9},
year = 2018,
month = {9},
url = {https://github.com/LLNL/SNLS},
annote =    {
   https://www.osti.gov//servlets/purl/1487196
   https://www.osti.gov/biblio/1487196
}
}

LICENSE

License is under the BSD-3-Clause license. See LICENSE file for details. And see also the NOTICE file.

SPDX-License-Identifier: BSD-3-Clause

LLNL-CODE-761139

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