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


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" [...]


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