Objective
The scope of quantitative research broadens due to the increasing availability of large data sets. In order to extract relevant information new statistical procedures are developed. Often, these procedures result in complex optimization problems which cannot be tackled by standard algorithms. Thus, there is an urgent need for the further dissemination of recent advances in heuristic optimization. These tools are still rarely used in statistics, econometrics, and quantitative finance. This neglect is due to missing information about the complexity of the problems, missing knowledge about the tools, and a missing standard for the analysis of the additional stochastic component introduced.
COMISEF will close these gaps by consolidating European research activities and by providing a research training framework for young scientists. COMISEF will generate synergies between researchers from different disciplines and overcome the fragmentation of the European Research Area. It will bundle resources to establish a new optimization paradigm in statistics, econometrics and finance. COMISEF focuses on new optimization methods, their implementation (including parallel, distributed, and grid computing), their applications in statistics, econometrics and quantitative finance, and the statistical analysis of procedures and implementations. A cohort of young scientists will acquire the relevant knowledge for the development and implementation of innovative computational tools. These experts are required in academia and in industry in Europe, e.g. in the finance and related sectors. Thus, educating these experts will provide European value added. Based on the scientific results, in particular a well established formal framework for the analysis of convergence of optimization heuristics, and the successful training of a cohort of researchers, it is expected that the new methods will become a standard in the disciplines covered by the network and the related industry.
Fields of science
Call for proposal
FP6-2005-MOBILITY-1
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