Optimization Days 2017
HEC Montréal, May 810, 2017
1st Canadian Healthcare Optimization Workshop (CHOW)
HEC Montréal, May 1011, 2017
JOPT2017
HEC Montréal, 8 — 11 May 2017
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
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
We propose an optimization framework which integrates mixedinteger 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
This talk describes a new decomposition approach where smallsized 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.