Nonlinear Min-Max Optimization
Version 1.4 (2010)
NLPMMX solves constrained min-max problems, i.e., nonlinear programs, where the objective function is the maximum of function values. In addition there may be any set of equality or inequality constraints. It is assumed that all individual problem functions are continuously differentiable.
By introducing one additional variable and additional constraints, the problem is transformed into a general smooth nonlinear programming problem which is then solved by the sequential quadratic programming (SQP) code NLPQLP.
- reverse communication
- nonlinear constraints
- bounds and linear constraints remain satisfied
- FORTRAN source code (close to F77, conversion to C by f2c possible)
- K. Schittkowski, NLPMMX: A Fortran implementation of an SQP algorithm for min-max optimization, Report, Department of Computer Science, University of Bayreuth (2008)
- K. Schittkowski, DFNLP: A Fortran implementation of an SQP-Gauss-Newton algorithm, Report, Department of Computer Science, University of Bayreuth (2005)