Research article

Eighth order, Numerov-like schemes with coefficients tailored for superior performance on ODE systems with oscillatory solutions

  • Received: 11 April 2024 Revised: 10 July 2024 Accepted: 22 July 2024 Published: 05 August 2024
  • MSC : 65L05, 65L06

  • Second order Ordinary Differential Equations (ODE) were considered. Numerov-like techniques employing effectively seven stages per step and sharing eighth algebraic order were under examination for numerically solving them. The coefficients of these methods were contingent on four independent parameters. To tackle issues with oscillatory solutions, we typically aimed to fulfill specific criteria such as minimizing phase-lag, expanding the periodicity interval, or even neutralizing amplification errors. These latter attributes stemmed from a test problem mimicking an ideal trigonometric trajectory. Here, we suggested training the coefficients of the chosen method family across a broad spectrum of pertinent problems. Following this training using the differential evolution method, we identified a particular method that surpassed others in this category across an even broader array of oscillatory problems.

    Citation: Theodore E. Simos, Charalampos Tsitouras. Eighth order, Numerov-like schemes with coefficients tailored for superior performance on ODE systems with oscillatory solutions[J]. AIMS Mathematics, 2024, 9(9): 23368-23383. doi: 10.3934/math.20241136

    Related Papers:

  • Second order Ordinary Differential Equations (ODE) were considered. Numerov-like techniques employing effectively seven stages per step and sharing eighth algebraic order were under examination for numerically solving them. The coefficients of these methods were contingent on four independent parameters. To tackle issues with oscillatory solutions, we typically aimed to fulfill specific criteria such as minimizing phase-lag, expanding the periodicity interval, or even neutralizing amplification errors. These latter attributes stemmed from a test problem mimicking an ideal trigonometric trajectory. Here, we suggested training the coefficients of the chosen method family across a broad spectrum of pertinent problems. Following this training using the differential evolution method, we identified a particular method that surpassed others in this category across an even broader array of oscillatory problems.



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