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Abstract |
Linear algebra is the foundation of scientific computing and its numerousapplications. Yet, the world is non-linear. In this lecture we argue that
it pays off to work with models that are described by non-linear polynomials, while still taking advantage of the power of numerical linear algebra. We present a glimpse of applied algebraic geometry, by discussing recent advances in tensor decomposition, polynomial optimization, and algebraic statistics. |
