MINOS (optimization software)
MINOS is a
MINOS was first developed by Bruce Murtagh and
Operation
Ideally, the user should provide gradients of the nonlinear functions. (This is automatic in most of the modeling systems mentioned above.) If some or all of the gradients are not provided, MINOS will approximate the missing ones by finite differences, but this could be slow and less reliable. If the objective function is convex and the constraints are linear, the solution obtained will be a global minimizer. Otherwise, the solution obtained may be a local minimizer.
For linear programs, a two-phase primal
For problems with nonlinear constraints, a linearly constrained Lagrangian method is used.[7] This involves a sequence of major iterations, each of which solves (perhaps approximately) a linearly constrained subproblem. The subproblem objective is an augmented Lagrangian, and the subproblem constraints are linearizations of the nonlinear constraints at the current point.
MINOS is intended for large sparse problems. There is no fixed limit on problem size. Most working storage is contained in one double-precision array (which should be sufficiently large). The source code is suitable for all scientific machines with a Fortran compiler.
References
- ^ B.A. Murtagh, M.A. Saunders (2003). "MINOS 5.51 User's Guide" (PDF).
- S2CID 38638809.
- ^ Beale-Orchard-Hays Prize winners
- ^ NEOS Server
- ^ Saunders, Michael (2013). Optimization Algorithms and Software at SOL (PDF).
- ^ GAMS/MINOS Solver guide
- .
Further reading
- B.A. Murtagh, M.A. Saunders (1982). "A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints" (PDF). Mathematical Programming. 16: 84–117.
External links
- Systems Optimization Laboratory, Stanford University Systems Optimization Laboratory (SOL).
- MINOS 5.5 - Description - Distributors of the software.
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