Consortium updates open standard for algorithm development

Nov. 8, 2006
The Numerical Mathematics Consortium announced the latest revision to a technical specification, introduced earlier this year, that defines an open mathematics semantics standard for numerical algorithm development.

Paris, France, Austin, Texas, USA, Waterloo, Canada, and Cambridge, Mass., USA:
The Numerical Mathematics Consortium announced the latest revision to a technical specification, introduced earlier this year, that defines an open mathematics semantics standard for numerical algorithm development.

This update includes newly ratified functions from classes that include polynomials and vector analysis. In addition to the new function definitions, the consortium resolved significant technical issues that simplify ratification of new functions.

The resolved issues address practical topics related to algorithm design and compliance with the standard. Topics include: when to specify vector orientation, how to support vectorisation, what it means to be compliant, and how to choose a semantic representation. Settling these technical issues provides guidelines that help to improve the rate of progress for new function adoption.

"The Numerical Mathematics Consortium has made significant progress since introducing the original technical specification," said John Pasquarette, NI Director of Software Marketing. "The new revision reflects the consortium's efforts to bridge theory to real-world applications as well as expand the opportunity for others to get involved. Recent efforts of the consortium provide a powerful framework for future development."

The founding companies of the Numerical Mathematics Consortium—which include INRIA, Maplesoft, Mathsoft recently acquired by PTC, and National Instruments—established the organisation in 2005 to create a specification that facilitates reuse and portability of numeric algorithms.

To reach this goal, the organisation is initially focusing on standardising a core set of mathematical functions that can be used in a variety of application areas, such as industrial control, embedded design, and scientific research. In addition, the functions will be able to be easily reused by researchers and developers in industry and academia.

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