Journées de l'optimisation 2017

HEC Montréal, 8-10 mai 2017

1er Atelier Canadien sur l'optimisation des soins de santé (CHOW)

HEC Montréal, 10-11 mai 2017


HEC Montréal, 8 — 11 mai 2017

Horaire Auteurs Mon horaire

WB3 Méthodes d'optimisation / Optimization methods

10 mai 2017 10h30 – 12h10

Salle: Marie-Husny

Présidée par Ryan Caverly

4 présentations

  • 10h30 - 10h55

    Service systems with adjustable speed

    • Eman Almehdawe, prés., University of Regina

    We investigate a fluid model of a service system, in which customers are discharged at an adjustable speed, which influences the proportion of customers that require rework after a delay. We formulate the model as a delay-differential equation. We study the transient and steady-state behavior of the system occupancy in four different regimes and we obtain conditions under which speedup reduces average occupancy.

  • 10h55 - 11h20

    Factorization-free methods for computed tomography

    • Maxime Mclaughlin, prés., Polytechnique Montreal
    • Dominique Orban, GERAD - Polytechnique Montréal

    We study a tomographic reconstruction problem in cylindrical coordinates. A change of variables involving a Fourier matrix attempts to improve the conditioning of the Hessian but introduces linear inequality constraints. The scale and density of the problem call for factorization-free methods. We argue that projections into the feasible set can be computed efficiently by solving a bound-constrained least-squares problem with a fast linear operator. In this talk, we focus on a Barzilai-Borwein projected gradient method and a trust-region projected Newton method. We compare two solvers for the projection subproblem: a two-metric projection algorithm and a trust-region projected Newton method. The performance of several combinations is assessed using synthetic data on the reconstruction problem.

  • 11h20 - 11h45

    Extremum-Seeking Guidance on SO(3) Using a Kalman Filter

    • Alex Walsh, University of Michigan
    • James Richard Forbes, prés., McGill University

    Extremum-seeking guidance endeavours to drive the output of a system to the extremum of an unknown objective function. This paper proposes an extremum-seeking guidance algorithm for constrained subsets of SO(3). The algorithm is enabled by a novel constrained Kalman filter, and is demonstrated on a spacecraft attitude guidance problem.

  • 11h45 - 12h10

    Controller Design for Regional Pole and Zero Placement using Linear Matrix Inequalities and the Modified Minimum Gain Lemma

    • Ryan Caverly, prés., University of Michigan
    • James Richard Forbes, McGill University

    This talk presents controller synthesis methods involving linear matrix inequalities that place closed-loop zeros in the open left-half complex plane. This prevents nonminimum phase closed-loop behaviour, often characterized by a response that initially moves in the opposite direction from a desired set point before asymptotically reaching the desired set point.