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 143 articles , 1 standard article )

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  1. Ha, Jung-Su; Choi, Han-Lim: On periodic optimal solutions of persistent sensor planning for continuous-time linear systems (2019)
  2. Ishfaq, Rafay; Bajwa, Naeem: Profitability of online order fulfillment in multi-channel retailing (2019)
  3. Amaya Moreno, Liana; Fügenschuh, Armin; Kaier, Anton; Schlobach, Swen: A nonlinear model for vertical free-flight trajectory planning (2018)
  4. Bonafini, M.: Convex relaxation and variational approximation of the Steiner problem: theory and numerics (2018)
  5. Bonafini, Mauro; Orlandi, Giandomenico; Oudet, Édouard: Variational approximation of functionals defined on 1-dimensional connected sets: the planar case (2018)
  6. Cozad, Alison; Sahinidis, Nikolaos V.: A global MINLP approach to symbolic regression (2018)
  7. Jara-Moroni, Francisco; Pang, Jong-Shi; Wächter, Andreas: A study of the difference-of-convex approach for solving linear programs with complementarity constraints (2018)
  8. Kang, Yongha; Albey, Erinc; Uzsoy, Reha: Rounding heuristics for multiple product dynamic lot-sizing in the presence of queueing behavior (2018)
  9. Kröger, Ole; Coffrin, Carleton; Hijazi, Hassan; Nagarajan, Harsha: Juniper: an open-source nonlinear branch-and-bound solver in Julia (2018)
  10. Liu, Changliu; Lin, Chung-Yen; Tomizuka, Masayoshi: The convex feasible set algorithm for real time optimization in motion planning (2018)
  11. Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Polyhedral approximation in mixed-integer convex optimization (2018)
  12. Ma, Ding; Judd, Kenneth L.; Orban, Dominique; Saunders, Michael A.: Stabilized optimization via an NCL algorithm (2018)
  13. Mitsos, Alexander; Najman, Jaromił; Kevrekidis, Ioannis G.: Optimal deterministic algorithm generation (2018)
  14. Putkaradze, Vakhtang; Rogers, Stuart: Constraint control of nonholonomic mechanical systems (2018)
  15. Akşin, Zeynep; Ata, Baris; Emadi, Seyed Morteza; Su, Che-Lin: Impact of delay announcements in call centers: an empirical approach (2017)
  16. Andrei, Neculai: Continuous nonlinear optimization for engineering applications in GAMS technology (2017)
  17. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  18. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)
  19. Grohmann, Sanja; Urošević, Dragan; Carrizosa, Emilio; Mladenović, Nenad: Solving multifacility Huff location models on networks using metaheuristic and exact approaches (2017)
  20. Hallac, David; Wong, Christopher; Diamond, Steven; Sharang, Abhijit; Sosič, Rok; Boyd, Stephen; Leskovec, Jure: SnapVX: a network-based convex optimization solver (2017)

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