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)

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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)
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  5. Brás, Carmo; Eichfelder, Gabriele; Júdice, Joaquim: Copositivity tests based on the linear complementarity problem (2016)
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  20. Locatelli, Marco; Maischberger, Mirko; Schoen, Fabio: Differential evolution methods based on local searches (2014)

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