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An Introduction to Simulated Annealing Algorithms for the Computation of Economic Equilibrium

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  • Wu, Lihua
  • Wang, Yuyun

Abstract

Economic equilibrium computation has raised the issue of global optimization algorithms since economic equilibrium problems can be cast as a global optimization problem. However, nearly all conventional algorithms stop when they find a local optimum. Over the last decade a number of new optimization algorithms have appeared, simulated annealing is one of them. It is a powerful stochastic search algorithm applicable to a wide range of problems for which little prior knowledge is available, and it asymptotically probabilistically converges to a global optimum. In this paper, we will give a brief introduction to simulated annealing and apply it to the computation of economic equilibrium. We also reported our computational experience in the paper. This early result shows that the application of simulated annealing to computation of economic equilibrium is encouraging and it deserves further research. Citation Copyright 1998 by Kluwer Academic Publishers.

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  • Wu, Lihua & Wang, Yuyun, 1998. "An Introduction to Simulated Annealing Algorithms for the Computation of Economic Equilibrium," Computational Economics, Springer;Society for Computational Economics, vol. 12(2), pages 151-169, October.
    • Handle: RePEc:kap:compec:v:12:y:1998:i:2:p:151-69
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    Cited by:

    1. Paolo Postiglione & M. Andreano & Roberto Benedetti, 2013. "Using Constrained Optimization for the Identification of Convergence Clubs," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 151-174, August.
    2. Christopher Garcia, 2016. "Winner Determination Algorithms for Combinatorial Auctions with Sub-cardinality Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 401-421, March.
    3. Roger A. McCain, 2000. "Road Rage: Imitative Learning Of Self-Destructive Behavior In An Agent-Based Simulation," Computing in Economics and Finance 2000 270, Society for Computational Economics.
    4. Creel, Michael & Kristensen, Dennis, 2016. "On selection of statistics for approximate Bayesian computing (or the method of simulated moments)," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 99-114.
    5. Grohall, Guenther & Jung, Juergen, 2003. "Multiple Objective Step Function Maximization with Genetic Algorithms and Simulated Annealing," Economics Series 141, Institute for Advanced Studies.

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