Constructing Segmented Differentiable Quadratics to Determine Algorithmic Run Times and Model Non-Polynomial Functions

TL;DR

This paper introduces a novel method using segmented differentiable quadratics to analyze algorithmic run times and construct non-polynomial functions with high accuracy, offering an alternative to traditional complexity analysis.

Contribution

The paper presents a new approach combining quadratic segments and Lagrangian principles to model and analyze complex functions and algorithm efficiencies.

Findings

Achieved over 99% accuracy in modeling functions.

Effectively determines run time behavior and derivatives.

Applicable to non-polynomial functions like logarithms.

Abstract

We propose an approach to determine the continual progression of algorithmic efficiency, as an alternative to standard calculations of time complexity, likely, but not exclusively, when dealing with data structures with unknown maximum indexes and with algorithms that are dependent on multiple variables apart from just input size. The proposed method can effectively determine the run time behavior $F$ at any given index $x$ , as well as $\frac{\partial F}{\partial x}$, as a function of only one or multiple arguments, by combining $\frac{n}{2}$ quadratic segments, based upon the principles of Lagrangian Polynomials and their respective secant lines. Although the approach used is designed for analyzing the efficacy of computational algorithms, the proposed method can be used within the pure mathematical field as a novel way to construct non-polynomial functions, such as $\log_2{n}$ or $\frac{n+1}{n-2}$, as a series of segmented differentiable quadratics to model functional behavior and reoccurring natural patterns. After testing, our method had an average accuracy of above of 99\% with regard to functional resemblance.