# Separable convexification and DCA techniques for capacity and flow assignment problems

P. Mahey; Thai Q. Phong; H. P.L. Luna

RAIRO - Operations Research (2010)

- Volume: 35, Issue: 2, page 269-281
- ISSN: 0399-0559

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topMahey, P., Phong, Thai Q., and Luna, H. P.L.. "Separable convexification and DCA techniques for capacity and flow assignment problems." RAIRO - Operations Research 35.2 (2010): 269-281. <http://eudml.org/doc/197823>.

@article{Mahey2010,

abstract = {
We study a continuous version of the capacity and flow assignment problem
(CFA) where the design cost is combined with an average delay measure
to yield a non convex objective function coupled with multicommodity flow
constraints. A separable convexification of each arc cost function is proposed
to obtain approximate feasible solutions within easily computable gaps from
optimality. On the other hand, DC (difference of convex functions) programming can be used
to compute accurate upper bounds and reduce the gap.
The technique is shown to be effective when topology is assumed
fixed and capacity expansion on some arcs is considered.
},

author = {Mahey, P., Phong, Thai Q., Luna, H. P.L.},

journal = {RAIRO - Operations Research},

keywords = {Network design; DC optimization; capacity and flow assignment.},

language = {eng},

month = {3},

number = {2},

pages = {269-281},

publisher = {EDP Sciences},

title = {Separable convexification and DCA techniques for capacity and flow assignment problems},

url = {http://eudml.org/doc/197823},

volume = {35},

year = {2010},

}

TY - JOUR

AU - Mahey, P.

AU - Phong, Thai Q.

AU - Luna, H. P.L.

TI - Separable convexification and DCA techniques for capacity and flow assignment problems

JO - RAIRO - Operations Research

DA - 2010/3//

PB - EDP Sciences

VL - 35

IS - 2

SP - 269

EP - 281

AB -
We study a continuous version of the capacity and flow assignment problem
(CFA) where the design cost is combined with an average delay measure
to yield a non convex objective function coupled with multicommodity flow
constraints. A separable convexification of each arc cost function is proposed
to obtain approximate feasible solutions within easily computable gaps from
optimality. On the other hand, DC (difference of convex functions) programming can be used
to compute accurate upper bounds and reduce the gap.
The technique is shown to be effective when topology is assumed
fixed and capacity expansion on some arcs is considered.

LA - eng

KW - Network design; DC optimization; capacity and flow assignment.

UR - http://eudml.org/doc/197823

ER -

## References

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