## Abstract A numerical method for solving nonβlinear optimal control problems with inequality constraints is presented in this paper. The method is based upon Legendre wavelet approximations. The properties of Legendre wavelets are first presented. The operational matrix of integration and the Gau
FEASIBLE DESCENT CONE METHODS FOR INEQUALITY CONSTRAINED OPTIMIZATION PROBLEMS
β Scribed by J. A. SNYMAN; N. STANDER
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 784 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0029-5981
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β¦ Synopsis
A new Feasible Descent Cone (FDC) method for constrained optimization, previously restricted to linear objectives, is here generalized to include non-linear objective functions as well. In the basic and exact algorithm a sequence of descent steps is taken through the interior of the feasible region along the central lines of mathematically defined descent cones, constructed at successive boundary points. Here the basic algorithm is modified to allow for a minimum to occur within the interior, along a central descent ray in the case of non-linear objectives. A special interior procedure, with desirable mathematical properties, is adopted should the latter occur. To ensure economic implementation, the new generalized and exact algorithm, referred to as SSOPT2, is successively applied to a sequence of approximate quadratic subproblems. The overall generalized procedure that includes the successive application of SSOPT2 to the approximate subproblems, is referred to as the successive approximation version 2 algorithm (SAM2). The practical performance of SAM2 is assessed through its application to a number of small but otherwise representative test problems.
π SIMILAR VOLUMES
## Abstract This paper is concerned with the numerical solution of a symmetric indefinite system which is a generalization of the KarushβKuhnβTucker system. Following the recent approach of LukΕ‘an and VlΔek, we propose to solve this system by a preconditioned conjugate gradient (PCG) algorithm and
A new implementation of the conjugate gradient method is presented that economically overcomes the problem of severe numerical noise superimposed on an otherwise smooth underlying objective function of a constrained optimization problem. This is done by the use of a novel gradient-only line search t