liu.seSearch for publications in DiVA

References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt152",{id:"formSmash:upper:j_idt152",widgetVar:"widget_formSmash_upper_j_idt152",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt153_j_idt155",{id:"formSmash:upper:j_idt153:j_idt155",widgetVar:"widget_formSmash_upper_j_idt153_j_idt155",target:"formSmash:upper:j_idt153:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

Convex multicommodity flow problems: a bidual approachPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
function selectAll()
{
var panelSome = $(PrimeFaces.escapeClientId("formSmash:some"));
var panelAll = $(PrimeFaces.escapeClientId("formSmash:all"));
panelAll.toggle();
toggleList(panelSome.get(0).childNodes, panelAll);
toggleList(panelAll.get(0).childNodes, panelAll);
}
/*Toggling the list of authorPanel nodes according to the toggling of the closeable second panel */
function toggleList(childList, panel)
{
var panelWasOpen = (panel.get(0).style.display == 'none');
// console.log('panel was open ' + panelWasOpen);
for (var c = 0; c < childList.length; c++) {
if (childList[c].classList.contains('authorPanel')) {
clickNode(panelWasOpen, childList[c]);
}
}
}
/*nodes have styleClass ui-corner-top if they are expanded and ui-corner-all if they are collapsed */
function clickNode(collapse, child)
{
if (collapse && child.classList.contains('ui-corner-top')) {
// console.log('collapse');
child.click();
}
if (!collapse && child.classList.contains('ui-corner-all')) {
// console.log('expand');
child.click();
}
}
PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2005 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Linköping: Linköpings universitet , 2005. , 12 p.
##### Series

Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 954
##### National Category

Mathematics
##### Identifiers

URN: urn:nbn:se:liu:diva-28478Local ID: 13625ISBN: 91-8529-963-4OAI: oai:DiVA.org:liu-28478DiVA: diva2:249288
##### Public defence

2005-06-02, BL32, B-huset, Campus Valla, Linköpings universitet, Linköping, 10:00 (English)
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt400",{id:"formSmash:j_idt400",widgetVar:"widget_formSmash_j_idt400",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt406",{id:"formSmash:j_idt406",widgetVar:"widget_formSmash_j_idt406",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt413",{id:"formSmash:j_idt413",widgetVar:"widget_formSmash_j_idt413",multiple:true});
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-11-23Bibliographically approved
##### List of papers

The topic of this dissertation, within the subfield of mathematics known as optimization, is the development of a new dual ascent method for convex multicommodity flow problems. Convex multicommodity flow problems arize in many different routing problems such as the design of packet switched computer networks and the computation of traffic network equilibria. The dual problem of a strictly convex twice differentiable convex multicommodity flow problem is an essentially unconstrained maximization problem with a piecewise twice differentiable concave objective. The idea behind this new dual ascent algorithm is to compute Newton-like ascent directions in the current differentiable piece and performing line searches in those directions combined with an active set type treatment of the borders between the differentiable pieces. The first contribution in this dissertation is a detailed investigation of this special structure. The insights gained are then used to explain the proposed algorithm. The algorithm is also tested numerically on well known traffic equilibrium problem instances. The computational results are very promising. The second contribution is a new approach for verifying feasibility in multicommodity flow problems. These feasibility problems arizes naturally in the new dual ascent algorithm proposed. The background of the problem is that if a certain representation of a solution to the dual convex multicommodity flow problem is proved to be feasible for the convex muticommodity flow problem aswell, an optimal solution is found. Hence, it is natural to seek a method for verifying feasibility of a given candidate solution for the multicommodity flow problem. The core of the approach is a distance minimizing method for verifying feasibility and hence for demonstrating optimality. This method is described in detail and some computational results in a traffic assignment setting is also given. Finally, a short note illustrating that many published test problems for traffic assignment algorithms have peculiarities are given. First and foremost, several of them have incorrectly specified attributes, such as the number of nodes. In other testproblems, the network contains subnetworks with constant travel times; subnetworks which to a large extent can be reduced or eliminated. In further test problems, the constant travel time subnetworks imply that the solution has nonunique arc flows.

1. A dual algorithm for the convex multicommodity flow problem$(function(){PrimeFaces.cw("OverlayPanel","overlay571680",{id:"formSmash:j_idt449:0:j_idt453",widgetVar:"overlay571680",target:"formSmash:j_idt449:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Verified feasibility of structured multicommodity flow solutions$(function(){PrimeFaces.cw("OverlayPanel","overlay571688",{id:"formSmash:j_idt449:1:j_idt453",widgetVar:"overlay571688",target:"formSmash:j_idt449:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. A comment on some test networks for the traffic assignment problem$(function(){PrimeFaces.cw("OverlayPanel","overlay571692",{id:"formSmash:j_idt449:2:j_idt453",widgetVar:"overlay571692",target:"formSmash:j_idt449:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1144",{id:"formSmash:lower:j_idt1144",widgetVar:"widget_formSmash_lower_j_idt1144",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1145_j_idt1147",{id:"formSmash:lower:j_idt1145:j_idt1147",widgetVar:"widget_formSmash_lower_j_idt1145_j_idt1147",target:"formSmash:lower:j_idt1145:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});