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 432 articles )

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  1. Janka, Dennis: Sequential quadratic programming with indefinite Hessian approximations for nonlinear optimum experimental design for parameter estimation in differential-algebraic equations (2015)
  2. Omer, Jérémy; Soumis, François: A linear programming decomposition focusing on the span of the nondegenerate columns (2015)
  3. Birgin, E. G.; Martínez, J. M.; Prudente, L. F.: Augmented Lagrangians with possible infeasibility and finite termination for global nonlinear programming (2014)
  4. Domes, Ferenc; Fuchs, Martin; Schichl, Hermann; Neumaier, Arnold: The optimization test environment (2014)
  5. Fernandes, Luís M.; Júdice, Joaquim J.; Fukushima, Masao; Iusem, Alfredo: On the symmetric quadratic eigenvalue complementarity problem (2014)
  6. Fernandes, Luís M.; Júdice, Joaquim J.; Sherali, Hanif D.; Forjaz, Maria A.: On an enumerative algorithm for solving eigenvalue complementarity problems (2014)
  7. Kadziński, Miłosz; Corrente, Salvatore; Greco, Salvatore; Słowiński, Roman: Preferential reducts and constructs in robust multiple criteria ranking and sorting (2014)
  8. Kleniati, Polyxeni-M.; Adjiman, Claire S.: Branch-and-sandwich: a deterministic global optimization algorithm for optimistic bilevel programming problems. Part II: Convergence analysis and numerical results (2014)
  9. Locatelli, Marco; Maischberger, Mirko; Schoen, Fabio: Differential evolution methods based on local searches (2014)
  10. Pan, Ping-Qi: Linear programming computation (2014)
  11. Zhu, Zhichuan; Yu, Bo; Yang, Li: Globally convergent homotopy method for designing piecewise linear deterministic contractual function (2014)
  12. Zorn, Keith; Sahinidis, Nikolaos V.: Global optimization of general non-convex problems with intermediate bilinear substructures (2014)
  13. Fasen, Vicky; Fuchs, Florian: Spectral estimates for high-frequency sampled continuous-time autoregressive moving average processes (2013)
  14. Jamelot, Erell; Ciarlet, Patrick: Fast non-overlapping Schwarz domain decomposition methods for solving the neutron diffusion equation (2013)
  15. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)
  16. Li, Wei: Dual-primal algorithm for linear optimization (2013)
  17. Palma-Benhke, Rodrigo; Philpott, Andy; Jofré, Alejandro; Cortés-Carmona, Marcelo: Modelling network constrained economic dispatch problems (2013)
  18. Van Dinter, Jennifer; Rebennack, Steffen; Kallrath, Josef; Denholm, Paul; Newman, Alexandra: The unit commitment model with concave emissions costs: a hybrid Benders’ decomposition with nonconvex master problems (2013)
  19. Bentobache, Mohand; Bibi, Mohand Ouamer: A two-phase support method for solving linear programs: numerical experiments (2012)
  20. Bonami, Pierre; Kilinç, Mustafa; Linderoth, Jeff: Algorithms and software for convex mixed integer nonlinear programs (2012)

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