Near NP-Completeness for Detecting p-adic Rational Roots in One Variable

We show that deciding whether a sparse univariate polynomial has a p-adic rational root can be done in NP for most inputs. We also prove a polynomial-time upper bound for trinomials with suitably generic p-adic Newton polygon. We thus improve the best previous complexity upper bound of EXPTIME. We also prove an unconditional complexity lower bound of NP-hardness with respect to randomized reductions for general univariate polynomials. The best previous lower bound assumed an unproved hypothesis on the distribution of primes in arithmetic progression. We also discuss how our results complement analogous results over the real numbers.Comment: 8 pages in 2 column format, 1 illustration. Submitted to a conferenc

Counting Value Sets: Algorithm and Complexity

Let $p$ be a prime. Given a polynomial in \F_{p^m}[x] of degree $d$ over the finite field \F_{p^m}, one can view it as a map from \F_{p^m} to \F_{p^m}, and examine the image of this map, also known as the value set. In this paper, we present the first non-trivial algorithm and the first complexity result on computing the cardinality of this value set. We show an elementary connection between this cardinality and the number of points on a family of varieties in affine space. We then apply Lauder and Wan's $p$-adic point-counting algorithm to count these points, resulting in a non-trivial algorithm for calculating the cardinality of the value set. The running time of our algorithm is $(pmd)^{O(d)}$. In particular, this is a polynomial time algorithm for fixed $d$ if $p$ is reasonably small. We also show that the problem is #P-hard when the polynomial is given in a sparse representation, $p=2$, and $m$ is allowed to vary, or when the polynomial is given as a straight-line program, $m=1$ and $p$ is allowed to vary. Additionally, we prove that it is NP-hard to decide whether a polynomial represented by a straight-line program has a root in a prime-order finite field, thus resolving an open problem proposed by Kaltofen and Koiran in \cite{Kaltofen03,KaltofenKo05}

Ultrametric Fewnomial Theory

An ultrametric field is a field that is locally compact as a metric space with respect to a non-archimedean absolute value. The main topic of this dissertation is to study roots of polynomials over such fields. If we have a univariate polynomial with coefficients in an ultrametric field and non-vanishing discriminant, then there is a bijection between the set of roots of the polynomial and classes of roots of the same polynomial in a finite ring. As a consequence, there is a ball in the polynomial space where all polynomials in it have the same number of roots. If a univariate polynomial satisfies certain generic conditions, then we can efficiently compute the exact number of roots in the field. We do that by using Hensel's lemma and some properties of Newton's polygon. In the multivariate case, if we have a square system of polynomials, we consider the tropical set which is the intersection of the tropical varieties of its polynomials. The tropical set contains the set of valuations of the roots, and for every point in the tropical set, there is a corresponding system of lower polynomials. If the system satisfies some generic conditions, then for each point w in the tropical set the number of roots of valuation w equals the number roots of valuation w of the lower system. The last result enables us to compute the exact number of roots of a polynomial system where the tropical set is finite and the lower system consists of binomials. This algorithmic method can be performed in polynomial-time if we fix the number of variables. We conclude the dissertation with a discussion of the feasibility problem. We consider the problem of the p-adic feasibility of polynomials with integral coefficients with the prime number p as a part of the input. We prove this problem can be solved in nondeterministic polynomial-time. Furthermore, we show that any problem, which can be solved in nondeterministic polynomial-time, can be reduced to this feasibility problem in randomized polynomial-time

Bounded-degree factors of lacunary multivariate polynomials

In this paper, we present a new method for computing bounded-degree factors of lacunary multivariate polynomials. In particular for polynomials over number fields, we give a new algorithm that takes as input a multivariate polynomial f in lacunary representation and a degree bound d and computes the irreducible factors of degree at most d of f in time polynomial in the lacunary size of f and in d. Our algorithm, which is valid for any field of zero characteristic, is based on a new gap theorem that enables reducing the problem to several instances of (a) the univariate case and (b) low-degree multivariate factorization. The reduction algorithms we propose are elementary in that they only manipulate the exponent vectors of the input polynomial. The proof of correctness and the complexity bounds rely on the Newton polytope of the polynomial, where the underlying valued field consists of Puiseux series in a single variable.Comment: 31 pages; Long version of arXiv:1401.4720 with simplified proof

Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems

Computational algebraic geometry is the study of roots of polynomials and polynomial systems. We are familiar with the notion of degree, but there are other ways to consider a polynomial: How many variables does it have? How many terms does it have? Considering the sparsity of a polynomial means we pay special attention to the number of terms. One can sometimes profit greatly by making use of sparsity when doing computations by utilizing tools from linear programming and integer matrix factorization. This thesis investigates several problems from the point of view of sparsity. Consider a system F of n polynomials over n variables, with a total of n + k distinct exponent vectors over any local field L. We discuss conjecturally tight bounds on the maximal number of non-degenerate roots F can have over L, with all coordinates having fixed phase, as a function of n, k, and L only. In particular, we give new explicit systems with number of roots approaching the best known upper bounds. We also give a complete classification for when an n-variate n + 2-nomial positive polynomial can be written as a sum of squares of polynomials. Finally, we investigate the problem of approximating roots of polynomials from the viewpoint of sparsity by developing a method of approximating roots for binomial systems that runs more efficiently than other current methods. These results serve as building blocks for proving results for less sparse polynomial systems