Why does Monte Carlo fail to work properly in high-dimensional optimization problems?

TL;DR

This paper investigates the reasons behind the failure of Monte Carlo methods in high-dimensional optimization, providing insights into their limitations and potential solutions.

Contribution

It offers a novel explanation for Monte Carlo failures in high-dimensional spaces, addressing a gap in understanding of their practical limitations.

Findings

Monte Carlo methods struggle with curse of dimensionality

Identifies key factors causing failure in high dimensions

Suggests potential approaches to mitigate issues

Abstract

The paper proposes an answer to the question formulated in the title.