KNITRO

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 166 articles , 1 standard article )

Showing results 1 to 20 of 166.
Sorted by year (citations)

1 2 3 ... 7 8 9 next

  1. 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)
  2. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)
  3. Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
  4. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  5. 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)
  6. Pan, Maodong; Chen, Falai; Tong, Weihua: Volumetric spline parameterization for isogeometric analysis (2020)
  7. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  8. Armand, Paul; Tran, Ngoc Nguyen: Rapid infeasibility detection in a mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2019)
  9. 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)
  10. Drusvyatskiy, D.; Paquette, C.: Efficiency of minimizing compositions of convex functions and smooth maps (2019)
  11. 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)
  12. 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)
  13. 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)
  14. Ha, Jung-Su; Choi, Han-Lim: On periodic optimal solutions of persistent sensor planning for continuous-time linear systems (2019)
  15. Hante, Falk M.; Schmidt, Martin: Complementarity-based nonlinear programming techniques for optimal mixing in gas networks (2019)
  16. Hu, Yunyi; Nagy, James G.; Zhang, Jianjun; Andersen, Martin S.: Nonlinear optimization for mixed attenuation polyenergetic image reconstruction (2019)
  17. Ishfaq, Rafay; Bajwa, Naeem: Profitability of online order fulfillment in multi-channel retailing (2019)
  18. Li, Can; Grossmann, Ignacio E.: A finite (\epsilon)-convergence algorithm for two-stage stochastic convex nonlinear programs with mixed-binary first and second-stage variables (2019)
  19. Park, Jungju; Mo, Jeonghoon: An efficient method for joint product line selection and pricing with fixed costs (2019)
  20. Schmidt, Klaus Werner; Hazır, Öncü: Formulation and solution of an optimal control problem for industrial project control (2019)

1 2 3 ... 7 8 9 next