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 415 articles )

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  1. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  2. Wan, Wei; Biegler, Lorenz T.: Structured regularization for barrier NLP solvers (2017)
  3. Zhu, Zhichuan; Yu, Bo: Globally convergent homotopy algorithm for solving the KKT systems to the principal-agent bilevel programming (2017)
  4. 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)
  5. Brás, Carmo; Eichfelder, Gabriele; Júdice, Joaquim: Copositivity tests based on the linear complementarity problem (2016)
  6. Qiu, Songqiang; Chen, Zhongwen: A globally convergent penalty-free method for optimization with equality constraints and simple bounds (2016)
  7. Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.: High detail stationary optimization models for gas networks: validation and results (2016)
  8. Bao, Xiaowei; Khajavirad, Aida; Sahinidis, Nikolaos V.; Tawarmalani, Mohit: Global optimization of nonconvex problems with multilinear intermediates (2015)
  9. Cai, Yongyang; Judd, Kenneth L.: Dynamic programming with Hermite approximation (2015)
  10. Goldberg, Noam; Leyffer, Sven: An active-set method for second-order conic-constrained quadratic programming (2015)
  11. Izmailov, A.F.; Solodov, M.V.: Newton-type methods: a broader view (2015)
  12. Izmailov, A.F.; Solodov, M.V.: Critical Lagrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it (2015)
  13. Janka, Dennis: Sequential quadratic programming with indefinite Hessian approximations for nonlinear optimum experimental design for parameter estimation in differential-algebraic equations (2015)
  14. Omer, Jérémy; Soumis, François: A linear programming decomposition focusing on the span of the nondegenerate columns (2015)
  15. Birgin, E.G.; Martínez, J.M.; Prudente, L.F.: Augmented Lagrangians with possible infeasibility and finite termination for global nonlinear programming (2014)
  16. Domes, Ferenc; Fuchs, Martin; Schichl, Hermann; Neumaier, Arnold: The optimization test environment (2014)
  17. Fernandes, Luís M.; Júdice, Joaquim J.; Sherali, Hanif D.; Forjaz, Maria A.: On an enumerative algorithm for solving eigenvalue complementarity problems (2014)
  18. Kadziński, Miłosz; Corrente, Salvatore; Greco, Salvatore; Słowiński, Roman: Preferential reducts and constructs in robust multiple criteria ranking and sorting (2014)
  19. 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)
  20. Locatelli, Marco; Maischberger, Mirko; Schoen, Fabio: Differential evolution methods based on local searches (2014)

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