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TU Munchen Professors Win Operations Research Award For Work Using FICO Optimization Software

MUNICH, July 25, 2013 /PRNewswire/— FICO (NYSE:FICO), a leading predictive analytics and decision management software company, today announced that their academic partners from the Technische Universität München have received the EURO Excellence in Practice Award 2013 for work using the FICO® Xpress Optimization Suite. Professors Peter Gritzmann, Andreas Brieden and Dr. Steffen Borgwardt received the award for their work on "Geometric Clustering for the Consolidation of Farm- and Woodland." The award was presented in Rome on July 4 at a conference organized by the operations research associations EURO and INFORMS.

(Logo: http://photos.prnewswire.com/prnh/20111010/CG83314LOGO)  

The Technische Universitat Munchen has been a FICO Academic Partner since 2005, using the FICO Xpress Optimization Suite in teaching and research – including the work of optimizing the model for this award-winning project.

"The problem of land consolidation is emotionally very rewarding, as it helps individual farmers and entire rural regions to survive economically," said Peter Gritzmann, "But it is also mathematically exciting, since it is extremely challenging and requires deep new mathematics and algorithms. Additionally, this research paves the way for fascinating new applications in the analysis of Big Data. Naturally, the mathematical models and ideas could not be brought into the reality without an extremely powerful optimization workhorse like FICO Xpress."

Professors Brieden and Gritzmann have already successfully developed extended and specifically customized models for several problem areas, including insurance loss prediction, maintenance prediction, and shipping logistics. The foundations of their algorithms are deep geometric insights that lead to high-quality segmentation of big data. Part of what makes their work particularly interesting for practitioners is the fact that it is a non-black-box algorithm which works with small setup costs and has high predictive power.

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