Sequential Convex Programming Methods
K. Schittkowski, C. Zillober: Stochastic Programming, K. Marti, P. Kall eds.,
Lecture Notes in Economics and Mathematical Systems, Vol. 423, Springer (1995)
Abstract:
Sequential convex programming methods became very popular in the past for
special domains of application, e.g. the optimal structural design in
mechanical engineering. The algorithm uses an inverse approximation
of certain variables so that a convex, separable nonlinear programming
problem must be solved in each iteration. In this paper the method is
outlined and it is shown, how the iteration process can be stabilized
by a line search. The convergence results are presented for a special
variant called method of moving asymptotes. The algorithm
was implemented in FORTRAN and the numerical performance is evaluated
by a comparative study, where the test problems are formulated through
a finite element analysis.
To download a preprint, click here: scp.pdf