A new method for nonlinearly constrained optimization

AIChE Journal ◽  
1975 ◽  
Vol 21 (3) ◽  
pp. 479-486 ◽  
Author(s):  
J. S. Newell ◽  
D. M. Himmelblau
Keyword(s):  
Constrained Optimization ◽  
New Method ◽  
Nonlinearly Constrained Optimization

QP-Based Methods for Large-Scale Nonlinearly Constrained Optimization.

1981 ◽  
Author(s):  
Philip E. Gill ◽  
Walter Murray ◽  
Michael A. Saunders ◽  
Margaret H. Wright
Keyword(s):  
Constrained Optimization ◽  
Large Scale ◽  
Nonlinearly Constrained Optimization

Sequential Quadratic Programming

Acta Numerica ◽  
1995 ◽  
Vol 4 ◽  
pp. 1-51 ◽  
Author(s):  
Paul T. Boggs ◽  
Jon W. Tolle
Keyword(s):  
Quadratic Programming ◽  
Constrained Optimization ◽  
Sequential Quadratic Programming ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Public Domain ◽  
Large Set ◽  
Constrained Optimization Problems ◽  
Nonlinearly Constrained Optimization

Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation, both commercial and public-domain SQP algorithms have been developed and used to solve a remarkably large set of important practical problems. Recently large-scale versions have been devised and tested with promising results.

scholarly journals On a New Optimization Method With Constraints

2020 ◽  
Vol 12 (5) ◽  
pp. 27
Author(s):  
Bouchta x RHANIZAR
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Convex Set ◽  
Optimization Method ◽  
New Method ◽  
Constrained Optimization Problem ◽  
Numerical Examples ◽  
Unconstrained Problem ◽  
Closed Convex Set

We consider the constrained optimization problem  defined by: $$f(x^*) = \min_{x \in  X} f(x) \eqno (1)$$ where the function  $f$ : $ \pmb{\mathbb{R}}^{n} \longrightarrow \pmb{\mathbb{R}}$ is convex  on a closed convex set X. In this work, we will give a new method to solve problem (1) without bringing it back to an unconstrained problem. We study the convergence of this new method and give numerical examples.

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