NLPQL

NLPQL: a Fortran subroutine for solving constrained nonlinear programming problems. NLPQL is a FORTRAN implementation of a sequential quadratic programming method for solving nonlinearly constrained optimization problems with differentiable objective and constraint functions. At each iteration, the search direction is the solution of a quadratic programming subproblem. This paper discusses the organization of NLPQL, including the formulation of the subproblem and the information that must be provided by a user. A summary is given of the performance of different algorithmic options of NLPQL on a collection of test problems (115 hand-selected or application problems, 320 randomly generated problems). The performance of NLPQL is compared with that of some other available codes.


References in zbMATH (referenced in 121 articles )

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  1. Elnagar, Gamal N.; Kazemi, Mohammad A.: Optimization of functional integral equations: a spectral approach (2020)
  2. Zhang, Ying; Yu, Changjun; Xu, Yingtao; Bai, Yanqin: Minimizing almost smooth control variation in nonlinear optimal control problems (2020)
  3. Ri, Jun-Hyok; Hong, Hyon-Sik: A basis reduction method using proper orthogonal decomposition for shakedown analysis of kinematic hardening material (2019)
  4. Salem, Malek Ben; Bachoc, François; Roustant, Olivier; Gamboa, Fabrice; Tomaso, Lionel: Gaussian process-based dimension reduction for goal-oriented sequential design (2019)
  5. Ashpazzadeh, Elmira; Lakestani, Mehrdad; Razzaghi, Mohsen: Nonlinear constrained optimal control problems and cardinal Hermite interpolant multiscaling functions (2018)
  6. Tromme, Emmanuel; Held, Alexander; Duysinx, Pierre; Brüls, Olivier: System-based approaches for structural optimization of flexible mechanisms (2018)
  7. Yassin, Belkourchia; Lahcen, Azrar; Zeriab, Es-Sadek Mohamed: Hybrid optimization procedure applied to optimal location finding for piezoelectric actuators and sensors for active vibration control (2018)
  8. Edrisi Tabriz, Yousef; Lakestani, Mehrdad: Direct solution of nonlinear constrained quadratic optimal control problems using B-spline functions. (2015)
  9. Sachsenberg, Björn; Schittkowski, Klaus: A combined SQP-IPM algorithm for solving large-scale nonlinear optimization problems (2015)
  10. Smith, J. MacGregor: Optimal workload allocation in closed queueing networks with state dependent queues (2015)
  11. Sun, Songtao; Zhang, Qiuhua; Loxton, Ryan; Li, Bin: Numerical solution of a pursuit-evasion differential game involving two spacecraft in low earth orbit (2015)
  12. Varshney, Rahul; Khan, M. G. M.; Fatima, Ummatul; Ahsan, M. J.: Integer compromise allocation in multivariate stratified surveys (2015)
  13. Paffrath, M.; Wever, U.: An efficient algorithm for a certain class of robust optimization problems (2014)
  14. Raghav, Yashpal Singh; Ali, Irfan; Bari, Abdul: Multi-objective nonlinear programming problem approach in multivariate stratified sample surveys in the case of non-response (2014)
  15. Park, Kun Soo; Whitt, Ward: Continuous-time Markov chain models to estimate the premium for extended hedge fund lockups (2013)
  16. Alwardi, H.; Wang, S.; Jennings, L. S.; Richardson, S.: An adaptive least-squares collocation radial basis function method for the HJB equation (2012)
  17. Exler, Oliver; Lehmann, Thomas; Schittkowski, Klaus: A comparative study of SQP-type algorithms for nonlinear and nonconvex mixed-integer optimization (2012)
  18. Gerzen, Nikolai; Materna, Daniel; Barthold, Franz-Joseph: The inner structure of sensitivities in nodal based shape optimisation (2012)
  19. Mashayekhi, S.; Ordokhani, Y.; Razzaghi, M.: Hybrid functions approach for nonlinear constrained optimal control problems (2012)
  20. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)

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