Approximately optimal utility maximization

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.