Recursive quadratic programming methods have become popular in the field of mathematical programming owing to their excellent convergence characteristics. There are two recursive quadratic programming methods that have been published in the literature. One is by Han and the other is by Pshenichny, published in 1977 and 1970, respectively. The algorithm of Pshenichny had been undiscovered until now, and is examined here for the first time. It is found that the proof of global convergence by Han requires computing sensitivity coefficients (derivatives) of all constraint functions of the problem at every iteration. This is prohibitively expensive for large‐scale applications in optimal design. In contrast, Pshenichny has proved global convergence of his algorithm using only an active‐set strategy. This is clearly preferable for large‐scale applications. The method of Pshenichny has been coded into a FORTRAN program. Applications of this method to four example problems are presented. The method is found to be very reliable. However, the method is found to be very sensitive to local minima, i.e. it converges to a local minimum nearest to the starting design. Thus, for optimal design problems (which usually possess multiple local minima) it is suggested that Pshenichny's method be used as part of a hybrid method.
|Original language||English (US)|
|Number of pages||14|
|Journal||International Journal for Numerical Methods in Engineering|
|State||Published - May 1984|
All Science Journal Classification (ASJC) codes
- Numerical Analysis
- Applied Mathematics