Invited Session Wed.1.H 3503

Wednesday, 10:30 - 12:00 h, Room: H 3503

Cluster 6: Derivative-free & simulation-based optimization [...]

Exploiting structure in derivative-free optimization

 

Chair: Luís Nunes Vicente and Stefan Wild

 

 

Wednesday, 10:30 - 10:55 h, Room: H 3503, Talk 1

Carl T. Kelley
Sparse interpolatory models for molecular dynamics

Coauthor: David S. Mokrauer

 

Abstract:
We describe a method for using interpolatory models to accurately and efficiently simulate molecular excitation and relaxation. We use sparse interpolation for efficiency and local error estimation and control for robustness and accuracy.
The objective of the project is to design an efficient algorithm for simulation of light-induced molecular transformations. The simulation seeks to follow the relaxation path of a molecule after excitation by light. The simulator is a predictive tool to see if light excitation and subsequent return to the unexcited or ground state will produce a different configuration than the initial one.
The goals of the simulation are not only to identify the end point, but to report the entire path in an high-dimensional configuration space so that one can look for nearby paths to interesting configurations and examine the energy landscape near the path to see if low energy barriers make jumping to a different path possible.

 

 

Wednesday, 11:00 - 11:25 h, Room: H 3503, Talk 2

Warren L. Hare
Derivative free optimization for finite minimax functions

Coauthors: Mason Macklem, Julie Nutini

 

Abstract:
In this talk we consider derivative-free optimization for finite minimax problems; that is objective
functions of the form F = max ( fi). We work under the assumption that each fi is provided
via an oracle function that is analytically unavailable. Commonly such problems are dealt with via
smoothing techniques. In this talk we avoid smooth techniques that obscure the substructure of such
functions and instead use the substructure to generate a approximate subdifferential. Techniques on
robust subdifferentials are employed to further improve convergence. Convergence and some numerical
results are presents.

 

 

Wednesday, 11:30 - 11:55 h, Room: H 3503, Talk 3

Rommel Regis
A derivative-free trust-region algorithm for constrained, expensive black-box optimization using radial basis functions

Coauthor: Stefan Wild

 

Abstract:
This talk will present a derivative-free algorithm for constrained black-box optimization where the objective and constraint functions are computationally expensive. The proposed algorithm employs a trust-region framework that uses interpolating radial basis function (RBF) models for the objective and constraint functions and is an extension of the ORBIT algorithm. This algorithm will be compared with alternative methods on a series of test problems and on an automotive application with 124 decision variables and 68 black-box inequality constraints.

 

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