Thursday, 14:15 - 14:40 h, Room: H 3003A

 

Anke Tröltzsch
A model-based trust-region algorithm for derivative-free optimization and its adaptation to handle noisy functions and gradients

Coauthors: Serge Gratton, Philippe L. Toint

 

Abstract:
Optimization algorithms are crucial to solve industrial optimization problems characterized by different requirements. Depending on the availability of the gradient, different algorithms have been developed such as Derivative-Free Optimization (DFO) or gradient-based algorithms. The software BC-DFO (Bound-Constrained Derivative-Free Optimization), using a self-correcting property of the geometry and an active-set strategy to handle bound constraints, has shown to be efficient on a set of test problems of the CUTEr collection. Here, we propose to extend this code by adding the possibility of handling noisy gradient information. It is well known that the L-BFGS method is a very efficient method for solving bound-constrained optimization problems when accurate gradient information is provided. Whereas, this is often not the case in practice. We would like to propose a family of algorithms which contains both, the derivative-free approach and the L-BFGS method, and which is therefore able to optimally take into account the error occurring in the cost function and/or gradient of the problem. We will present numerical experiments on academic and real-life test cases.

 

Talk 3 of the invited session Thu.2.H 3003A
"Addressing noise in derivative-free optimization" [...]
Cluster 6
"Derivative-free & simulation-based optimization" [...]

 

  The system of instant Virginia Payday Loans allows any adult U.S. citizen. In this section we give only a brief summary recommendation for admission of Levitra. Full information can be found in the instructions for receiving medications with vardenafil.