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Optimization Challenges in Tensor Factorization

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Speaker: Tamara Kolda, Sandia National Labs, Livermore, CA
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Location: 2112 MSB
Start time: Thu, Feb 4 2016, 11:00AM

Tensors are multiway arrays, and tensor decomposition is a powerful tool for compression and data interpretation. In this talk, we demonstrate the utility of tensor decomposition with several examples and explain the optimization challenges, both theoretical and practical. The optimization problems are nonconvex, but they can typically be solved via an alternating approach that yields convex subproblems. We consider open problems such as determining the model complexity, tensor completion, incorporating symmetries and other constraints, handling ambiguities in scaling and permutation, enforcing structure like sparsity, and considering alternative objective functions.

Tamara (Tammy) Kolda is a Distinguished Member of the Technical Staff at Sandia National Laboratories in Livermore, CA. She received a 2003 Presidential Early Career Award for Scientists and Engineers (PECASE), was named a Distinguished Scientist of the Association for Computing Machinery (ACM) in 2011 and a Fellow of the Society for Industrial and Applied Mathematics (SIAM) in 2015. She has led numerous projects in computational science and data analysis on topics in multilinear algebra and tensor decompositions, graph models and algorithms, data mining, optimization, nonlinear solvers, parallel computing and the design of scientific software.