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 105 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. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  3. Wan, Wei; Biegler, Lorenz T.: Structured regularization for barrier NLP solvers (2017)
  4. Eggleston, Jonathan: An efficient decomposition of the expectation of the maximum for the multivariate normal and related distributions (2016)
  5. Miles Lubin, Emre Yamangil, Russell Bent, Juan Pablo Vielma: Polyhedral approximation in mixed-integer convex optimization (2016) arXiv
  6. Rose, Daniel; Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.: Computational optimization of gas compressor stations: MINLP models versus continuous reformulations (2016)
  7. Yoda, Kunikazu; Prékopa, András: Convexity and solutions of stochastic multidimensional 0-1 knapsack problems with probabilistic constraints (2016)
  8. Ansari, Mohammad Reza; Mahdavi-Amiri, Nezam: A robust combined trust region-line search exact penalty projected structured scheme for constrained nonlinear least squares (2015)
  9. Birgin, E.G.; Martínez, J.M.; Prudente, L.F.: Optimality properties of an augmented Lagrangian method on infeasible problems (2015)
  10. Cai, Yongyang; Judd, Kenneth L.: Dynamic programming with Hermite approximation (2015)
  11. David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec: SnapVX: A Network-Based Convex Optimization Solver (2015) arXiv
  12. Gould, Nick I.M.; Orban, Dominique; Toint, Philippe L.: An interior-point $\ell_1$-penalty method for nonlinear optimization (2015)
  13. Hiller, Benjamin; Humpola, Jesco; Lehmann, Thomas; Lenz, Ralf; Morsi, Antonio; Pfetsch, Marc E.; Schewe, Lars; Schmidt, Martin; Schwarz, Robert; Schweiger, Jonas; Stangl, Claudia; Willert, Bernhard M.: Computational results for validation of nominations (2015)
  14. Hora, Stephen C.; Kardeş, Erim: Calibration, sharpness and the weighting of experts in a linear opinion pool (2015)
  15. Janka, Dennis: Sequential quadratic programming with indefinite Hessian approximations for nonlinear optimum experimental design for parameter estimation in differential-algebraic equations (2015)
  16. Kanzow, Christian; Schwartz, Alexandra: The price of inexactness: convergence properties of relaxation methods for mathematical programs with complementarity constraints revisited (2015)
  17. Koch, Thorsten (ed.); Hiller, Benjamin (ed.); Pfetsch, Marc (ed.); Schewe, Lars (ed.): Evaluating gas network capacities (2015)
  18. Lee, Yu-Ching; Pang, Jong-Shi; Mitchell, John E.: Global resolution of the support vector machine regression parameters selection problem with LPCC (2015)
  19. Lee, Yu-Ching; Pang, Jong-Shi; Mitchell, John E.: An algorithm for global solution to bi-parametric linear complementarity constrained linear programs (2015)
  20. Leung, Michael P.: Two-step estimation of network-formation models with incomplete information (2015)

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