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 196 articles , 1 standard article )

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  1. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  2. Ji, Ye; Wang, Meng-Yun; Pan, Mao-Dong; Zhang, Yi; Zhu, Chun-Gang: Penalty function-based volumetric parameterization method for isogeometric analysis (2022)
  3. Lüttgens, Luis; Jurgelucks, Benjamin; Wernsing, Heinrich; Roy, Sylvain; Büskens, Christof; Flaßkamp, Kathrin: Autonomous navigation of ships by combining optimal trajectory planning with informed graph search (2022)
  4. Tangi Migot; Dominique Orban; Abel Soares Siqueira: DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility (2022) not zbMATH
  5. Wang, Chun-Han; Zhang, Wenzhu; Dai, Yue; Lee, Yu-Ching: Frequency competition among airlines on coordinated airports network (2022)
  6. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  7. Gabriel, Steven A.; Leal, Marina; Schmidt, Martin: Solving binary-constrained mixed complementarity problems using continuous reformulations (2021)
  8. Holzmann, Tim; Smith, J. Cole: The shortest path interdiction problem with randomized interdiction strategies: complexity and algorithms (2021)
  9. Kuppusamy, Saravanan; Magazine, Michael J.; Rao, Uday: Buyer selection and service pricing in an electric fleet supply chain (2021)
  10. Liu, Yanchao: A note on solving DiDi’s driver-order matching problem (2021)
  11. Mahajan, Ashutosh; Leyffer, Sven; Linderoth, Jeff; Luedtke, James; Munson, Todd: Minotaur: a mixed-integer nonlinear optimization toolkit (2021)
  12. Nguyen, Trang T.; Richard, Jean-Philippe P.; Tawarmalani, Mohit: Convexification techniques for linear complementarity constraints (2021)
  13. Oudet, Édouard; Kao, Chiu-Yen; Osting, Braxton: Computation of free boundary minimal surfaces via extremal Steklov eigenvalue problems (2021)
  14. Pecci, Filippo; Stoianov, Ivan; Ostfeld, Avi: Relax-tighten-round algorithm for optimal placement and control of valves and chlorine boosters in water networks (2021)
  15. Saban, Daniela; Weintraub, Gabriel Y.: Procurement mechanisms for assortments of differentiated products (2021)
  16. Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee, Timo Kuosmanen: pyStoNED: A Python Package for Convex Regression and Frontier Estimation (2021) arXiv
  17. Singh, Derek; Zhang, Shuzhong: Robust arbitrage conditions for financial markets (2021)
  18. Casanellas, Glòria; Castro, Jordi: Using interior point solvers for optimizing progressive lens models with spherical coordinates (2020)
  19. 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)
  20. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)

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