NLPQLB - Nonlinear Programming with very Many Constraints

Version 2.6 (2012)
 
Purpose:
NLPQLB is an extension of the general nonlinear programming code NLPQLP with the intention to solve also problems with very many constraints, where the derivative matrix of the constraints does not possess any special sparse structures that could be exploited.
Numerical Method:
The user defines the maximum number of lines in the matrix of the linearized constraints, that can be stored in core. By investigating the constraint function values, a decision is made which restrictions are necessary to fill that matrix. The algorithm will stop, if too many constraints are violated.
Program Organization:
NLPQLB is a double precision FORTRAN subroutine where all parameters are passed through subroutine arguments. The program organization is very similar to that of NLPQLP.
Special Features:
  1. same as those for NLPQLP (scaling, reverse communication, etc.)
  2. NLPQLB executes NLPQLP in reverse communication
  3. solves academic test problems with up to 200,000,000 nonlinear constraints
  4. full documentation by initial comments
  5. FORTRAN source code (close to F77, conversion to C by f2c possible)
Applications:
NLPQLB is implemented within the structural mechanical optimization system LAGRANGE at EADS to solve large structural design optimization problems.

Reference:
K. Schittkowski, Solving nonlinear programming problems with very many constraints, Optimization, Vol. 25, 179-196 (1992) 
K. Schittkowski, NLPQLB: A Fortran implementation of an SQP algorithm with active set strategy for solving optimization problems with a very large number of nonlinear constraints - user's guide, version 2.0, Report, Department of Computer Science, University of Bayreuth
K. Schittkowski, An active set strategy for solving optimization problems with up to 200,000,000 nonlinear constraints, submitted for publication

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