OPT++: An object-oriented toolkit for nonlinear optimization
ACM Transactions on Mathematical Software (TOMS)
Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination
INFORMS Journal on Computing
Experience with Approximations in the Trust-Region Parallel Direct Search Algorithm
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Simultaneous optimization and uncertainty quantification
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
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We present a new class of optimization methods that incorporates a parallel direct search (PDS) method within a trust-region Newton framework. This approach combines the inherent parallelism of PDS with the rapid and robust convergence properties of Newton methods. Numerical tests have yielded favorable results for both standard test problems and engineering applications. In addition, the new method appears to be more robust in the presence of noisy functions, which are inherent in many engineering simulations.