Lower bounds for decomposable univariate wild polynomials

A univariate polynomial f over a field is decomposable if f = g o h = g(h) for nonlinear polynomials g and h. It is intuitively clear that the decomposable polynomials form a small minority among all polynomials over a finite field. The tame case, where the characteristic of Fq does not divide n = deg f, is fairly well-understood, and we have reasonable bounds on the number of decomposables of degree n. Nevertheless, no exact formula is known if n has more than two prime factors. In order to count the decomposables, one wants to know, under a suitable normalization, the number of collisions, where essentially different components (g, h) yield the same f. In the tame case, Ritt's Second Theorem classifies all collisions of two such pairs.We present a normal form for collisions of any number of decompositions with any number of components and describe exactly the (non)uniqueness of the parameters in the tame case. This yields an exact formula for the number of such collisions over a finite field. We conclude with a fast algorithm for the exact number of decomposable polynomials of degree n over a finite field Fq of characteristic coprime to n.

A univariate polynomial f over a field is decomposable if it is the composition f = g ⊕ h of two polynomials g and h whose degree is at least 2. We determine an approximation to the number of decomposable polynomials over a finite field. The tame case, where the field characteristic p does not divide the degree n of f, is reasonably well understood, and we obtain exponentially decreasing error bounds.The wild case, where p divides n, is more challenging and our error bounds are weaker. A centerpiece of our approach is a decomposition algorithm in the wild case, which shows that sufficiently many polynomials are decomposable.

Compositions and collisions at degree [formula omitted

Concretely efficient interactive oracle proofs (IOPs) are of interest due to their applications to scaling blockchains, their minimal security assumptions, and their potential future-proof resistance to quantum attacks.Scalable IOPs, in which prover time scales quasilinearly with the computation size and verifier time scales poly-logarithmically with it, have been known to exist thus far only over a set of finite fields of negligible density, namely, over “FFT-friendly” fields that contain a sub-group of size \(2^\mathsf{{k}} \).Our main result is to show that scalable IOPs can be constructed over any sufficiently large finite field, of size that is at least quadratic in the length of computation whose integrity is proved by the IOP. This result has practical applications as well, because it reduces the proving and verification complexity of cryptographic statements that are naturally stated over pre-defined finite fields which are not “FFT-friendly”. Prior state-of-the-art scalable IOPs relied heavily on arithmetization via univariate polynomials and Reed–Solomon codes over FFT-friendly fields. To prove our main result and extend scalability to all large finite fields, we generalize the prior techniques and use new algebraic geometry codes evaluated on sub-groups of elliptic curves (elliptic curve codes). We also show a new arithmetization scheme that uses the rich and well-understood group structure of elliptic curves to reduce statements of computational integrity to other statements about the proximity of functions evaluated on the elliptic curve to the new family of elliptic curve codes.

We present a deterministic $2^{O(t)}q^{\frac{t-2}{t-1}+o(1)}$ algorithm to decide whether a univariate polynomial $f$, with $t$ monomial terms and degree $<q$, has a root in the finite field $\mathbb{F}_q$. Our method is the first with complexity sublinear in $q$ when $t$ is fixed. We also prove a structural property for the nonzero roots in $\mathbb{F}_q$ of any $t$-nomial: The nonzero roots always admit a partition into no more than $2(q-1)^{\frac{t-2}{t-1}}$ cosets, each associated with one of two subgroups $S_1\subseteq S_2$ of $\mathbb{F}^*_q$. This can be thought of as a finite field analogue of Descartes' rule. A corollary of our results is the first deterministic sublinear algorithm for detecting common degree-one factors of $k$-tuples of $t$-nomials in $\mathbb{F}_q[x]$ when $k$ and $t$ are fixed. When $t$ is not fixed we show that, for $p$ prime, detecting roots in $\mathbb{F}_p$ for $f$ is $\mathbf{NP}$-hard with respect to $\mathbf{BPP}$-reductions. Finally, we prove that if the complexity of root detection is sublinear (in a refined sense), relative to the straight-line program encoding, then $\mathbf{NEXP}\nsubseteq\mathbf{P/poly}$.

Sparse univariate polynomials with many roots over finite fields

This paper gives an algorithm for finding the irreducible factors of any multivariate polynomial with integer coefficients. The algorithm begins by making substitutions for all but one of the variable. This univariate polynomial is then factored by a known method, which uses an algorithm of Berlekamp for factoring univariate polynomials over finite fields. After this factorization is done, the multivariate factors are recovered from the univariate ones by a kind of Hensel algorithm. A number of ideas are given which greatly speed the computation in some special cases.

This thesis discusses two sets of fully specified algorithms which, given a univariate polynomial with integer coefficients (with possible multiple roots) and a positive rational error bound, uses infinite-precision arithmetic and Sturm's Theorem to compute intervals containing the real roots of the polynomial and whose lengths are less than the given error bound, The algorithms also provide a simple means of determining the number of real roots in any interval.The first set of algorithms uses infinite-precision integer arithmetic to compute the sequence of polynomials required by Sturm's Theorem and also to do all the required polynomial evaluations. The second set uses congruence arithmetic to compute the images of the sequence of polynomials over the finite fields GF(p i ) for several primes p i and then uses the natural homomorphisms from the integers onto the finite fields GF(p i ) and the Chinese Remainder Theorem to do the evaluations. The primary advantage of the use of congruence arithmetic in the second set of algorithms is that the computing time to multiply any two elements of GF(p i ) is a constant whereas the time to multiply any two integers is proportional to the square of their lengths.The computing times of both sets of algorithms are analyzed in detail using the techniques of computing time analysis developed in the past few years by G. E. Collins and D. Knuth. It is shown that if P is a primitive, positive, univariate polynomial over the integers of positive degree, m, whose coefficients are bounded in magnitude by d, and which has L real roots, and ε is some positive rational number, 0 &lt; ε ≤ 1, then the computing time to approximate the roots of P to within is:[EQUATION]using either set of algorithms.The empirical results of applying the two sets of algorithms to random polynomials and the first ten Legendre and Chebyshev polynomials are reported. Neither set of algorithms proved to be faster for all the polynomials tested.

We present the formula for the number of monic irreducible polynomials of degree n over the finite field \({\mathbb {F}}_q\) where the coefficients of \(x^{n-1}\) and x vanish for \(n\ge 3\). In particular, we give a relation between rational points of algebraic curves over finite fields and the number of elements \(a\in {\mathbb {F}}_{q^n}\) for which Trace\((a)=0\) and Trace\((a^{-1})=0\).

On the characterization of some algebraically defined bipartite graphs of girth eight

The Number of Permutation Polynomials of a Given Degree Over a Finite Field

Polynomial and spline quasi-interpolants (QIs) are practical and effective approximation operators. Among their remarkable properties, let us cite for example: good shape properties, easy computation and evaluation (no linear system to solve), uniform boundedness independently of the degree (polynomials) or of the partition (splines), good approximation order. We shall emphasize new results on various types of univariate and multivariate polynomial or spline QIs, depending on the nature of coefficient functionals, which can be differential, discrete or integral. We shall also present some applications of QIs to numerical methods.

Factorization of a class of polynomials over finite fields

We give a formula and an estimation for the number of irreducible polynomials in two (or more) variables over a finite field.

Lifting/Descending Processes for Polynomial Zeros