Improving Optimization performance with Parallel Computing

http://www.mathworks.com/company/newsletters/digest/2009/mar/parallel-optimization.html

First submitted by Linda Webb on 29 Jun 2009

Parallel computing can accelerate the solution of computationally expensive optimization problems

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Date	Contributor	Description	Rating
29 Jun 2009	Linda Webb	Engineers, scientists, and financial analysts frequently use optimization methods to solve computationally expensive problems such as smoothing the large computational meshes used in fluid dynamic simulations, performing image registration, or analyzing high-dimensional financial portfolios. However, computing a solution can take anywhere from hours to days. This article describes two ways to use the inherent parallelism in optimization problems to reduce the time to solution. The first example solves a mathematical problem using the parallel computing option in Optimization Toolbox™, and requires no code modification. The second, a practical engineering optimization problem, requires a single-line change in code. Both examples use Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™ to automate and manage the parallel computing tasks. By Stuart Kozola, The MathWorks This article was published in MATLAB Digest, March 2009

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whitepaper	Linda Webb	29 Jun 2009 at 4:16pm
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parallel for loop	Linda Webb	29 Jun 2009 at 4:16pm
parallel computing toolbox	Linda Webb	29 Jun 2009 at 4:16pm
parallel computing	Linda Webb	29 Jun 2009 at 4:16pm
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paper	Linda Webb	29 Jun 2009 at 4:16pm
optimization toolbox	Linda Webb	29 Jun 2009 at 4:16pm
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gradient estimation	Linda Webb	29 Jun 2009 at 4:16pm
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