Incluant une Journée industrielle de l'optimisation
HEC Montréal, 7  9 mai 2012
JOPT2012
HEC Montréal, 7 — 9 mai 2012
TB3 Outils d'optimisation / Optimization Tools
8 mai 2012 10h30 – 12h10
Salle: Metro inc.
Présidée par David Woodruff
4 présentations

10h30  10h55
LocalSolver: BlackBox Local Search for Combinatorial Optimization
We present LocalSolver 2.0, modelandrun solver based on localsearch techniques. It can handle very large nonlinear problems with millions of 01 decisions. LocalSolver offers simple APIs as well as an efficient modeling language for fast prototyping. It is used in several reallife applications and has succeeded the first tour of Google ROADEF/EURO Challenge.

10h55  11h20
Recent Developments in SurrogateBased Algorithms for Constrained BlackBox Optimization
This talk will present a survey of some recent surrogatebased algorithms for the optimization
of expensive blackbox functions subject to expensive blackbox constraints. It will also provide numerical results on some test problems and on an automotive problem with 124 decision variables and 68 blackbox constraints. 
11h20  11h45
The Mesh Adaptive Direct Search Algorithm with Reduced Number of Directions
The Mesh Adaptive Direct Search (MADS) algorithm is designed for blackbox optimization where the objective function and constraints correspond to a costly computer simulation. Each iteration of the algorithm consists of launching the simulation at a nite number of trial points. These candidates are constructed using typically 2n directions, where n is the number of variables. This presentation shows some ways of reducing that number to a minimal positive spanning set of n + 1 directions. This transformation is generic and can be applied to any method that generates at least n + 1 directions.

11h45  12h10
Pyomo: Modeling and Solving Mathematical Programs in Python
We describe Pyomo, an open source software package for modeling and solving mathematical programs in Python. Pyomo can be used to define abstract and concrete problems, create problem instances, and solve these instances with standard opensource and commercial solvers. Pyomo provides a capability that is commonly associated with algebraic modeling languages such as AMPL, AIMMS, and GAMS. In contrast, Pyomo’s modeling objects are embedded within a fullfeatured highlevel programming language with a rich set of supporting libraries. Pyomo leverages the capabilities of the Coopr software library, which together with Pyomo is part of IBM’s COINOR opensource initiative for operations research software. Coopr integrates Python packages for defining optimizers, modeling optimization applications, and managing computational experiments. Numerous examples illustrating advanced scripting applications are provided.