The Linear Convergence of a Successive Linear Programming Algorithm

dc.contributor.author	Zavriev, Sergei K.
dc.contributor.author	Ferris, Michael C.
dc.date.accessioned	2013-06-06T17:35:57Z
dc.date.available	2013-06-06T17:35:57Z
dc.date.issued	1996-12-03
dc.identifier.citation	96-12	en
dc.identifier.uri	http://digital.library.wisc.edu/1793/65796
dc.description.abstract	We present a successive linear programming algorithm for solving constrained nonlinear optimization problems. The algorithm employs an Armijo procedure for updating a trust region radius. We prove the linear convergence of the method by relating the solutions of our subproblems to standard trust region and gradient projection subproblems and adapting an error bound analysis due to Luo and Tseng. Computational results are provided for polyhedrally constrained nonlinear programs.	en
dc.title	The Linear Convergence of a Successive Linear Programming Algorithm	en
dc.type	Technical Report	en

Math Prog Technical Reports
Math Prog Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison