Collaborative Research: Algorithms for Large-Scale Stochastic and Nonlinear Optimization

Project: Research project

Project Details


Nonlinear stochastic optimization plays a vital role in a wide range of applications, includ-
ing machine learning, statistics, energy, and signal processing. In machine learning, models are
formulated parametrically and those parameters are optimized to best predict output for yet
unseen data. Some machine learning problems are distinguished by the immense scale of data
they face and by the high dimensional nature of the parameters requiring optimization. The
goal of this proposal is to develop new algorithms for large-scale stochastic optimization prob-
lems of this kind. The proposal describes a broad research agenda of creating new algorithms
that are more e�cient and more robust than classical methods, and are better able to address
the challenges unique to data science applications. The proposal consists of three projects,
two of which are thematically related as they focus on stochastic problems, and a project that
addresses non-smooth structured deterministic optimization problems. In all cases, the goal is
to design and implement algorithms in software, and test them on realistic applications.
Effective start/end date8/1/167/31/20


  • National Science Foundation (DMS-1620022)


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