Interval Techniques in Computing, Modelling, Simulation and Optimization

A Workshop Dedicated to the 30th anniversary of the Interval Analysis
Part of IASTED International Conference on Modeling, Simulation and Optimization (Gold Coast, Australia)
Afternoon of May 5, 1996

Convenor: Simeon Simoff, University of Sydney, Australia.

Co-Convenor: Fay Sudweeks (, University of Sydney, Australia.

Duration: Half day.

Price: $US 65.00

Venue: The Workshop was held at the conference hotel.

Overview: The Interval Techniques (IT) workshop was part of the MSO'96 conference. This workshop was intended to provide a forum for provocative discussion related to the theory, application and evolution of methods and techniques of Interval Analysis.

Since Moore's publication on Interval Analysis appeared in 1966, the academic research community has been promoting the use of interval techniques to achieve guaranteed bounds for the results of numerical computations. Conventional numerical algorithms compute an estimate for an answer and an estimate for computing error. To be able to judge about the accuracy of the estimated answer user needs also extensive, expensive error analysis. The quality of scientific software packages has improved dramatically, but there are still problems for which catastrophically incorrect answers are returned to the user with no warning.

Interval techniques compute an interval in which the correct answer is validated to lie. If interval techniques compute an answer [100.953456, 100.953457], then the result is within this interval, that means that 7 decimal places are known to be correct. If as a result an algorithm yields the interval [-10^30, +10^30], then it means that the answer is unknown. Consequently, interval computations include an assurance of their quality.

From the field of reliable computations interval approach entered the area of modeling, simulation and optimizaion. The advantages of the interval techniques for model building are the "wide" initial assumptions (methods require only that the unknown true value lies within the interval obtained from the experiments) and easy to perform hypothesis testing.

In the early days, it was not very convenient to apply interval methods because suitable software was not widely available. Now there are several commercially available systems, others are under development. However, interval technique is known mostly within a narrow community of researchers in computer science and applied mathematics. Therefore, its application is relatively immature and as such presents exciting and challenging issues to the scientists from different disciplines, related to modeling, simulation and optimization.

Workshop Material: Each participant received a copy of workshop proceedings.

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