Improving Optimization performance with Parallel Computing

Parallel computing can accelerate the solution of computationally expensive optimization problems

Description

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, which you can read at http://www.mathworks.com/company/newsletters/?s_cid=nws_flex

• MATLAB release: MATLAB 7.8 (R2009a)

[Shahab Anbarjafari]

It was interesting :) quite useful (at least for me)

[Rody Oldenhuis]

Useful indeed. Just out of curiosity: did you make the PDF yourself? How do you do that? It looks awesome!