User guide for NPSOL 5.0: Fortran package for nonlinear programming. NPSOL is a set of Fortran 77 subroutines for minimizing a smooth function subject to constraints, which may include simple bounds on the variables, linear constraints, and smooth nonlinear constraints. The user provides subroutines to define the objective and constraints functions and (optionally) their first derivatives. NPSOL is not intended for large sparse problems, but there is no fixed limit on problem size. NPSOL uses a sequential quadratic programming (SQP) algorithm, in which each search direction is the solution of a QP subproblem. Bounds, linear constraints, and nonlinear constraints are treated separately. Hence it is especially effective if the objective or constraint functions are expensive to evaluate.

References in zbMATH (referenced in 147 articles )

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  1. Williams, Nakeya D.; Mehlsen, Jesper; Tran, Hien T.; Olufsen, Mette S.: An optimal control approach for blood pressure regulation during head-up tilt (2019)
  2. Charles Driver and Johan Oud and Manuel Voelkle: Continuous Time Structural Equation Modeling with R Package ctsem (2017) not zbMATH
  3. Guzman, Yannis A.; Faruque Hasan, M. M.; Floudas, Christodoulos A.: Performance of convex underestimators in a branch-and-bound framework (2016)
  4. Neale, Michael C.; Hunter, Michael D.; Pritikin, Joshua N.; Zahery, Mahsa; Brick, Timothy R.; Kirkpatrick, Robert M.; Estabrook, Ryne; Bates, Timothy C.; Maes, Hermine H.; Boker, Steven M.: OpenMX 2.0: extended structural equation and statistical modeling (2016)
  5. Yang, Feng; Teo, Kok Lay; Loxton, Ryan; Rehbock, Volker; Li, Bin; Yu, Changjun; Jennings, Leslie: Visual MISER: an efficient user-friendly visual program for solving optimal control problems (2016)
  6. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  7. Auslender, A.; Ferrer, A.; Goberna, M. A.; López, M. A.: Comparative study of RPSALG algorithm for convex semi-infinite programming (2015)
  8. Cai, Yongyang; Judd, Kenneth L.: Dynamic programming with Hermite approximation (2015)
  9. Carvalho, Ashwin; Lefévre, Stéphanie; Schildbach, Georg; Kong, Jason; Borrelli, Francesco: Automated driving: the role of forecasts and uncertainty -- a control perspective (2015)
  10. Sun, Songtao; Zhang, Qiuhua; Loxton, Ryan; Li, Bin: Numerical solution of a pursuit-evasion differential game involving two spacecraft in low earth orbit (2015)
  11. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)
  12. Balesdent, Mathieu; Bérend, Nicolas; Dépincé, Philippe; Chriette, Abdelhamid: A survey of multidisciplinary design optimization methods in launch vehicle design (2012)
  13. Kulkarni, Ankur A.; Shanbhag, Uday V.: Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms (2012)
  14. Lantoine, Gregory; Russell, Ryan P.: A hybrid differential dynamic programming algorithm for constrained optimal control problems. I: Theory (2012)
  15. Eldred, Michael S.: Design under uncertainty employing stochastic expansion methods (2011)
  16. Kerstens, Kristiaan; Mounir, Amine; Van de Woestyne, Ignace: Geometric representation of the mean-variance-skewness portfolio frontier based upon the shortage function (2011)
  17. Pontani, Mauro: Numerical solution of orbital combat games involving missiles and spacecraft (2011)
  18. Chung, H.; Polak, E.; Sastry, S.: On the use of outer approximations as an external active set strategy (2010)
  19. Di Trapani, Lyall Jonathan; Inanc, Tamer: NTGsim: a graphical user interface and a 3D simulator for nonlinear trajectory generation methodology (2010)
  20. McAllister, Scott R.; Floudas, Christodoulos A.: An improved hybrid global optimization method for protein tertiary structure prediction (2010)

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