Beyond Worst-Case Analysis for Symbolic Computation: Root Isolation Algorithms

TL;DR

This paper introduces a smoothed analysis framework for root isolation algorithms in symbolic computation, explaining their practical efficiency and bridging the gap between empirical performance and worst-case complexity theory.

Contribution

It develops a smoothed analysis approach for polynomial root isolation, providing quasi-linear complexity bounds for Descartes and other algorithms, and explains their practical efficiency.

Findings

Descartes algorithm has quasi-linear expected and smoothed complexity.

Smoothed analysis clarifies the efficiency of root isolation algorithms.

Results bridge the gap between empirical performance and worst-case theoretical bounds.

Abstract

We introduce beyond-worst-case analysis into symbolic computation. This is an extensive field which almost entirely relies on worst-case bit complexity, and we start from a basic problem in the field: isolating the real roots of univariate polynomials. This is a fundamental problem in symbolic computation and it is arguably one of the most basic problems in computational mathematics. The problem has a long history decorated with numerous ingenious algorithms and furnishes an active area of research. However, most available results in literature either focus on worst-case analysis in the bit complexity model or simply provide experimental benchmarking without any theoretical justifications of the observed results. We aim to address the discrepancy between practical performance of root isolation algorithms and prescriptions of worst-case complexity theory: We develop a smoothed analysis framework for polynomials with integer coefficients to bridge this gap. We demonstrate (quasi-)linear (expected and smoothed) complexity bounds for Descartes algorithm, that is one most well know symbolic algorithms for isolating the real roots of univariate polynomials with integer coefficients. Our results explain the surprising efficiency of Descartes solver in comparison to sophisticated algorithms that have superior worst-case complexity. We also analyse the Sturm solver, ANewDsc a symbolic-numeric algorithm that combines Descartes with Newton operator, and a symbolic algorithm for sparse polynomials.