MINOS

MINOS is a large-scale optimization system, for the solution of sparse linear and nonlinear programs. The objective function and constraints may be linear or nonlinear, or a mixture of both. The nonlinear functions must be smooth. Stable numerical methods are employed throughout. Features include a new basis package (for maintaining sparse LU factors of the basis matrix), automatic scaling of linear contraints, and automatic estimation of some or all gradients. Upper and lower bounds on the variables are handled efficiently. File formats for constraint and basis data are compatible with the industry MPS format. The source code is suitable for machines with a Fortran 66 or 77 compiler and at least 500K bytes of storage. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 459 articles )

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  1. Andreani, R.; Haeser, G.; Schuverdt, M. L.; Secchin, L. D.; Silva, P. J. S.: On scaled stopping criteria for a safeguarded augmented Lagrangian method with theoretical guarantees (2022)
  2. Kirches, Christian; Larson, Jeffrey; Leyffer, Sven; Manns, Paul: Sequential linearization method for bound-constrained mathematical programs with complementarity constraints (2022)
  3. Eichfelder, Gabriele; Klamroth, Kathrin; Niebling, Julia: Nonconvex constrained optimization by a filtering branch and bound (2021)
  4. Ghobadi, Kimia; Mahmoudzadeh, Houra: Inferring linear feasible regions using inverse optimization (2021)
  5. Ploskas, Nikolaos; Sahinidis, Nikolaos V.; Samaras, Nikolaos: A triangulation and fill-reducing initialization procedure for the simplex algorithm (2021)
  6. Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee, Timo Kuosmanen: pyStoNED: A Python Package for Convex Regression and Frontier Estimation (2021) arXiv
  7. Xie, Jianhui; Xie, Qiwei; Li, Yongjun; Liang, Liang: Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique (2021)
  8. Bosch, P.; Contreras, J. P.; Munizaga-Rosas, J.: Feasibility and cost minimisation for a lithium extraction problem (2020)
  9. Leyffer, Sven; Vanaret, Charlie: An augmented Lagrangian filter method (2020)
  10. Qiu, Songqiang; Chen, Zhongwen: An adaptively regularized sequential quadratic programming method for equality constrained optimization (2020)
  11. Gelashvili, K.: Add-in for solvers of unconstrained minimization to eliminate lower bounds of variables by transformation (2019)
  12. Amaya Moreno, Liana; Fügenschuh, Armin; Kaier, Anton; Schlobach, Swen: A nonlinear model for vertical free-flight trajectory planning (2018)
  13. Khajavirad, Aida; Sahinidis, Nikolaos V.: A hybrid LP/NLP paradigm for global optimization relaxations (2018)
  14. Kılınç, Mustafa R.; Sahinidis, Nikolaos V.: Exploiting integrality in the global optimization of mixed-integer nonlinear programming problems with BARON (2018)
  15. Wang, I-Lin: Multicommodity network flows: A survey. II: Solution methods (2018)
  16. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  17. Andrei, Neculai: Continuous nonlinear optimization for engineering applications in GAMS technology (2017)
  18. Sioshansi, Ramteen; Conejo, Antonio J.: Optimization in engineering. Models and algorithms (2017)
  19. Tran, Thi Thuy; Le Thi, Hoai An; Pham Dinh, Tao: DC programming and DCA for enhancing physical layer security via cooperative jamming (2017)
  20. Vinkó, Tamás; Gelle, Kitti: Basin hopping networks of continuous global optimization problems (2017)

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