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Solving correlation matrix completion problems using parallel differential evolution

University of British Columbia

Matrix Completion problems have been receiving increased attention due to their varied applicability in different domains. Correlation matrices arise often in studying multiple streams of time series data like technical analysis of stock market data. Often some of the values in the matrix are unknown and some reasonable replacements have to be found at the earliest opportunity to avert an unwanted consequence or keep up the pace in the business. After looking to background research related to solving this problem, we propose a new parallel technique that can solve general correlation matrix completion problems over a set of computers connected to a high speed network. We present some of our results where we could reduce the execution time.

Text

2010-12-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/30302

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/