Artículos de revistas
A relaxed constant positive linear dependence constraint qualification and applications
Fecha
2012Registro en:
MATHEMATICAL PROGRAMMING, NEW YORK, v. 135, n. 41306, supl., Part 3, pp. 255-273, OCT, 2012
0025-5610
10.1007/s10107-011-0456-0
Autor
Andreani, Roberto
Haeser, Gabriel
Laura Schuverdt, Maria
Silva, Paulo J. S.
Institución
Resumen
In this work we introduce a relaxed version of the constant positive linear dependence constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent generalization of the constant rank constraint qualification by Minchenko and Stakhovski that was called RCRCQ. We show that RCPLD is enough to ensure the convergence of an augmented Lagrangian algorithm and that it asserts the validity of an error bound. We also provide proofs and counter-examples that show the relations of RCRCQ and RCPLD with other known constraint qualifications. In particular, RCPLD is strictly weaker than CPLD and RCRCQ, while still stronger than Abadie's constraint qualification. We also verify that the second order necessary optimality condition holds under RCRCQ.