Linking the Basel Problem to Infinite Series Analysis

Fascia, Michael

doi:10.2139/ssrn.5019530

Linking the Basel Problem to Infinite Series Analysis

Fascia, Michael

Authors

Dr Michael Fascia M.Fascia2@napier.ac.uk
Lecturer

Abstract

This paper reexamines the Basel problem, an inquiry into the sum of the infinite series of squared reciprocals, famously resolved by Leonhard Euler. Euler's resolution has left a lasting legacy on the domains of mathematical analysis, number theory, and complex analysis. In this study, we augment historical methodologies with contemporary computational techniques, introducing the ISeries Formula, an innovative method designed for the efficient approximation of infinite series. This method significantly streamlines computational procedures and accelerates numerical calculations, rendering it indispensable for scenarios necessitating swift and dependable estimations. We explore Euler's classical strategies juxtaposed with this novel contribution, substantiating the performance and precision of the ISeries Formula through rigorous mathematical proofs. Our analysis contrasts its computational economy and enhanced processing velocity with conventional methods. Moreover, the broader ramifications of our findings for pedagogical and practical applications are discussed, illustrating how the ISeries Formula acts as a conduit linking venerable mathematical inquiries with modern computational exigencies. The integration of the ISeries Formula into the narrative of the Basel problem not only reaffirms the pertinence of these classical inquiries within today's mathematical discourse but also highlights how contemporary innovations can advance theoretical and applied mathematics. This synthesis underscores the potential of new analytical tools to fortify the foundations of mathematical analysis, ensuring that traditional challenges continue to inspire and inform future generations of mathematicians. The paper makes a notable contribution to the field by introducing a theoretically sound and computationally efficient method for series approximation.