SUBPLEX is a subspace-searching simplex method for the unconstrained optimization of general multivariate functions. Like the Nelder-Mead simplex method it generalizes, the subplex method is well suited for optimizing noisy objective functions. The number of function evaluations required for convergence typically increases only linearly with the problem size, so for most applications the subplex method is much more efficient than the simplex method. It can be used like the Matlab fminsearch algorithm. SUBPLEX was developed by Tom Rowan for his Ph.D. Thesis: Functional Stability Analysis of Numerical Algorithms (University of Texas at Austin). Although SUBPLEX was originally developed as a routine for this analysis, it is a general-purpose algorithm well suited for optimization of high-dimensional noisy functions.

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  1. Zheltkova, V. V.; Zheltkov, Dmitry A.; Bocharov, G. A.; Tyrtyshnikov, Eugene: Application of the global optimization methods for solving the parameter estimation problem in mathematical immunology (2020)
  2. Quey, Romain; Renversade, Loïc: Optimal polyhedral description of 3D polycrystals: method and application to statistical and synchrotron X-ray diffraction data (2018)
  3. Zheltkova, Valeriya V.; Zheltkov, Dmitry A.; Grossman, Zvi; Bocharov, Gennady A.; Tyrtyshnikov, Eugene E.: Tensor based approach to the numerical treatment of the parameter estimation problems in mathematical immunology (2018)
  4. Alawieh, Hiba; Wicker, Nicolas; Ayoubi, Baydaa Al; Moulinier, Luc: Penalized multidimensional fitting for protein movement detection (2017)
  5. Lovecchio, C.; Cherukattil, S.; Cilenti, B.; Herrera, I.; Cataliotti, F. S.; Montangero, S.; Calarco, T.; Caruso, F.: Quantum state reconstruction on atom-chips (2015)
  6. Torres, Jose L.: Determination of mass diffusion coefficients in Norway spruce using an anisotropic, concentration-dependent model (2009)
  7. Zhang, Jiaxiang; Bogacz, Rafal; Holmes, Philip: A comparison of bounded diffusion models for choice in time controlled tasks (2009)
  8. Shea-Brown, Eric; Gilzenrat, Mark S.; Cohen, Jonathan D.: Optimization of decision making in multilayer networks: The role of locus coeruleus (2008)
  9. Wild, Jochen: Multi-objective constrained optimisation in aerodynamic design of high-lift systems (2008)
  10. Herrmann, U.: Multiple discipline take-off weight minimization for a supersonic transport aircraft (2007)
  11. Han, Lixing; Neumann, Michael: Effect of dimensionality on the Nelder--Mead simplex method (2006)
  12. Haunschild, M. D.; Wahl, S. A.; Freisleben, B.; Wiechert, W.: A general framework for large-scale model selection (2006)
  13. Howell, Gary W.; Diaa, Nadia: Algorithm 841: BHESS: Gaussian reduction to a similar banded Hessenberg form (2005)
  14. Sofroniou, Mark; Spaletta, Giulia: Precise numerical computation (2005)
  15. Kajberg, J.; Lindkvist, G.: Characterisation of materials subjected to large strains by inverse modelling based on in-plane displacement fields (2004)
  16. Kajberg, J.; Sundin, K. G.; Melin, L. G.; Ståhle, P.: High strain-rate tensile testing and viscoplastic parameter identification using microscopic high-speed photography. (2004)
  17. Singer, Saša; Singer, Sanja: Efficient implementation of the Nelder-Mead search algorithm (2004)
  18. Eriksson, M.; Wikman, B.; Bergman, G.: Estimation of material parameters at elevated temperatures by inverse modelling of a Gleeble experiment (2003)
  19. Kolda, Tamara G.; Lewis, Robert Michael; Torczon, Virginia: Optimization by direct search: New perspectives on some Classical and modern methods (2003)
  20. Wild, Jochen: On the potential of numerical optimization of high-lift multi-element airfoils based on the solution of the Navier-Stokes-equations (2003)

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