Journées de l'optimisation 2016

HEC Montréal, Québec, Canada, 2 — 4 mai 2016

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TA1 Exposé magistral III / Tutorial III

3 mai 2016 10h30 – 12h10

Salle: Banque Scotia

Présidée par Dominique Orban

1 présentation

  • 10h30 - 12h00

    Modeling and optimization using IPsolve

    • Aleksandr Aravkin, prés., University of Washington

    IPsolve is an interior point method that exploits conjugate representations of PLQ penalties. In this tutorial, we will begin with a set of penalties used in machine learning and signal processing, and work with their convex conjugates to get a uniform representation used by IPsolve for modeling. PLQ penalties include least square (regression, kernel machines), huber (robust regression), 1-norm (sparse regularization), asymmetric 1-norm (quantile regression), hinge loss (support vector machines), and many others, including CVAR for financial applications. We will go through several application examples, in each case modeling and solving them using IPsolve. The tutorial will focus on modeling in different application domains, with some supporting material from convex analysis to go between problem representations.