Large Scale Optimization Methods for Data Science Applications

Project: Research project

Project Details


In recent years, Optimization has been recognized as a crucial component in Data Science disciplines,
such as machine learning, data mining and statistics. Due to the high dimensionality of
real-world data sets and models, it is often imperative to follow the principle of parsimony, in the
form of either sparsity or minimal rank of solutions. This principle can be formulated in terms of
optimization models, which are often very di�cult to solve. Our research plan focuses on these key
challenges that face the �eld of optimization and its applications in Data Science.
To help guide our research, we selected three important problems that are in need of improved
optimization models and algorithms: matrix completion, sparse inverse covariance estimation, and
dictionary learning. These problems o�er a focal point for a rich set of applications, ranging from
image processing and text mining to predictive control and recommender systems. State-of-the-art
algorithms are often slow and do not parallelize well. Our goal is to develop optimization algorithms
that are tailored to these classes of problems.
Effective start/end date5/1/1410/31/17


  • Office of Naval Research (N00014-14-1-0313)


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