Quadratic cost flow and the conjugate gradient method [An article from: European Journal of Operational Research]
Book Details
Author(s)J. Sun, X. Yang, X. Chen
PublisherElsevier
ISBN / ASINB000RR658C
ISBN-13978B000RR6585
MarketplaceFrance 🇫🇷
Description
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
By introducing quadratic penalty terms, a convex non-separable quadratic network program can be reduced to an unconstrained optimization problem whose objective function is a piecewise quadratic and continuously differentiable function. A conjugate gradient method is applied to the reduced problem and its convergence is proved. The computation exploits the special network data structures originated from the network simplex method. This algorithmic framework allows direct extension to multicommodity cost flows. Some preliminary computational results are presented.
Description:
By introducing quadratic penalty terms, a convex non-separable quadratic network program can be reduced to an unconstrained optimization problem whose objective function is a piecewise quadratic and continuously differentiable function. A conjugate gradient method is applied to the reduced problem and its convergence is proved. The computation exploits the special network data structures originated from the network simplex method. This algorithmic framework allows direct extension to multicommodity cost flows. Some preliminary computational results are presented.
