Contributed Session Thu.3.H 1058

Thursday, 15:15 - 16:45 h, Room: H 1058

Cluster 10: Implementations & software [...]

Modeling languages and software III

 

Chair: Robert Fourer

 

 

Thursday, 15:15 - 15:40 h, Room: H 1058, Talk 1

Per Rutquist
Trajectory optimization with TOMLAB/PROPT

Coauthors: Marcus M. Edvall, Kenneth Holmström

 

Abstract:
We demonstrate an easy-to-use symbolic interface for trajectory optimization, and for general linear and nonlinear programming, using Matlab syntax.
PROPT allows ordinary differential equations (as well as more general differential algebraic equations) to be converted into optimization constraints using pseudo-spectral collocation. Multi-phase problems and links to time-independent equations are also handled in a straightforward manner.
Equations are entered via the symbolic interface TOMSYM, which automatically generates the linear constraint matrix as well as derivatives of nonlinear functions. These are then integrated with the entire TOMLAB suite of solvers, which includes mixed-integer optimization with KNITRO and MINLPBB and global optimization with multiMin. As a result, we achieve very good results on many problems described as "hard'' in literature.
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As illustration, we present solved examples from robotics, aerospace, process control and parameter estimation.

 

 

Thursday, 15:45 - 16:10 h, Room: H 1058, Talk 2

Christian Valente
Optimisation under uncertainty: Software tools for modelling and solver support

Coauthors: Gautam Mitra, Victor Zverovich

 

Abstract:
Algebraic modelling languages are now well established as a formulation tool used by practitioners and academics in the field of operational research. We describe an integrated modelling and solver platform for investigating stochastic and robust optimisation models. We consider the following well known approaches: stochastic programming (SP) with recourse, chance constrained programming, integrated chance constrained programming, and robust optimisation.
In an earlier work Valente et al. introduced Stochastic extensions of AMPL called SAMPL. The extended language constructs are used to represent two- and multi-stage SP problems. In this paper we describe a set of extensions to SAMPL for representing robust optimisation problems and the additional classes of SP problems listed above. We not only describe syntax and semantics of the extensions but also discuss solver requirements, reformulation techniques and connection between the modelling system and external solvers. In particular, we show that direct representation of some of the modelling constructs not only makes the models easier to understand but also facilitates the use of specialised solution algorithms.

 

 

Thursday, 16:15 - 16:40 h, Room: H 1058, Talk 3

Vincent Beraudier
Modeling best practices: How to write good optimization models efficiently thanks to IBM ILOG CPLEX Optimization Studio's Integrated Development Environment (IDE) and its debugging support

Coauthors: Ferenc Katai, Arnaud Schulz

 

Abstract:
A good optimization model has to execute fast, but also it has to be scalable to adapt to changes in data and/or constraints.
Therefore at development time, debugging support is a crucial factor to deliver scalable optimization models into solutions.
This interactive demo will show the debugging capabilities to deal with optimization model testing in IBM ILOG CPLEX Optimization Studio.
The talk will consist in a showcase on an application developed by IBM.
It will describe in fair amount of details how OPL language and its IDE helps its users to detect memory and time bottlenecks in an optimization model.
It will show how OPL provides its users introspection mechanisms to detect issues early on, to avoid them, and eventually to eliminate errors as soon as possible in the development process.
We will also discuss how the OPL language and Studio ensure quality throughout the entire application life-cycle, from design to deployment.

 

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