October 20, 2016 – The Numerical Algorithms Group (NAG), experts in algorithms, software and HPC, announces the latest Mark of its flagship software, the NAG Library. At Mark 26, NAG have introduced an Optimization Modelling Suite for linear and nonlinear semidefinite programming and general nonlinear programming. In addition to the Optimization Modelling Suite, the NAG Library Mark 26 also features new routines in the important computational areas of Nearest Correlation Matrix and Quadrature.
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 for this work the prestigious Wilkinson Prize for outstanding achievements in numerical software. 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 Mark 26 Library
More new mathematical and statistical content in:
- Gaussian Quadrature
- Least Squares and Eigenvalue Problems (LAPACK)
- Nearest Correlation Matrix Functions
- OpenMP Utilities
The NAG Library is expertly developed, maintained, documented and supported. NAG experts are proud that much of the new content is added in direct response to customer requests, including those from major banks, market intelligence companies and Universities.
Correct results of computation are vital to business and research.” John Holden, NAG VP Global Markets. “NAG’s new Optimization solvers and the other routines added at Mark 26 have all been through a stringent testing process making them accurate, robust, flexible and reliable. It is fantastic to see leading research translated into fully documented, fast and robust code. We know that Mark 26 will be a hit and would like to thank all our developers as well as the code contributing academics”
The inherent flexibility of the mathematical and statistical functions in the NAG Library enable it to be used across multiple programming languages, environments and operating systems including C and C++, Cloudera, Julia, Excel, Java, Microsoft .NET, Python, Visual Basic, Fortran and many more.