sequence transformation in a sentence

• Sequence transformations that are not linear are called nonlinear sequence transformations.
• Sequence transformations that are not linear are called nonlinear sequence transformations.
• Operators on these spaces are known as sequence transformations.
• Such acceleration is commonly accomplished with sequence transformations.
• In general, the non-linear sequence transformations tend to be more powerful.
• Summation of divergent series is also related to extrapolation methods and sequence transformations as numerical techniques.
• If the original sequence is divergent, the sequence transformation acts as extrapolation method to the antilimit \ ell.
• The mappings from the original to the transformed series may be linear ( as defined in the article sequence transformations ), or non-linear.
• An example of a nonlinear sequence transformation is Aitken's delta-squared process, used to improve the rate of convergence of a slowly convergent sequence.
• Examples of such techniques are Pad?approximants, Levin-type sequence transformations, and order-dependent mappings related to renormalization techniques for large-order perturbation theory in quantum mechanics.
• It's difficult to see sequence transformation in a sentence .
• Especially nonlinear sequence transformations often provide powerful numerical methods for the summation of divergent series or asymptotic series that arise for instance in perturbation theory, and may be used as highly effective extrapolation methods.
• There are powerful numerical summation methods that are neither regular nor linear, for instance nonlinear sequence transformations like Levin-type sequence transformations and Pad?approximants, as well as the order-dependent mappings of perturbative series based on renormalization techniques.
• There are powerful numerical summation methods that are neither regular nor linear, for instance nonlinear sequence transformations like Levin-type sequence transformations and Pad?approximants, as well as the order-dependent mappings of perturbative series based on renormalization techniques.