A bank asset and liability management model
Operations Research
MSLiP: a computer code for the multistage stochastic linear programming problem
Mathematical Programming: Series A and B
Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Formulating two-stage stochastic programs for interior point methods
Operations Research
A massively parallel algorithm for nonlinear stochastic network problems
Operations Research
Stochastic network programming for financial planning problems
Management Science - Focused issue on financial modeling
An efficient block-oriented approach to parallel sparse Cholesky factorization
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Symmetric indefinite systems for interior point methods
Mathematical Programming: Series A and B
Primal-relaxed dual global optimization approach
Journal of Optimization Theory and Applications
Multi-stage stochastic linear programs for portfolio optimization
Annals of Operations Research
Mathematical Programming: Series A and B
Computational Optimization and Applications
Solving the multi-stage portfolio optimization problem with a novel particle swarm optimization
Expert Systems with Applications: An International Journal
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This paper describes a framework for modeling significant financial planning problems based on multi-stage optimization under uncertainty. Applications include risk management for institutions, banks, government entities, pension plans, and insurance companies. The approach also applies to individual investors who are interested in integrating investment choices with savings and borrowing strategies. A dynamic discrete-time structure addresses realistic financial issues. The resulting stochastic program is enormous by current computer standards, but it possesses a special structure that lends itself to parallel and distributed optimization algorithms. Interior-point methods are particularly attractive. Solving these stochastic programs presents a major challenge for the computational operations research and computer science community.