The data revolution is reshaping science, technology and business.
Large-scale distributed optimization is emerging as a key tool in
extracting useful information from the deluge of data that arises in
many areas of application. In this project you will develop optimization
methods for big data that are based on the alternating direction method of
multipliers (ADMM) and the stochastic gradient descent (SGD) method.
Applications of interest include, for example, matrix and tensor
decompositions that may be used to generate user recommendations for
movie and music streaming. The optimization algorithms will first be
explored in Matlab. Areas of study may include algorithmic convergence
acceleration of the ADMM and SGD methods, or efficient distributed
implementations in the Spark framework for big data analytics. Some relevant links: Required: -a previous course on numerical computing -interest in and experience with programming (any of Matlab, Python, C, Java, C++, Scala, Spark, ...) Please email me if you are interested in this project or have questions about it. |
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