One of the features of the parallel implementation method is

Abstract

Many econometric problems can benefit from the application of parallel computing techniques, and recent advances in hardware and software have made such application feasible. There are a number of freely available software libraries that make it possible to write message passing parallel programs using personal computers or Unix workstations. This review discusses one of these-the LAM (Local Area Multiprocessor) implementation of MPI (the Message Passing Interface).

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The Journal of Applied Econometrics is a bi-monthly international journal which publishes articles of high quality dealing with the application of existing as well as new econometric techniques to a wide variety of problems in economics and related subjects, covering topics in measurement, estimation, testing, forecasting, and policy analysis. The emphasis is on the careful and rigorous application of econometric techniques and the appropriate interpretation of the results. The economic content of the articles is stressed. The intention of the Journal is to provide an outlet for innovative, quantitative research in economics which cuts across areas of specialization, involves transferable techniques, and is easily replicable by other researchers. Contributions that introduce statistical methods that are applicable to a variety of economic problems are actively encouraged. The Journal also features occasional sections of short papers re-evaluating previously published papers.

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Abstract

This paper generalizes the widely used Nelder and Mead (Comput J 7:308–313, 1965) simplex algorithm to parallel processors. Unlike most previous parallelization methods, which are based on parallelizing the tasks required to compute a specific objective function given a vector of parameters, our parallel simplex algorithm uses parallelization at the parameter level. Our parallel simplex algorithm assigns to each processor a separate vector of parameters corresponding to a point on a simplex. The processors then conduct the simplex search steps for an improved point, communicate the results, and a new simplex is formed. The advantage of this method is that our algorithm is generic and can be applied, without re-writing computer code, to any optimization problem which the non-parallel Nelder–Mead is applicable. The method is also easily scalable to any degree of parallelization up to the number of parameters. In a series of Monte Carlo experiments, we show that this parallel simplex method yields computational savings in some experiments up to three times the number of processors.

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Authors and Affiliations

1. Department of Economics, New York University, 269 Mercer St., 7FL, New York, NY, 10003, USA

   Donghoon Lee & Matthew Wiswall

Authors

1. Donghoon Lee

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2. Matthew Wiswall

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Correspondence to Matthew Wiswall.

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Lee, D., Wiswall, M. A Parallel Implementation of the Simplex Function Minimization Routine. Comput Econ 30, 171–187 (2007). https://doi.org/10.1007/s10614-007-9094-2

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• Received: 10 September 2006

• Accepted: 15 May 2007

• Published: 26 June 2007

• Issue Date: September 2007

• DOI: https://doi.org/10.1007/s10614-007-9094-2

Keywords

• Parallel computing
• Optimization algorithms

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