Optimization with an auxiliary constraint and decomposition
SIAM Journal on Control and Optimization
Approximate scenario solutions in the progressive hedging algorithm: a numerical study
Annals of Operations Research
On convergence of an augmented Lagrangian decomposition method for sparse convex optimization
Mathematics of Operations Research
Augemented Lagrangian Techniques for Elliptic State Constrained Optimal Control Problems
SIAM Journal on Control and Optimization
Computers and Operations Research
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Discrete-time optimal control problems arise naturally in many economic problems. Despite the rapid growth in computing power and new developments in the literature, many economic problems are still quite challenging to solve. Economists are aware of the limitations of some of these approaches for solving these problems due to memory and computational requirements. However, many of the economic models present some special structure that can be exploited in an efficient manner. This paper introduces a decomposition methodology, based on a mathematical programming framework, to compute the equilibrium path in dynamic models by breaking the problem into a set of smaller independent subproblems. We study the performance of the method solving a set of dynamic stochastic economic models. The numerical results reveal that the proposed methodology is efficient in terms of computing time and accuracy.