Generalized Programming by Linear Approximation of the Dual Gradient: Convex Programming Case
AbstractA modified version of Generalized Programming is presented for solving convex programming problems. The procedure uses convenient linear approximations of the gradient of the dual in order to approximate the Kuhn-Tucker conditions for the dual. Solution points of these approximate Kuhn-Tucker conditions are then used for column generation. Computational results are reported.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 23 (1977)
Issue (Month): 12 (August)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).
If references are entirely missing, you can add them using this form.