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 120 articles , 1 standard article )

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  1. Akşin, Zeynep; Ata, Baris; Emadi, Seyed Morteza; Su, Che-Lin: Impact of delay announcements in call centers: an empirical approach (2017)
  2. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  3. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)
  4. Hallac, David; Wong, Christopher; Diamond, Steven; Sharang, Abhijit; Sosič, Rok; Boyd, Stephen; Leskovec, Jure: SnapVX: a network-based convex optimization solver (2017)
  5. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  6. Liu, Changliu; Tomizuka, Masayoshi: Real time trajectory optimization for nonlinear robotic systems: relaxation and convexification (2017)
  7. Sioshansi, Ramteen; Conejo, Antonio J.: Optimization in engineering. Models and algorithms (2017)
  8. Wan, Wei; Biegler, Lorenz T.: Structured regularization for barrier NLP solvers (2017)
  9. Arreckx, Sylvain; Lambe, Andrew; Martins, Joaquim R.R.A.; Orban, Dominique: A matrix-free augmented Lagrangian algorithm with application to large-scale structural design optimization (2016)
  10. Chrapary, Hagen; Ren, Yue: The software portal swMATH: a state of the art report and next steps (2016)
  11. Eggleston, Jonathan: An efficient decomposition of the expectation of the maximum for the multivariate normal and related distributions (2016)
  12. Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Extended formulations in mixed-integer convex programming (2016)
  13. Miles Lubin, Emre Yamangil, Russell Bent, Juan Pablo Vielma: Polyhedral approximation in mixed-integer convex optimization (2016) arXiv
  14. Rose, Daniel; Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.: Computational optimization of gas compressor stations: MINLP models versus continuous reformulations (2016)
  15. Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.: High detail stationary optimization models for gas networks: validation and results (2016)
  16. Yoda, Kunikazu; Prékopa, András: Convexity and solutions of stochastic multidimensional 0-1 knapsack problems with probabilistic constraints (2016)
  17. Ansari, Mohammad Reza; Mahdavi-Amiri, Nezam: A robust combined trust region-line search exact penalty projected structured scheme for constrained nonlinear least squares (2015)
  18. Birgin, E.G.; Martínez, J.M.; Prudente, L.F.: Optimality properties of an augmented Lagrangian method on infeasible problems (2015)
  19. Cai, Yongyang; Judd, Kenneth L.: Dynamic programming with Hermite approximation (2015)
  20. David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec: SnapVX: A Network-Based Convex Optimization Solver (2015) arXiv

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