As in the previous application example, we compute the optimal power allocation using the bisection method (relative error below 10−5).
The optimal power allocation is computed using the bisection method (relative error below 10−5).
Using the bisection method described in Remark 2.3, we can get a better approximation for such a b.
Thus, we can use the bisection method to find the optimal global multiplier μ [19].
We use the bisection method to solve this constrained optimization problem.
Here, we used the bisection method (Burden and Faires 1985), but any other simple root-finding algorithm might be used.
The results show that the proposed method gives parameter identification with good quality for short accelerations using the ARMA model and the bisection method for the SDOF system.
For the ML analyses, full heuristic ML and bootstrap (200 pseudoreplicates) searches were conducted with 10 random addition replicates and tree bisection-reconnection branch swapping using the best-fit model in PAUP* 4.0b10 [55].
The ML tree was reconstructed with PAUP* 4 [ 54] under the TrN + Γ + I model of sequence evolution and using the tree bisection reconnection (TBR) branch-swapping algorithm and 100 random sequence addition starting trees.
Maximum likelihood analyses were done using the proposed evolutionary model settings of Modeltest with three random addition replicates and heuristic tree search algorithm with tree bisection and reconnection (TBR).
In the latter, starting trees were obtained using random sequence addition and swapped using the tree bisection reconnection algorithm.
