TY - GEN

T1 - Approximately optimal utility maximization

AU - Nedić, Angelia

AU - Subramanian, Vijay G.

PY - 2009

Y1 - 2009

N2 - All opportunistic scheduling algorithms solve simpler optimization problems at each scheduling instance in order to achieve good long-term performance. The analysis of these algorithms assumes that the simpler optimization problems are solved exactly. However, in contrast, real-life implementations only approximately solve these problems but still yield close to optimal performance. We formalize this observation by explicitly bounding the longterm performance in terms of the error in the approximation made at every stage.

AB - All opportunistic scheduling algorithms solve simpler optimization problems at each scheduling instance in order to achieve good long-term performance. The analysis of these algorithms assumes that the simpler optimization problems are solved exactly. However, in contrast, real-life implementations only approximately solve these problems but still yield close to optimal performance. We formalize this observation by explicitly bounding the longterm performance in terms of the error in the approximation made at every stage.

UR - http://www.scopus.com/inward/record.url?scp=77950671552&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77950671552&partnerID=8YFLogxK

U2 - 10.1109/ITWNIT.2009.5158572

DO - 10.1109/ITWNIT.2009.5158572

M3 - Conference contribution

AN - SCOPUS:77950671552

SN - 9781424445363

T3 - Proceedings - 2009 IEEE Information Theory Workshop on Networking and Information Theory, ITW 2009

SP - 206

EP - 210

BT - Proceedings - 2009 IEEE Information Theory Workshop on Networking and Information Theory, ITW 2009

T2 - 2009 IEEE Information Theory Workshop on Networking and Information Theory, ITW 2009

Y2 - 10 June 2009 through 12 June 2009

ER -