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It can easily be proved that the condition of the theorem leads to (beta I le (I - sum_{i = 1}^{m} A_{i}^A_{i} )^{frac{1}{r}}).
Then (mathop{min} _{1 le i le n} r_{i} lerho(A) lemathop{max} _{1 le i le n} r_{i}).
Then for any (1 le i le j le ), (M_{j} subset M_i).
S i : the ith sensor, (1 le i le N, S_{i} in mathcal {N}).
For each (1 le i le m), let (C_i=C_{i,1} sqcap cdots sqcap C_{i,n}).
Assume that each color (c_i) is associated with cost 1, where (1 le i le g).
(eta _{1 le i le 4}) are negative and (eta _5) is positive, as expected.
All (sigma _i) for all clusters (C_i) ((1 le i le K)) follow Pareto distribution ((alpha)).
Then the elements (x_iGamma _2), (1 le i le m), generate the Lie ring (L(Gamma )).
(Vert {{e^{tA}}} Vert le M{e^{ - gamma t}} ) for some constant M and (gamma> 0 ); (sup _{1 le i le N} vert {{alpha _{i}}} vert < infty), (sup _{1 le i le N} vert {{beta_{i}}} vert < infty).
The time series Y is sampled in time steps t(i), (1 le i le n).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com