Solving Stochastic Dynamic Programming Problems: A Mixed Complementarity Approach

被引：0
|
作者：
Wonjun Chang
Michael C. Ferris
Youngdae Kim
Thomas F. Rutherford
机构：
[1] CRA International,Department of Agricultural and Applied Economics
[2] Mathematics and Computer Science Division,Department of Computer Sciences
[3] Argonne National Laboratory,undefined
[4] University of Wisconsin-Madison,undefined
[5] University of Wisconsin-Madison,undefined
[6] Optimization Group,undefined
[7] Wisconsin Institutes for Discovery,undefined
来源：
Computational Economics | 2020年 / 55卷
关键词：
Dynamic Programming; Computable general equilibrium; Complementarity; Computational methods;
D O I：
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中图分类号：
学科分类号：
摘要：
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.
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页码：925 / 955
页数：30
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