2016 Honours project: Numerical optimization methods for big data analytics

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: 

-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.