Operations Research Proceedings 1995 pp 102-107 | Cite as
SG-Portfolio Test Problems for Stochastic Multistage Linear Programming
Abstract
The solvability of dynamic decision problems suffer from the curse of dimensionality which limits the planning horizon one can afford for mapping the real problem into a numeric solvable dynamic optimization model. In this note, stochastic multistage programming is applied to dynamic fixed-income portfolio selection. We report on the goodness fixed income portfolio problems are currently solved with, using barycentric approximation. In particular, it is illustrated how the planning horizon becomes effective with respect to the numerical effort for solving the programs. The computational results serve as benchmark for decomposition methods of mathematical programming.
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