## NLPQLG: A Fortran Implementation
of a Sequential Quadratic Programming Algorithm for Heuristic
Global Optimization - User's Guide,
Version 3.1

K. Schittkowski, Report, Department of Computer Science, University of Bayreuth
(2008)

**Abstract:**
Usually, global optimization codes with guaranteed convergence require a
large number of function evaluations. On the other hand, there are efficient
optimization methods which exploit gradient information, but only the
approximation of a local minimum can be expected. If, however, the underlying
application model is expensive, if there are additional constraints, especially
equality constraints, and if the existence of different local solutions is
expected, then heuristic rules for successive switches from one local minimum
to another are often the only applicable approach. For this specific situation,
we present some simple ideas for cutting off a local minimum and to restart a
new local optimization run. However, some safeguards are needed to stabilize the
algorithm, since very little is usually known about the distribution of local
minima. The paper introduces an approach where the nonlinear programs generated
can be solved by any available *black box* software. For our
implementation, a sequential quadratic programming code (NLPQLP)
is chosen for local optimization. The usage of the code is outlined and we
present some numerical results based on a set of 58 test examples found in the
literature.
To download the report, click here: NLPQLG.pdf