NPSOL

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 97 articles )

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  1. Guzman, Yannis A.; Faruque Hasan, M.M.; Floudas, Christodoulos A.: Performance of convex underestimators in a branch-and-bound framework (2016)
  2. 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)
  3. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  4. Auslender, A.; Ferrer, A.; Goberna, M.A.; López, M.A.: Comparative study of RPSALG algorithm for convex semi-infinite programming (2015)
  5. Cai, Yongyang; Judd, Kenneth L.: Dynamic programming with Hermite approximation (2015)
  6. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)
  7. Balesdent, Mathieu; Bérend, Nicolas; Dépincé, Philippe; Chriette, Abdelhamid: A survey of multidisciplinary design optimization methods in launch vehicle design (2012)
  8. Kulkarni, Ankur A.; Shanbhag, Uday V.: Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms (2012)
  9. Eldred, Michael S.: Design under uncertainty employing stochastic expansion methods (2011)
  10. Kerstens, Kristiaan; Mounir, Amine; Van de Woestyne, Ignace: Geometric representation of the mean-variance-skewness portfolio frontier based upon the shortage function (2011)
  11. Pontani, Mauro: Numerical solution of orbital combat games involving missiles and spacecraft (2011)
  12. Chung, H.; Polak, E.; Sastry, S.: On the use of outer approximations as an external active set strategy (2010)
  13. Yuan, De-Hu; Jin, Hui-Liang; Meng, Guo-Xiang; Feng, Zheng-Jin: A numerical approach to trajectory planning for yoyo movement (2010)
  14. Pytlak, Radosław: Conjugate gradient algorithms in nonconvex optimization (2009)
  15. Vaz, A.Ismael F.; Ferreira, Eugénio C.: Air pollution control with semi-infinite programming (2009)
  16. Chaplais, François; Petit, Nicolas: Inversion in indirect optimal control of multivariable systems (2008)
  17. Gill, Philip E.; Murray, Walter; Saunders, Michael A.; Tomlin, John A.; Wright, Margaret H.: George B. Dantzig and systems optimization (2008)
  18. Gounaris, Chrysanthos E.; Floudas, Christodoulos A.: Tight convex underestimators for $\mathcalC^2$-continuous problems. II: Multivariate functions (2008)
  19. Silva, C^andida Elisa P.; Monteiro, M.Teresa T.: A filter algorithm: Comparison with NLP solvers (2008)
  20. Silva, C^andida Elisa P.; Monteiro, M.Teresa T.: A filter inexact-restoration method for nonlinear programming (2008)

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