Quadratically convergent techniques in linearly constrained optimization

Quadratically convergent techniques in linearly constrained optimization

Methods for the solution of linearly constrained optimization of a nonlinear objective function are presented and compared. The methods of Fletcher‐Reeves, Fletcher, and Powell are used to generate search directions in the decision variable space. Generalized Kuhn‐Tucker conditions are presented and...

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Veröffentlicht in:AIChE journal 1974-05, Vol.20 (3), p.555-563
Hauptverfasser: Husen, Gregory J., Eakman, James M.
Format: Artikel
Sprache:eng
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Zusammenfassung:Methods for the solution of linearly constrained optimization of a nonlinear objective function are presented and compared. The methods of Fletcher‐Reeves, Fletcher, and Powell are used to generate search directions in the decision variable space. Generalized Kuhn‐Tucker conditions are presented and used to check for a local minimum.
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.690200317