TY - JOUR
T1 - A new semidefinite programming relaxation scheme for a class of quadratic matrix problems
AU - Beck, Amir
AU - Drori, Yoel
AU - Teboulle, Marc
PY - 2012/7
Y1 - 2012/7
N2 - We consider a special class of quadratic matrix optimization problems which often arise in applications. By exploiting the special structure of these problems, we derive a new semidefinite relaxation which, under mild assumptions, is proven to be tight for a larger number of constraints than could be achieved via a direct approach. We show the potential usefulness of these results when applied to robust least-squares and sphere-packing problems.
AB - We consider a special class of quadratic matrix optimization problems which often arise in applications. By exploiting the special structure of these problems, we derive a new semidefinite relaxation which, under mild assumptions, is proven to be tight for a larger number of constraints than could be achieved via a direct approach. We show the potential usefulness of these results when applied to robust least-squares and sphere-packing problems.
KW - Homogenization
KW - Nonconvex quadratic optimization
KW - Rank reduction
KW - Semidefinite relaxations
KW - Strong duality
UR - http://www.scopus.com/inward/record.url?scp=84861528841&partnerID=8YFLogxK
U2 - 10.1016/j.orl.2012.03.005
DO - 10.1016/j.orl.2012.03.005
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AN - SCOPUS:84861528841
SN - 0167-6377
VL - 40
SP - 298
EP - 302
JO - Operations Research Letters
JF - Operations Research Letters
IS - 4
ER -