Accelerated Newton Iteration: Roots of Black Box Polynomials and Matrix Eigenvalues

We study the problem of computing the largest root of a real rooted polynomial $p(x)$ to within error $\varepsilon$ given only black box access to it, i.e., for any $x \in {\mathbb R}$, the algorithm can query an oracle for the value of $p(x)$, but the algorithm is not allowed access to the coefficients of $p(x)$. A folklore result for this problem is that the largest root of a polynomial can be computed in $O(n \log (1/\varepsilon ))$ polynomial queries using the Newton iteration. We give a simple algorithm that queries the oracle at only $O(\log n \log(1/\varepsilon ))$ points, where $n$ is the degree of the polynomial. Our algorithm is based on a novel approach for accelerating the Newton method by using higher derivatives. As a special case, we consider the problem of computing the top eigenvalue of a symmetric matrix in ${\mathbb Q}^{n \times n}$ to within error $\varepsilon$ in time polynomial in the input description, i.e., the number of bits to describe the matrix and $\log(1/\varepsilon )$. Well-known methods such as the power iteration and Lanczos iteration incur running time polynomial in $1/\varepsilon$, while Gaussian elimination takes $Ω(n^4)$ bit operations. As a corollary of our main result, we obtain a $\tilde{O}(n^ω \log^2 ( ||A||_F/\varepsilon ))$ bit complexity algorithm to compute the top eigenvalue of the matrix $A$ or to check if it is approximately PSD ($A \succeq -\varepsilon I$).

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