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.

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  1. Ribeiro, L. H. M. S.; Dal Poggetto, V. F.; Arruda, J. R. F.: Robust optimization of attenuation bands of three-dimensional periodic frame structures (2022)
  2. Krishnasamy, Ganesh; Kulkarni, Anand J.; Shastri, Apoorva S.: An improved cohort intelligence with panoptic learning behavior for solving constrained problems (2021)
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  5. Elnagar, Gamal N.; Kazemi, Mohammad A.: Optimization of functional integral equations: a spectral approach (2020)
  6. Williams, David M.; Frontin, Cory V.; Miller, Edward A.; Darmofal, David L.: A family of symmetric, optimized quadrature rules for pentatopes (2020)
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  8. Ri, Jun-Hyok; Hong, Hyon-Sik: A basis reduction method using proper orthogonal decomposition for shakedown analysis of kinematic hardening material (2019)
  9. Salem, Malek Ben; Bachoc, François; Roustant, Olivier; Gamboa, Fabrice; Tomaso, Lionel: Gaussian process-based dimension reduction for goal-oriented sequential design (2019)
  10. Ashpazzadeh, Elmira; Lakestani, Mehrdad; Razzaghi, Mohsen: Nonlinear constrained optimal control problems and cardinal Hermite interpolant multiscaling functions (2018)
  11. Carini, Manuela; Maiolo, Mario; Pantusa, Daniela; Chiaravalloti, Francesco; Capano, Gilda: Modelling and optimization of least-cost water distribution networks with multiple supply sources and users (2018)
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  14. Kulkarni, Anand J.; Baki, M. F.; Chaouch, Ben A.: Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems (2016)
  15. Shahi, Mohsen Rahimi Mazrae; Mehdipour, Elnaz Fallah; Amiri, Maghsoud: Optimization using simulation and response surface methodology with an application on subway train scheduling (2016)
  16. Ciavolino, Enrico; Carpita, Maurizio; Al-Nasser, Amjad: Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches (2015)
  17. Edrisi Tabriz, Yousef; Lakestani, Mehrdad: Direct solution of nonlinear constrained quadratic optimal control problems using B-spline functions. (2015)
  18. Sachsenberg, Björn; Schittkowski, Klaus: A combined SQP-IPM algorithm for solving large-scale nonlinear optimization problems (2015)
  19. Smith, J. MacGregor: Optimal workload allocation in closed queueing networks with state dependent queues (2015)
  20. Sun, Songtao; Zhang, Qiuhua; Loxton, Ryan; Li, Bin: Numerical solution of a pursuit-evasion differential game involving two spacecraft in low earth orbit (2015)

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