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However, Luoma et al. (2004) argue that linear models in a nonlinear situation result in incorrect inferences.
In addition, Luoma et al. (2004) argue that linear models in a nonlinear situation result in the estimation of incorrect inferences.
However, it should be noted that the extension to the nonlinear situation is not done without the introduction of new problems.
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It frequently consists of piecewise linear approximations to basically nonlinear situations (i.e., a series of short straight lines that roughly approximate a curve).
It consists of nonlinear situation-changing continuous-time properties.
Attention is first focused on linear problems and extensions to nonlinear situations are subsequently briefly addressed.
These tests show that stable computations are obtained with the proposed algorithms in nonlinear situations where the trapezoidal rule encounters a well-known instability.
Whereas Part 1 primarily focuses on the fundamental theoretical developments and rigor of the generalized representations as related to stability, accuracy and the like, in Part 2 we specifically focus attention towards practical second-order time accurate representations including also assessing the algorithmic attributes and extensions to nonlinear situations.
All these central steps are thus more or less directly transferable between the linear and nonlinear situations.
However, for nonlinear systems the situation is more complicated.
In the case of nonlinear ODEs, the situation is not so simple due to the fact that the singular points in this case may depend on the initial conditions: they are movable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com