KNITRO is a solver for nonlinear optimization. It is the most powerful and versatile solver on the market, providing three state-of-the-art algorithms. The broad range of behaviors exhibited by nonlinear problems makes this an essential feature. KNITRO is designed for large problems with dimensions running into the hundred thousands. It is effective for solving linear, quadratic, and nonlinear smooth optimization problems, both convex and nonconvex. It is also effective for nonlinear regression, problems with complementarity constraints (MPCCs or MPECs), and mixed-integer programming (MIPs), particular convex mixed integer, nonlinear problems (MINLP). KNITRO is highly regarded for its robustness and efficiency. KNITRO provides a wide range of user options, and offers interfaces to C, C++, Fortran, Java, AMPL, AIMMS, GAMS, MPL, Mathematica, MATLAB Microsoft Excel, and LabVIEW. Continuing active development and support ensures that KNITRO will remain the leader in nonlinear optimization.

References in zbMATH (referenced in 175 articles , 1 standard article )

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  1. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  2. Chen, Rui; Qian, Xinwu; Miao, Lixin; Ukkusuri, Satish V.: Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium (2020)
  3. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)
  4. Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
  5. Estrin, Ron; Friedlander, Michael P.; Orban, Dominique; Saunders, Michael A.: Implementing a smooth exact penalty function for general constrained nonlinear optimization (2020)
  6. Estrin, Ron; Friedlander, Michael P.; Orban, Dominique; Saunders, Michael A.: Implementing a smooth exact penalty function for equality-constrained nonlinear optimization (2020)
  7. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  8. Ghosh, Debdas; Sharma, Akshay; Shukla, K. K.; Kumar, Amar; Manchanda, Kartik: Globalized robust Markov perfect equilibrium for discounted stochastic games and its application on intrusion detection in wireless sensor networks. I. Theory (2020)
  9. Kardoš, Juraj; Kourounis, Drosos; Schenk, Olaf: Structure-exploiting interior point methods (2020)
  10. Leyffer, Sven; Vanaret, Charlie: An augmented Lagrangian filter method (2020)
  11. Pan, Maodong; Chen, Falai; Tong, Weihua: Volumetric spline parameterization for isogeometric analysis (2020)
  12. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  13. Armand, Paul; Tran, Ngoc Nguyen: Rapid infeasibility detection in a mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2019)
  14. Burlacu, Robert; Egger, Herbert; Groß, Martin; Martin, Alexander; Pfetsch, Marc E.; Schewe, Lars; Sirvent, Mathias; Skutella, Martin: Maximizing the storage capacity of gas networks: a global MINLP approach (2019)
  15. Drusvyatskiy, D.; Paquette, C.: Efficiency of minimizing compositions of convex functions and smooth maps (2019)
  16. Foroozandeh, Z.; Shamsi, M.; de Pinho, M. d. R.: A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems (2019)
  17. Furini, Fabio; Traversi, Emiliano; Belotti, Pietro; Frangioni, Antonio; Gleixner, Ambros; Gould, Nick; Liberti, Leo; Lodi, Andrea; Misener, Ruth; Mittelmann, Hans; Sahinidis, Nikolaos V.; Vigerske, Stefan; Wiegele, Angelika: QPLIB: a library of quadratic programming instances (2019)
  18. González Rueda, Ángel M.; González Díaz, Julio; Fernández de Córdoba, María P.: A twist on SLP algorithms for NLP and MINLP problems: an application to gas transmission networks (2019)
  19. Ha, Jung-Su; Choi, Han-Lim: On periodic optimal solutions of persistent sensor planning for continuous-time linear systems (2019)
  20. Hante, Falk M.; Schmidt, Martin: Complementarity-based nonlinear programming techniques for optimal mixing in gas networks (2019)

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