NLPMMX - Nonlinear Min-Max Optimization

Purpose:

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.

 

Numerical Method:

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.

Program Organization:
          NLPMMX is a double precision FORTRAN subroutine and parameters are passed through arguments.

Special Features:

1. reverse communication
2. nonlinear constraints
3. bounds and linear constraints remain satisfied
4. FORTRAN source code (close to F77, conversion to C by f2c possible)
   

Reference:

	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)

Availability:

For more details contact the author or click here for free license for members and students of academic institutions.