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MINOS |
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MINOS is a solver for large-scale nonlinear optimization (NLP) developed by B. Murtaugh and M. Saunders at Macquarie University and Stanford University. MINOS solves such problems using a reduced-gradient algorithm combined with a quasi-Newton algorithm. When constraints are nonlinear, MINOS employs a projected Lagrangian algorithm. This involves a sequence of major iterations, each of which requires the solution of a linearly constrained subproblem. Each subproblem contains linearized versions of the nonlinear constraints, as well as the original linear constraints and bounds. MINOS can solve LP, RMIP, NLP, DNLP, and RMINLP model types. The solver manual is MINOS.pdf. MINOSD is an in core version.
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