Andrew R. Conn is a research staff member at the IBM T. J. Watson Research Center, Yorktown Heights, NY. In 1994 he was (with N. I. M. Gould and Ph. L. Toint) a joint recipient of the Beale/Orchard-Hays Prize for Computational Excellence in Mathematical Programming and with Chandu Visweswariah he received an IBM Corporate Award in 2002 for contributions to circuit tuning. Currently his major application projects are in the petroleum industry.
Preface
1. Introduction
Part I. Sampling and Modeling: 2. Sampling and linear models
3. Interpolating nonlinear models
4. Regression nonlinear models
5. Underdetermined interpolating models
6. Ensuring well poisedness and suitable derivative-free models
Part II. Frameworks and Algorithms: 7. Directional direct-search methods
8. Simplicial direct-search methods
9. Line-search methods based on simplex derivatives
10. Trust-region methods based on derivative-free models
11. Trust-region interpolation-based methods
Part III. Review of Other Topics: 12. Review of surrogate model management
13. Review of constrained and other extensions to derivative-free optimization
Appendix: software for derivative-free optimization
Bibliography
Index.
