NLPQLY - Easy-To-Use Nonlinear Programming

 

Purpose:

NLPQLY is an easy-to-use version of the SQP code NLPQLP for solving constrained nonlinear optimization problems.  

 

Numerical Method:

The Fortran subroutine NLPQLY simplifies the numerical solution of nonlinear programming problems by calling the standard SQP code NLPQLP. The calling sequence is simplified as much as possible. A user has to provide objective and constraint function values in the same code which calls NLPQLY (reverse communication). Derivatives are internally approximated by forward differences.

 

Program Organization:

NLPQLY is written in double precision FORTRAN and organized in form of a subroutine. Nonlinear problem functions must be provided by the user within the calling program by reverse communication. NLPQLY calls NLPQLP after setting some default tolerances.

 

Special Features:

1. upper and lower bounds on the variables handled separately
2. reverse communication
3. bounds and linear constraints remain satisfied
4. robust and efficient implementation
5. Fortran source code (close to F77, conversion to C by f2c possible)
6. Extensive set of test problems
    

Applications:

Basically, NLPQLY has the same domain of applications as NLPQLP, as long as forward differences are applied for approximating derivatives and the standard settings of internal tolerances are feasible.

 

Reference:

	K. Schittkowski, NLPQLY: An easy-to-use Fortran implementation of a sequential quadratic programming algorithm - User's guide, Report, Department of Computer Science, University of Bayreuth (2009)
	Yu-hong Dai, K. Schittkowski (2008):  A sequential quadratic programming algorithm with non-monotone line search, Pacific Journal of Optimization, Vol. 4, 335-351
	K. Schittkowski, NLPQLP: A Fortran implementation of a sequential quadratic programming algorithm with distributed and non-monotone line Search - User's guide, Version 2.2, Report, Department of Computer Science, University of Bayreuth (2006)
	K. Schittkowski, NLPQL: A Fortran subroutine for solving constrained nonlinear programming problems, Annals of Operations Research, Vol. 5, 485-500 (1985/86)

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