Sequential equality-constrained optimization for nonlinear programming

A novel idea is proposed for solving optimization problems with equality constraints and bounds on the variables. In the spirit of sequential quadratic programming and sequential linearly-constrained programming, the new proposed approach approximately solves, at each iteration, an equality-constrai...

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Bibliographic Details
Published in:Computational optimization and applications Vol. 65; no. 3; pp. 699 - 721
Main Authors: Birgin, E. G., Bueno, L. F., Martínez, J. M.
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2016
Springer Nature B.V
Subjects:
Algorithms
Applied mathematics
Computation
Convergence
Convex and Discrete Geometry
Experiments
Iterative methods
Linear programming
Management Science
Mathematical models
Mathematics
Mathematics and Statistics
Methods
Nonlinear programming
Operations Research
Operations Research/Decision Theory
Optimization
Optimization algorithms
Programming
Quadratic programming
Statistics
Studies
Sequential equality-constrained optimization
Nonlinear programming
Numerical experiments
Augmented Lagrangian
ISSN:0926-6003, 1573-2894
Online Access:Get full text
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Summary:A novel idea is proposed for solving optimization problems with equality constraints and bounds on the variables. In the spirit of sequential quadratic programming and sequential linearly-constrained programming, the new proposed approach approximately solves, at each iteration, an equality-constrained optimization problem. The bound constraints are handled in outer iterations by means of an augmented Lagrangian scheme. Global convergence of the method follows from well-established nonlinear programming theories. Numerical experiments are presented.
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ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-016-9849-6