Abstract / Description of output
Multistage Stochastic Programming is a popular method to solve financial planning problems such as Asset and Liability Management (ALM). The desirability to have future scenarios match static and dynamic correlations between assets leads to problems of truly enormous sizes (often reaching tens of millions of unknowns or more). Clearly parallel processing becomes mandatory to deal with such problems.
Solution approaches for these problems include nested Decomposition and Interior Point Methods. The latter class in particular is appealing due to its flexibility with regard to model formulation and its amenability to parallelisation on massively parallel architectures. We review some of the results and challenges in this approach, demonstrate how popular risk measures can be integrated into the framework and address the issue of modelling for High Performance Computing.
Solution approaches for these problems include nested Decomposition and Interior Point Methods. The latter class in particular is appealing due to its flexibility with regard to model formulation and its amenability to parallelisation on massively parallel architectures. We review some of the results and challenges in this approach, demonstrate how popular risk measures can be integrated into the framework and address the issue of modelling for High Performance Computing.
Original language | English |
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Title of host publication | Euro-Par 2010 Parallel Processing Workshops |
Editors | Mario Guarracino, Frédéric Vivien, Jesper Träff, Mario Cannatoro, Marco Danelutto, Anders Hast, Francesca Perla, Andreas Knüpfer, Beniamino Di Martino, Michael Alexander |
Publisher | Springer |
Pages | 423-430 |
Number of pages | 8 |
Volume | 6586 |
Publication status | Published - 2011 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Berlin / Heidelberg |