Figure 3 - uploaded by Kevin E. M. Church
Content may be subject to copyright.
Left: Approximate spectrum of monodromy operator for the linearized predator-prey model (98)-(99) with parameters from Theorem 8.1.1, τ = 3 and h = 0.060, with a unit circle for scale and visual instability reference. Right: zoomed in portion of the region in the dashed box showing the unstable Floquet multiplier (black dot).

Left: Approximate spectrum of monodromy operator for the linearized predator-prey model (98)-(99) with parameters from Theorem 8.1.1, τ = 3 and h = 0.060, with a unit circle for scale and visual instability reference. Right: zoomed in portion of the region in the dashed box showing the unstable Floquet multiplier (black dot).

Source publication
Preprint
Full-text available
We develop validated numerical methods for the computation of Floquet multipliers of equilibria and periodic solutions of delay differential equations, as well as impulsive delay differential equations. Using our methods, one can rigorously count the number of Floquet multipliers outside a closed disc centered at zero or the number of multipliers c...

Contexts in source publication

Context 1
... previous observations, these results carry over to the linearization of the full system (95)-(97) at the predator-free equilibrium. The results of the computer-assisted proof are tabulated in Table 1 and the approximate spectrum for h = 0.060 and τ = 3 is plotted in Figure 3. ...
Context 2
... results of the computer-assisted proof are tabulated in Table 1 and the approximate spectrum for h = 0.060 and τ = 3 is plotted in Figure 3. ...

Similar publications

Article
Full-text available
We develop validated numerical methods for the computation of Floquet multipliers of equilibria and periodic solutions of delay differential equations, as well as impulsive delay differential equations. Using our methods, one can rigorously count the number of Floquet multipliers outside a closed disc centered at zero or the number of multipliers c...
Preprint
Full-text available
Existing unsupervised hash learning is a kind of attribute-centered calculation. It may not accurately preserve the similarity between data. This leads to low down the performance of hash function learning. In this paper, a hash algorithm is proposed with a hyper-class representation. It is a two-steps approach. The first step finds potential decis...