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Our convergence analyses guarantee that the algorithm weakly converges to a solution under certain assumptions.
Explicit stability and convergence analyses are presented using Lyapunov tools.
Theoretical and experimental complexity, accuracy and convergence analyses are presented.
In addition, the convergence analyses regarding both TI-KT-CM and TII-KT-CM are conducted in our research.
Convergence analyses are carried out and the textile model is directly used to build up a virtual composite model suitable for virtual mechanical tests.
Convergence analyses are carried out on eigenvalues, displacement and stress fields in order to describe the capability of the formulation when compared to the exact three-dimensional results.
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Both "converge then cluster" and "cluster then converge" analyses document convergence between the results obtained from these two independent datasets in ways that are virtually never found by chance.
We also provide their convergence rate analyses (Theorem 2.6).
To check for convergence, all analyses were run twice, respectively using initial ω values of 0.5 and 1.5.
To avoid falling into suboptimal likelihood peaks and ensure proper convergence, the analyses were carried out multiple times, using various initial settings of the estimated parameters (d N/d S ratio and transition to transversion rate kappa, κ).
Following experimental runs of varying lengths to test for convergence, final analyses consisted of a single long chain sampled for 5 million generations (sampling increment = 100 generations, burnin = 100,000 trees), independently replicated three times.
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