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The development of the aforementioned method is based on the use of an extended quasi-metric space, the so-called extended dual complexity space, introduced in the aforesaid reference.
In [6], it was proved that the extended dual complexity space enjoys the following interesting properties, which played a relevant role in the aforementioned method.
Salvador and Schellekens [10] have shown that the dual complexity space can be modelled as stable partial monoids.
Finally, notice that the fixed point technique developed in [6] uses the fact that the extended dual complexity space ((mathcal{C}, e_{mathcal{C}})) is right K-sequentially complete (Theorem 4 in [6]).
Concretely, the extended dual complexity space is formed by the pair ((mathcal {C},e_{mathcal{C}})), where mathcal{C}=Biggl{ finmathcal{TC}: sum_{n=1}^{infty }2^{-n}f(n)< inftyBiggr} and (e_{mathcal{C}}) is the extended quasi-metric given by e_{mathcal{C}}(f,g)= left { textstylebegin{array}l@{quad}l} sum_{n=1}^{infty}2^{-n} (g(n -f(n -f & mbox{if }fpreceq_{mathcal{C}} g, infty& mbox{if otherwise}fpreceq_{mathcal{
Instead of using simplifications, the presented model uses a dual dynamic programming approach to reduce computational complexity.
To reduce the computation complexity in large-scale networks, a dual decomposition algorithm is presented which incorporates bandwidth, backhaul capacity, and access price.
Therefore, the level of complexity within and between tasks in a dual task activity must be considered relative to the amount of cognitive-motor interference and thus performance.
A dual decomposition technique is applied in order to reduce the computational complexity of the system.
As the size and complexity of a network increases, the probability of a dual link failure also increases.
To resolve this complexity, a dual-porosity model (Nie et al. 2012) and recently a triple-porosity model (Huang et al. 2015; Sang et al. 2016) have been introduced to simulate fractured reservoirs.
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