Solving Stochastic Dynamic Programming Problems: A Mixed Complementarity Approach

Wonjun Chang (), Michael C. Ferris, Youngdae Kim and Thomas F. Rutherford
Additional contact information
Wonjun Chang: CRA International
Michael C. Ferris: University of Wisconsin-Madison
Youngdae Kim: Mathematics and Computer Science Division, Argonne National Laboratory
Thomas F. Rutherford: University of Wisconsin-Madison

Computational Economics, 2020, vol. 55, issue 3, No 8, 925-955

Abstract: Abstract We present a mixed complementarity problem (MCP) formulation of continuous state dynamic programming problems (DP-MCP). We write the solution to projection methods in value function iteration (VFI) as a joint set of optimality conditions that characterize maximization of the Bellman equation; and approximation of the value function. The MCP approach replaces the iterative component of projection based VFI with a one-shot solution to a square system of complementary conditions. We provide three numerical examples to illustrate our approach.

Keywords: Dynamic Programming; Computable general equilibrium; Complementarity; Computational methods (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-019-09921-y

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