MapReduce/Bigtable for Distributed Optimization
published: Jan. 13, 2011, recorded: December 2010, views: 985
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For large data it can be very time consuming to run gradient based optimization, for example to minimize the log-likelihood for maximum entropy models. Distributed methods are therefore appealing and a number of distributed gradient optimization strategies have been proposed including: distributed gradient, asynchronous updates, and iterative parameter mixtures. In this paper, we evaluate these various strategies with regards to their accuracy and speed over MapReduce/Bigtable and discuss the techniques needed for high performance.
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