Title
On the behaviour of constrained optimization methods when Lagrange multipliers do not exist
Abstract
Sequential optimality conditions are related to stopping criteria for nonlinear programming algorithms. Local minimizers of continuous optimization problems satisfy these conditions without constraint qualifications. It is interesting to discover whether well-known optimization algorithms generate primal–dual sequences that allow one to detect that a sequential optimality condition holds. When this is the case, the algorithm stops with a ‘correct’ diagnostic of success ‘convergence’. Otherwise, closeness to a minimizer is not detected and the algorithm ignores that a satisfactory solution has been found. In this paper it will be shown that a straightforward version of the Newton–Lagrange sequential quadratic programming method fails to generate iterates for which a sequential optimality condition is satisfied. On the other hand, a Newtonian penalty–barrier Lagrangian method guarantees that the appropriate stopping criterion eventually holds.
Year
DOI
Venue
2014
10.1080/10556788.2013.841692
Optimization Methods and Software
Keywords
Field
DocType
dual sequence,constraint qualification,continuous optimization problem,barrier lagrangian method guarantee,sequential optimality condition,newtonian penalty,well-known optimization algorithm,nonlinear programming algorithm,optimization method,lagrange sequential quadratic programming,algorithm stop,lagrange multiplier,constrained optimization
Continuous optimization,Convergence (routing),Mathematical optimization,Lagrange multiplier,Closeness,Nonlinear programming,Sequential quadratic programming,Iterated function,Mathematics,Constrained optimization
Journal
Volume
Issue
ISSN
29
3
1055-6788
Citations 
PageRank 
References 
2
0.45
28
Authors
4
Name
Order
Citations
PageRank
R. Andreani143425.10
J. M. Martínez253135.28
L. T. Santos3151.64
B. F. Svaiter460872.74