arXiv subjects

Hervé Fournier

Publications and source records attributed to Hervé Fournier.

5 recordsLinked to original sources

arXiv2023

Towards Optimal Depth-Reductions for Algebraic Formulas

Classical results of Brent, Kuck and Maruyama (IEEE Trans. Computers 1973) and Brent (JACM 1974) show that any algebraic formula of size s can be converted to one of depth O(log s) with only a polynomial blow-up in size. In this paper, we consider a fine-grained version of this result depending on the degree of the polynomial computed by the algebraic formula. Given a homogeneous algebraic formula of size s computing a polynomial P of degree d, we show that P can also be computed by an (unbounded fan-in) algebraic formula of depth O(log d) and size poly(s). Our proof shows that this result also holds in the highly restricted setting of monotone, non-commutative algebraic formulas. This improves on previous results in the regime when d is small (i.e., d<<s). In particular, for the setting of d=O(log s), along with a result of Raz (STOC 2010, JACM 2013), our result implies the same depth reduction even for inhomogeneous formulas. This is particularly interesting in light of recent algebraic formula lower bounds, which work precisely in this ``low-degree" and ``low-depth" setting. We also show that these results cannot be improved in the monotone setting, even for commutative formulas.

cs.CC↗

arXiv2015

Computing the Gromov hyperbolicity of a discrete metric space

We give exact and approximation algorithms for computing the Gromov hyperbolicity of an n-point discrete metric space. We observe that computing the Gromov hyperbolicity from a fixed base-point reduces to a (max,min) matrix product. Hence, using the (max,min) matrix product algorithm by Duan and Pettie, the fixed base-point hyperbolicity can be determined in O(n^2.69) time. It follows that the Gromov hyperbolicity can be computed in O(n^3.69) time, and a 2-approximation can be found in O(n^2.69) time. We also give a (2 log_2 n)-approximation algorithm that runs in O(n^2) time, based on a tree-metric embedding by Gromov. We also show that hyperbolicity at a fixed base-point cannot be computed in O(n^2.05) time, unless there exists a faster algorithm for (max,min) matrix multiplication than currently known.

cs.CG↗

arXiv2013

On fixed-polynomial size circuit lower bounds for uniform polynomials in the sense of Valiant

Assuming the Generalised Riemann Hypothesis (GRH), we show that for all k, there exist polynomials with coefficients in $\MA$ having no arithmetic circuits of size O(n^k) over the complex field (allowing any complex constant). We also build a family of polynomials that can be evaluated in AM having no arithmetic circuits of size O(n^k). Then we investigate the link between fixed-polynomial size circuit bounds in the Boolean and arithmetic settings. In characteristic zero, it is proved that $\NP \not\subset \size(n^k)$, or $\MA \subset \size(n^k)$, or NP=MA imply lower bounds on the circuit size of uniform polynomials in n variables from the class VNP over the complex field, assuming GRH. In positive characteristic p, uniform polynomials in VNP have circuits of fixed-polynomial size if and only if both VP=VNP over F_p and Mod_pP has circuits of fixed-polynomial size.

cs.CC↗

arXiv2012

A deterministic algorithm for fitting a step function to a weighted point-set

Given a set of n points in the plane, each point having a positive weight, and an integer k>0, we present an optimal O(n \log n)-time deterministic algorithm to compute a step function with k steps that minimizes the maximum weighted vertical distance to the input points. It matches the expected time bound of the best known randomized algorithm for this problem. Our approach relies on Cole's improved parametric searching technique.

cs.DS↗

arXiv2012

Monomials in arithmetic circuits: Complete problems in the counting hierarchy

We consider the complexity of two questions on polynomials given by arithmetic circuits: testing whether a monomial is present and counting the number of monomials. We show that these problems are complete for subclasses of the counting hierarchy which had few or no known natural complete problems. We also study these questions for circuits computing multilinear polynomials.

cs.CC↗