Nonlinear Maximum Norm Data Fitting
Version 2.6 (2013)
NLPINF solves constrained nonlinear data fitting problems in the maximum norm, i.e., nonlinear programs, where the objective function is the maximum of absolute 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. The code is particularly useful for solving nonlinear approximation problems with a large number of support values.
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)
NLPINF is part of the interactive data fitting system EASY-FIT which contains now 1,300 test examples.
- K. Schittkowski, NLPINF: A Fortran implementation of an SQP algorithm for maximum-norm optimization problems - User's guide, Report, Department of Computer Science, University of Bayreuth (2008)
- K. Schittkowski, An active set strategy for solving optimization problems with up to 60,000,000 nonlinear constraints, submitted for publication
- K. Schittkowski, DFNLP: A Fortran implementation of an SQP-Gauss-Newton algorithm, Report, Department of Computer Science, University of Bayreuth (2005)
- K. Schittkowski (2002): EASY-FIT: A software system for data fitting in dynamic systems, Structural and Multidisciplinary Optimization, Vol. 23, No. 2, 153-169
- K. Schittkowski (2002): Numerical Data Fitting in Dynamical Systems - A Practical Introduction with Applications and Software, Kluwer Academic Publishers