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Contrary to the above works approximating MWM, our approach achieves throughput optimality with low-complexity and local information exchange, by avoiding solving MWM and relying on a converged Gibbs sampler for obtaining the optimal schedule at each time slot.
Algorithm 1 computes a valid time slot assignment using at most 2φ(γ max +1) times the time slots needed by the optimal schedule, where γ max is the longest harvesting duration of vertex in G and φ is the upper bound deduced in Theorem 1.
An optimal schedule should clear all the flows within a minimum number of time slots, which implies maximum parallelism of transmissions.
An optimal schedule for combined treatment in humans has not yet been determined48.
Although we show that finding an optimal schedule is an intractable problem, we present a heuristic algorithm that can usually find an optimal or near-optimal schedule.
Use a Pomodoro timer to create an optimal schedule.
Nevertheless, the optimal schedule is uncertain.
Algorithm 1: CCEVT Function: find optimal schedule.
Note also that the TGVP performance is better for the second case, since there are more time-slots now, and the optimal scheduling algorithm is designed for more time-slots.
However, as the number of time-slots increases (LTE), we observe that the TGVP of the optimal scheduling algorithm approaches zero.
For systems that have many time-slots within the time-window, e.g., for WINNER I, the optimal scheduling algorithm also performs better than all the other well-known algorithms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com