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Solving Dynamic Programming Problems on a Computational Grid

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  • Yongyang Cai
  • Kenneth Judd
  • Greg Thain
  • Stephen Wright

Abstract

We implement a dynamic programming algorithm on a computational grid consisting of loosely coupled processors, possibly including clusters and individual workstations. The grid changes dynamically during the computation, as processors enter and leave the pool of workstations. The algorithm is implemented using the Master–Worker library running on the HTCondor grid computing platform, which can be deployed on many networks. We implement value function iteration for large dynamic programming problems of two kinds: optimal growth problems and dynamic portfolio problems. We present examples that solve in hours on HTCondor but would take weeks if executed on a single workstation. The cost of using HTCondor is small because it uses CPU resources that otherwise would be idle. The use of HTCondor can increase a researcher’s computational productivity by at least two orders of magnitude. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Yongyang Cai & Kenneth Judd & Greg Thain & Stephen Wright, 2015. "Solving Dynamic Programming Problems on a Computational Grid," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 261-284, February.
    • Handle: RePEc:kap:compec:v:45:y:2015:i:2:p:261-284
    DOI: 10.1007/s10614-014-9419-x
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    16. Cai, Yongyang & Judd, Kenneth L. & Lontzek, Thomas S. & Michelangeli, Valentina & Su, Che-Lin, 2017. "A Nonlinear Programming Method For Dynamic Programming," Macroeconomic Dynamics, Cambridge University Press, vol. 21(2), pages 336-361, March.
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    Cited by:

    1. Yongyang Cai & Kenneth L. Judd & Rong Xu, 2013. "Numerical Solution of Dynamic Portfolio Optimization with Transaction Costs," NBER Working Papers 18709, National Bureau of Economic Research, Inc.
    2. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
    3. Wonjun Chang & Michael C. Ferris & Youngdae Kim & Thomas F. Rutherford, 2020. "Solving Stochastic Dynamic Programming Problems: A Mixed Complementarity Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 925-955, March.
    4. Yongyang Cai & Thomas S. Lontzek, 2019. "The Social Cost of Carbon with Economic and Climate Risks," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 2684-2734.
    5. Yongyang Cai & Kenneth Judd, 2015. "Dynamic programming with Hermite approximation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 245-267, June.
    6. Cai, Yongyang & Steinbuks, Jevgenijs & Elliott, Joshua & Hertel, Thomas W., 2014. "The effect of climate and technological uncertainty in crop yields on the optimal path of global land use," Policy Research Working Paper Series 7009, The World Bank.
    7. Lilia Maliar, 2015. "Assessing gains from parallel computation on a supercomputer," Economics Bulletin, AccessEcon, vol. 35(1), pages 159-167.
    8. Yongyang Cai & William Brock & Anastasios Xepapadeas & Kenneth Judd, 2019. "Climate Policy under Spatial Heat Transport: Cooperative and Noncooperative Regional Outcomes," Papers 1909.04009, arXiv.org.
    9. Cerqueti, Roy & Quaranta, Anna Grazia & Ventura, Marco, 2016. "Innovation, imitation and policy inaction," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 22-30.
    10. Rongju Zhang & Nicolas Langren'e & Yu Tian & Zili Zhu & Fima Klebaner & Kais Hamza, 2016. "Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach," Papers 1610.07694, arXiv.org, revised Jun 2019.
    11. Cai, Yongyang & Brock, William & Xepapadeas, Anastasios, 2016. "Climate Change Economics and Heat Transport across the Globe: Spatial-DSICE," 2017 Allied Social Sciences Association (ASSA) Annual Meeting, January 6-8, 2017, Chicago, Illinois 251833, Agricultural and Applied Economics Association.
    12. Peter Schober & Julian Valentin & Dirk Pflüger, 2022. "Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 185-224, January.

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    More about this item

    Keywords

    Numerical dynamic programming; Parallel computing; Grid computing; Value function iteration; Dynamic portfolio optimization; Multi-country optimal growth; C61; C63; G11;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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