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\subsection{Explanation of graph and values}  The benchmarks and their graphical representation correspond well to the expected results.  Without doing any regressional deep  analysis of the data  it is obvious \textbf{obvious}  from the graph that - with the exception of the first part of the Algo-1 results - represent instances of  the corresponding O(.) functions.  The oddities in the Algo-3 graph can be explained by the fact that  when a method hasn't hasn't  been run before, it hasn't hasn't  been Just-In-Time compiled and thus it takes a few runs before it performs with optimal speed.  For the subsequent benchmarks the method seems to have been JIT compiled  and thus performs better than for n = 50.  \begin{subequations}  When \textbf{When  n is doubled, the time develops as follows: follows:}  \begin{align}  Algo1, O(n^3): (2n)^3 & &  = 8n^3, \\ Algo2, O(n^2): (2n)^2 & &  = 4n^2, \\ Algo3, O(n^1): (2n)^1 & &  = 2n \end{align}  \end{subequations}