An efficient Sequential Linear Programming algorithm for engineering optimization

2005 ◽  
Vol 16 (3) ◽  
pp. 353-371 ◽  
Author(s):  
L. Lamberti ◽  
C. Pappalettere
Keyword(s):  
Linear Programming ◽  
Programming Algorithm ◽  
Engineering Optimization ◽  
Sequential Linear Programming ◽  
Linear Programming Algorithm

Tuning a Hybrid Optimization Algorithm by Determining the Modality of the Design Space

2001 ◽  
Author(s):  
Kurt Hacker ◽  
John Eddy ◽  
Kemper Lewis
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Optimization Algorithm ◽  
Design Space ◽  
Hybrid Optimization ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Hybrid Optimization Algorithm ◽  
Linear Programming Algorithm ◽  
Algorithm Benchmarking

Abstract In this paper we present an approach for increasing the efficiency of a hybrid Genetic/Sequential Linear Programming algorithm. We introduce two metrics for evaluating the modality of the design space and then use this information to efficiently switch between the Genetic Algorithm and SLP algorithm. The motivation for this study is an effort to reduce the computational expense associated with the use of a Genetic Algorithm by reducing the number of function evaluations needed to find good solutions. In the paper the two metrics used to evaluate the modality of the design space are the variance in fitness of the population of the designs in the Genetic Algorithm and the error associated with fitting a response surface to the designs evaluates by the Genetic Algorithm. The effectiveness of this approach is demonstrated by considering a highly multimodal Genetic Algorithm benchmarking problem.

GE Asset Management, Genworth Financial, and GE Insurance Use a Sequential-Linear-Programming Algorithm to Optimize Portfolios

2005 ◽  
Vol 35 (5) ◽  
pp. 370-380 ◽  
Author(s):  
Kete Charles Chalermkraivuth ◽  
Srinivas Bollapragada ◽  
Michael C. Clark ◽  
John Deaton ◽  
Lynn Kiaer ◽  
...  
Keyword(s):  
Linear Programming ◽  
Asset Management ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Linear Programming Algorithm

scholarly journals Strong uniqueness in sequential linear programming

1990 ◽  
Vol 31 (4) ◽  
pp. 379-384 ◽  
Author(s):  
M. R. Osborne ◽  
R. S. Womersley
Keyword(s):  
Linear Programming ◽  
Convergence Result ◽  
Second Order ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Strong Uniqueness ◽  
Order Convergence ◽  
Linear Programming Algorithm ◽  
Second Order Convergence ◽  
Special Case

AbstractIt is known that strong uniqueness can be used to prove second order convergence of the generalised Gauss-Newton algorithm. Formally this algorithm includes sequential linear programming as a special case. Here we show that the second order convergence result extends when the sequential linear programming algorithm is formulated appropriately. Also this discussion provides an example which shows that the assumption of Lipschitz continuity is necessary for the second order convergence result based on strong uniqueness.

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