A Family of L(α) − stable Block Methods for Stiff Ordinary Differential Equations
I. J. Ajie, M. N. O. Ikhile, P. Onumanyi
Abstract
I. J. Ajie, M. N. O. Ikhile, P. Onumanyi
Abstract
A family of self-starting L(α)-stable linear multistep block methods (LMBMs) for solving stiff initial value problems (IVPs) in ordinary differential equations (ODEs) is proposed. The constructions are done by pairing three-step Top Order Method (TOM) and k-step Backward Differentiation Formulas (BDF) and using the shifting techniques introduced by Ajie et al (2011). The performance of the resultant block methods on the numerical examples considered shows their effectiveness. Keywords Top Order Method (TOM), Backward Differentiation Formulas (BDF), L(α) − stable, A(α) − stable, Linear multistep block methods (LMBMs)
OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
A family of self-starting L(α)-stable linear multistep block methods (LMBMs) for solving stiff initial value problems (IVPs) in ordinary differential equations (ODEs) is proposed. The constructions are done by pairing three-step Top Order Method (TOM) and k-step Backward Differentiation Formulas (BDF) and using the shifting techniques introduced by Ajie et al (2011). The performance of the resultant block methods on the numerical examples considered shows their effectiveness. Keywords Top Order Method (TOM), Backward Differentiation Formulas (BDF), L(α) − stable, A(α) − stable, Linear multistep block methods (LMBMs)
Key concepts: Linear multistep method, Backward differentiation formula, Mathematics, Ordinary differential equation, Ode, Block (permutation group theory), Runge–Kutta methods, Stability (learning theory)