SILO



The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group

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with additional support from the Analytics Group of the Northwestern Mutual Life Insurance Company

Northwestern Mutual

Using sparse coding to find independent components of conflict | Convex Quadratic Programming with Variable Bounds

Bryan Daniels | Hyemin Jeon , Post-doc in WID and graduate student in IE respectively

Date and Time: Feb 15, 2012 (12:30 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building

Abstract:

***Bryan:
Both cognitive constraints and limited amounts of data restrict the
complexity of inferential models. Sparse coding is an elegant way to
address these restrictions, extracting correlated subparts from data
in a way that can be efficient, predictive, and adaptive. We present
a sparse coding method for use on binary data and use it to
study correlated groups in conflict in a macaque society. The
method reveals specific groups as predictable components of
conflict in our data, and a description of collective behavior in
terms of sparse groups can be shown to be cognitively efficient.

***Hyemin:
A mixed binary set constrained by convex, nonseparable quadratic functions appears as a substructure in many practical mixed integer nonlinear programs (MINLPs) including portfolio management or model selection. We aim to obtain a good approximation of its convex hull, and our approach starts by transforming the set using Cholesky factorization. A number of valid nonlinear inequalities for the transformed set are derived, most of which are represented as second-order cone constraints. Computational experiments were conducted to compare the relaxations to the convex hull.