The computational complexity of computing the true range of d f is very high in a practical application.
Since these designed schemes have different beamforming and jamming patterns in the relaying phase, we just need to analyze the computational complexity of computing the beamforming and jamming vectors given the beamforming set (|mathcal {D}|=M) and jamming set (|mathcal {J}|=K).
It is assumed that the number of symbols B is used for each batching processing to form the data matrix Y ^ ≡ y ^ 1, y ^ 2, ⋯, y ^ B. First, the computational complexity of computing the autocorrelation matrix R y ^ = E y ^ y ^ H = 1 / B ∑ i = 1 B y ^ i y ^ i H needs M2B multiplications implementing and is thus of order O(M2B).
Thus, the computational complexity of computing doublets, which can potentially be quadratic to the total number of genes in the dataset, is not critical since only a very small subset of the genes is used.
As mentioned earlier, the complexity of computing the metric M X ̂ ( i ) depends heavily on computation of matrix P i.
These wave phenomena are well understood, but have been largely ignored in computer graphics due to the high cost and complexity of computing them at audio rates.
Since decisions taken by realistic players cannot involve unbounded resources, recent computer science literature advocated the importance of assessing the complexity of computing with solution concepts.
The computational complexity of AM2CP (Algorithm 1) depends on the time complexity of computing σ, which further depends on the time complexity of computing the influence propagation p C k ( i, j ) for community C k and all the pairs (i,j) of nodes in it.
Special attention is also devoted to the complexity of computing the measures.
We consider the complexity of computing a longest increasing subsequence (LIS) parameterised by the length of the output.
We investigate solutions for weighted argument systems and the complexity of computing such solutions, focussing in particular on weighted variations of grounded extensions.
