SNOBFIT -- Stable Noisy Optimization by Branch and Fit

The software package SNOBFIT for bound-constrained (and soft-constrained) noisy optimization of an expensive objective function is described. It combines global and local search by branching and local fits. The program is made robust and flexible for practical use by allowing for hidden constraints, batch function evaluations, change of search regions, etc.

[1]  Donald R. Jones,et al.  Global versus local search in constrained optimization of computer models , 1998 .

[2]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[3]  Arnold Neumaier,et al.  Global Optimization by Multilevel Coordinate Search , 1999, J. Glob. Optim..

[4]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[5]  Hermann Schichl,et al.  GLOPT { A Program for Constrained Global Optimization , 1997 .

[6]  Frank Vanden Berghen,et al.  CONDOR, a new parallel, constrained extension of Powell's UOBYQA algorithm: experimental results and comparison with the DFO algorithm , 2005 .

[7]  M. J. D. Powell,et al.  UOBYQA: unconstrained optimization by quadratic approximation , 2002, Math. Program..

[8]  Katya Scheinberg,et al.  Recent progress in unconstrained nonlinear optimization without derivatives , 1997, Math. Program..

[9]  Owen J. Eslinger,et al.  Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization , 2001 .

[10]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[11]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[12]  Panos M. Pardalos,et al.  Global optimization by continuous grasp , 2007, Optim. Lett..

[13]  Michael C. Ferris,et al.  A Direct Search Algorithm for Optimization with Noisy Function Evaluations , 2000, SIAM J. Optim..

[14]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..

[15]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[16]  Klaus Schittkowski,et al.  More test examples for nonlinear programming codes , 1981 .

[17]  William J. Welch,et al.  Computer experiments and global optimization , 1997 .

[18]  P. Deuflhard,et al.  Affine Invariant Convergence Theorems for Newton’s Method and Extensions to Related Methods , 1979 .

[19]  M. J. D. Powell,et al.  Direct search algorithms for optimization calculations , 1998, Acta Numerica.

[20]  Boxin Tang.  Orthogonal Array-Based Latin Hypercubes , 1993 .

[21]  Stephen J. Wright,et al.  Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .

[22]  C. D. Perttunen,et al.  Lipschitzian optimization without the Lipschitz constant , 1993 .

[23]  A. Neumaier,et al.  A grid algorithm for bound constrained optimization of noisy functions , 1995 .

[24]  Virginia Torczon,et al.  Numerical Optimization Using Computer Experiments , 1997 .

[25]  A. Owen.  Lattice Sampling Revisited: Monte Carlo Variance of Means Over Randomized Orthogonal Arrays , 1994 .

[26]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[27]  R. F. Guratzsch,et al.  SENSOR PLACEMENT OPTIMIZATION UNDER UNCERTAINTY FOR STRUCTURAL HEALTH MONITORING SYSTEMS OF HOT AEROSPACE STRUCTURES , 2007 .

[28]  C. T. Kelley,et al.  Superlinear Convergence and Implicit Filtering , 1999, SIAM J. Optim..

[29]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.