Including an Industrial Optimization Day
HEC Montréal, May 7  9, 2012
JOPT2012
HEC Montréal, May 7 — 9, 2012
MD8 Méthodes d'optimisation en ingénierie des structures aéronautiques / Optimization Methods in Aerostructural Engineering
May 7, 2012 03:30 PM – 05:10 PM
Location: Transat
Chaired by Sylvain Arreckx
4 Presentations

03:30 PM  03:55 PM
A MatrixFree Augmented Lagrangian Method for Structural Optimization
We present how using a matrixfree optimizer for structural optimization allows us to handle a large number of design variables and failure constraints efficiently when the structural analysis is expensive. In particular, we show how to solve large structural design problems without aggregating the failure constraints.

03:55 PM  04:20 PM
Implementation of a MatrixFree Augmented Lagrangian for Nonlinear Optimization
In many applications, problems are so large that we cannot compute/store explicit Jacobians and don't have access to Hessian information. In this talk we outline a matrixfree algorithm for solving nonlinear problems with both equalities and inequalities. Our algorithm is based on an augmented Lagrangian approach and relies on matrixvector products only.

04:20 PM  04:45 PM
Optimization of Composite WingBox Structures: An Engineering Perspective
Composite materials have favourable strength and stiffness to weight properties that can be exploited to obtain highly tailored structures for highperformance structural applications. For these reasons, composites are increasingly used in primary aricraft structures. However, the favourable structural properties of composites can only be fully exploited using structural design optimization. Realistic composite wingbox design optimization problems require thousands of design variables and hundreds to thousands of constraints. In this presentation we outline our structural optimization framework and illustrate the difficulty and complexity of realistic composite wingbox design problems.

04:45 PM  05:10 PM
An Hybrid Approach for Solving NonConvex Quadratic Programs with NonConvex Quadratic Constraints
We present a new hybrid method to solve exactly nonconvex quadratic programs with nonconvex quadratic constraints. The method combines interval arithmetic branch and bound approaches (Messine 2004) and an improved version of the branch and cut algorithm of Audet et al (2000).