Step (3) Following an action selection policy, for instance e-greedy policy mentioned later, an action is selected making use of the Q-values of observed states at step (1).
Step (4) Among those sets whose received powers are equal to the pilot signal powers, UEs usually choose one set that has the lowest Q-value or rarely choose one set randomly to avoid local minima as e-greedy policy [11].
As a result, they provide much more reliable statistics in the context of comparing all different algorithmic frameworks (e.g., greedy, OLC and SBH).
There are many algorithms (e.g., greedy algorithms (Matching Pursuit (MP [ 15]), OMP [ 16] and Homotopy [ 17]) to solve the sparse models.
While it can be solved with reasonable effort at the beginning of the pricing process (e.g., by greedy heuristics) it gets increasingly hard to solve in the long run.
Such tasks are computationally difficult, so a number of search heuristics have been proposed: for example, PathBLAST uses randomized dynamic programming to search for conserved pathways across networks, while NetworkBLAST-E implements a greedy heuristic to search for conserved protein complexes.
Assuming an approximation of a throughput, we can find the following equation for the choice of e g for the greedy player to minimize its utility function based on full knowledge of the game played by the vigilante player: frac{mathrm{d} u_{{g}}}{mathrm{d} e_{{g}}}=0 rightarrow e_{{g}}~=~e_{{v}} frac{N}{N~-~1}, (7).
When p equals 0 [ 15, 16], e.g., L0 norm, the greedy algorithms (e.g., MP, OMP) will be employed to solve the problem of Eq. (2).
To derive these results, we considered for each query the solutions having the lowest and the highest overall packet processing costs among the total solution set (e.g., for A-greedy we get seven different solutions for each query, one for a different profile window), respectively.
To study the effect of e v, we assumed a greedy player with e g = 1.2 for updating its transmission probability (Eq. 1) in a cell of N nodes from 5 to 45.
Therefore, conventional greedy coloring algorithms (e.g., Welsh-Powell algorithm [11]) can be easily adopted to determine the resource unit vector for a given MBS Zone topology.
