The relative delay tolerance of data applications, together with bursty traffic characteristics, opens up the possibility for scheduling transmissions so as to optimize throughput. A particularly attractive approach in fading environments is to exploit the variations in the channel conditions and transmit to the user with the current "best" channel. We show that the "best" user may be identified as the maximum-rate user when feasible rates are weighted with some appropriately determined coefficients. Interpreting the coefficients as shadow prices, or reward values, the optimal strategy may thus be viewed as a revenue-based policy, which always assigns the transmission slot to the user yielding the maximum revenue. Calculating the optimal-revenue vector directly is a formidable task, requiring detailed information on the channel statistics. Instead, we present adaptive algorithms for determining the optimal-revenue vector online in an iterative fashion, without the need for explicit knowledge of the channel behavior. Starting from an arbitrary initial vector, the algorithms iteratively adjust the reward values to compensate for observed deviations from the target throughput rates. The algorithms are validated through extensive numerical experiments. Besides verifying long-run convergence, we also examine the transient performance, in particular the rate of convergence to the optimal-revenue vector. The results show that the target throughput ratios are tightly maintained and that the algorithms are well able to track sudden changes in channel conditions or throughput targets.