New NAG Library for Python featuring new Optimization and Nearest Correlation Matrix techniques launched at PyCon 2017

The latest update to the NAG Library offers more numerical functionality for Python developers

New routines include: the NAG Optimization Modelling Suite featuring new Interior Point Method and Semidefinite Programming plus new Quadrature, LAPACK, Nearest Correlation Matrix and OpenMP Utilities

17 May 2017 – The Numerical Algorithms Group (NAG) has made available the latest version of their NAG Library for Python. The updated numerical algorithm library now features new Optimization techniques and lots of other features, and will be demonstrated at the PyCon 2017 conference in Portland, Oregon this May.

The NAG Library for Python gives users of the Python programming language access to hundreds of fully tested, robust and high performing mathematical and statistical routines in-line with the latest releases of Python. Further enhancements also include updated Example Scripts which give detailed explanation of the routines to aid in their use.

New Optimization Solvers in the NAG Optimization Modelling Suite

Included in the new Optimization Modelling Suite is the first release of a linear and nonlinear semidefinite programming (SDP) solver, created in collaboration with the University of Birmingham, UK. The new type of SDP constraints, matrix inequalities, allows users to address a completely new set of problems or to express some existing problems in a whole new way. In addition, our SDP with bilinear matrix inequalities, is the only supported commercial solver in the world. This type of problem is especially important in system and control theory.

A new interior point method for large-scale nonlinear programming problems has been integrated into the Library to complement our existing methods. The solver has been developed by NAG collaborators Andreas Waechter, Northwestern University, and Carl D. Laird, Purdue University, who were awarded the prestigious Wilkinson Prize for outstanding achievements in numerical software for this work. There will be a plethora of applications across various fields such as finance, engineering and operational research which will exploit the new addition.

To make these new solvers accessible, the Optimization Modelling Suite allows users to build up their problem to be solved in stages, instead of calling one monolithic solver with many arguments; making it simpler to use and easier to avoid mistakes.

The new solvers add to NAG’s existing optimization routines featured in the Library which cover all the following areas:

  • Unconstrained and constrained nonlinear programming
  • Nonlinear least squares, data fitting
  • Linear and quadratic programming
  • Derivative free optimization
  • Global optimization
  • Mixed-integer nonlinear optimization

Other additions in the NAG Library for Python

More new mathematical and statistical content in

NAG Consultants Caleb Hamilton and Chris Brandt will be showing the new mathematical and statistical functionality in the NAG Library for Python at PyCon Conference this May. Come and see us at booth #442 in Oregon May 17-25.