A SELF-SCALING IMPLICIT SQP METHOD FOR LARGE-SCALE STRUCTURAL OPTIMIZATION

The basic idea bf an implicit sequential quadratic programming (ISQP) method for constrained problems is to use the approximate Hessian of t he Lagrangian without explicitly calculating and storing it. This over comes one of the major drawbacks of the traditional SQP method where a large matrix needs to be calculated and stored. This concept of an im plicit method is explained and an algorithm based on it is presented. The proposed method extends a similar algorithm for unconstrained prob lems where a two-loop recursion formula is used for the inverse Hessia n matrix. The present paper develops a similar algorithm for not only the constrained problem but also the direct Hessian updates. Several s caling procedures for the Hessian are also presented and evaluated. Th e basic method and some of its variations are evaluated using a set of mathematical programming test problems, and a set of structural desig n test problems-small to larger scale. The ISQP method performs much b etter than a method that does not use any approximate Hessian matrix. Its performance is better than the full SQP method for larger scale pr oblems. The test results also show that an appropriate scaling of the Hessian can improve both efficiency and reliability substantially.