<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">El Amir Djeffal</style></author><author><style face="normal" font="default" size="100%">Bachir Bounibane</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Kernel function based interior point algorithms for linear optimisation</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Mathematical Modelling and Numerical Optimisation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.inderscience.com/info/inarticle.php?artid=98785</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">158 - 177</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a primal-dual interior-point algorithm for linear optimisation based on a class of kernel functions which is eligible. New search directions and proximity measures are defined based on these functions. We derive the complexity bounds for large and small-update methods respectively. These are currently the best known complexity results for such methods.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record></records></xml>