# Comments on "Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach"

## TL;DR

This paper critically examines a fractional steepest descent algorithm, highlighting overestimated convergence rates and a fundamental flaw that limits its effectiveness in adaptive learning applications.

## Contribution

It provides a detailed critique of the original algorithm's convergence analysis and identifies a critical flaw affecting its practical applicability.

## Key findings

- The convergence rate estimate is overstated.
- A fundamental flaw hampers the algorithm's broad applicability.
- Experimental results support the critique.

## Abstract

In this comment, we raise serious concerns over the derivation of the rate of convergence of fractional steepest descent algorithm in Fractional Adaptive Learning (FAL) approach presented in `Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach' [IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 4, pp. 653--662, April 2015]. We substantiate that the estimate of the rate of convergence is grandiloquent. We also draw attention towards a critical flaw in the design of the algorithm stymieing its applicability for broad adaptive learning problems. Our claims are based on analytical reasoning supported by experimental results.