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A Globally Convergent Newton Method for Polynomials

Newton's method for polynomial root finding is one of mathematics' most well-known algorithms. The method also has its shortcomings: it is undefined at critical points, it could exhibit chaotic behavior and is only guaranteed to converge locally. Based on the {\it Geometric Modulus Principle} for a complex polynomial $p(z)$, together with a {\it Modulus Reduction Theorem} proved here, we develop the {\it Robust Newton's method} (RNM), defined everywhere with a step-size that guarantees an {\it a priori} reduction in polynomial modulus in each iteration. Furthermore, we prove RNM iterates converge globally, either to a root or a critical point. Specifically, given $\varepsilon $ and any seed $z_0$, in $t=O(1/\varepsilon^{2})$ iterations of RNM, independent of degree of $p(z)$, either $|p(z_t)| \leq \varepsilon$ or $|p(z_t) p'(z_t)| \leq \varepsilon$. By adjusting the iterates at {\it near-critical points}, we describe a {\it modified} RNM that necessarily convergence to a root. In combination with Smale's point estimation, RNM results in a globally convergent Newton's method having a locally quadratic rate. We present sample polynomiographs that demonstrate how in contrast with Newton's method RNM smooths out the fractal boundaries of basins of attraction of roots. RNM also finds potentials in computing all roots of arbitrary degree polynomials. A particular consequence of RNM is a simple algorithm for solving cubic equations.