Adaptive Adams-Type Methods Based on Algebraic, Trigonometric, and Exponential Interpolation

We consider an adaptive Adams-type multistep method for the numerical solution of ordinary differential equations based on algebraic, trigonometric, and exponential interpolation (the ATE method). For each interpolation class, the corresponding schemes are derived following the same construction as classical Adams methods. The stability properties of the trigonometric and exponential variants are analyzed, highlighting their differences relative to the algebraic method. The adaptive ATE algorithm selects one of the three schemes at each step according to a local interpolation indicator. This enables the method to adjust to different types of solution behavior. Numerical experiments are presented across a variety of differential equations, and the accuracy and execution times of the methods are compared.

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Publication Details

Journal
Mathematics
Published
2026-11-03
DOI
https://doi.org/10.3390/math14214013
Primary Topic
Numerical methods for differential equations
Type
article
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article

Adaptive Adams-Type Methods Based on Algebraic, Trigonometric, and Exponential Interpolation

P. Kh. Atanasova, Stoyan Cheresharov, Valentin Georgiev, Elis Dieva
Mathematics
Numerical methods for differential equations
article

Adaptive Adams-Type Methods Based on Algebraic, Trigonometric, and Exponential Interpolation

P. Kh. Atanasova, Stoyan Cheresharov, Valentin Georgiev, Elis Dieva
article en

Abstract

We consider an adaptive Adams-type multistep method for the numerical solution of ordinary differential equations based on algebraic, trigonometric, and exponential interpolation (the ATE method). For each interpolation class, the corresponding schemes are derived following the same construction as classical Adams methods. The stability properties of the trigonometric and exponential variants are analyzed, highlighting their differences relative to the algebraic method. The adaptive ATE algorithm selects one of the three schemes at each step according to a local interpolation indicator. This enables the method to adjust to different types of solution behavior. Numerical experiments are presented across a variety of differential equations, and the accuracy and execution times of the methods are compared.

MathematicsVol. 14(21)
Plovdiv University (BG), Bulgarian Academy of Sciences (BG), Institute of Mechanics (BG)
Openalex Percentile: Top 15%
Numerical methods for differential equations
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