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Model
Size Reduction in Portfolio Optimization
Multi-stage stochastic programming provides
one of the more powerful tools for decision making
under uncertainty. Such decisions problems arise
in a variety of situations, including portfolio
optimization, currency hedging, and asset/liability
management in general. New algorithms developed
at the University of Arizona make it possible to
solve realistic portfolio optimization problems
by reducing model size without sacrificing solution
quality
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