Optimization Days 2017

HEC Montréal, May 8-10, 2017

1st Canadian Healthcare Optimization Workshop (CHOW)

HEC Montréal, May 10-11, 2017



HEC Montréal, 8 — 11 May 2017

Schedule Authors My Schedule

TD4 Apprentissage et diagrammes de décision / Learning and decision diagrams

May 9, 2017 03:30 PM – 05:10 PM

Location: Meloche Monnex

Chaired by Andre Augusto Cire

3 Presentations

  • 03:30 PM - 03:55 PM

    Optimization methods for neural networks training

    • Dimitri Papadimitriou, presenter, Bell Labs

    Given a set of labeled data points, the optimization problem associated to the training of neural networks aims at determining the parameters, e.g., synaptic weights, which minimize the empirical loss between the true output to the given input and the predicted output. The (regularized) problem is nonconvex even when the loss (and the regularization) function is convex. We analyze and compare extended bundle and trust region methods for nonconvex loss and non/convex non/smooth regularization term.

  • 03:55 PM - 04:20 PM

    A hybrid decision diagram approach for the job shop scheduling problem

    • Jaime Gonzalez, presenter, Polytecnique Montréal
    • Louis-Martin Rousseau, Polytechnique Montréal
    • Andre A. Cire, University of Toronto Scarborough
    • Andrea Lodi, Polytechnique Montreal

    We propose an optimization framework which integrates mixed-integer programming (MIP) and multivalued decision diagrams (MDDs) for optimization. A MDD representation of the problem identifies parts of the search space that can be efficiently explored by MIP technology, while the MIP results are iteratively used to refine the MDD.

  • 04:20 PM - 04:45 PM

    Decompositions based on Decision Diagrams

    • Andre Augusto Cire, presenter, University of Toronto
    • David Bergman, University of Connecticut

    This talk describes a new decomposition approach where small-sized decision diagrams exactly represent different portions of a discrete optimization problem, all of which are linked through special constraints. We discuss potential techniques to solve the underlying decomposition problem and show a number of applications of this method.