We investigate when a computable automorphism of a computable field can be effectively extended to a computable automorphism of its (computable) algebraic closure. We then apply our results and techniques to study effective embeddings of computable difference fields into computable difference closed fields.

The problem of finding a nontrivial factor of a polynomial $f(x)$ over a finite field ${\mathbb{F}}_q$ has many known efficient, but randomized, algorithms. The deterministic complexity of this problem is a famous open question even assuming the generalized Riemann hypothesis (GRH). In this work we improve the state of the art by focusing on prime degree polynomials; let $n$ be the degree. If $(n-1)$ has a ‘large’ $r$-smooth divisor $s$, then we find a nontrivial factor of $f(x)$ in deterministic $\mbox{poly}(n^r,\log q)$ time, assuming GRH and that $s=\Omega (\sqrt{n/2^r})$. Thus, for $r=O(1)$ our algorithm is polynomial time. Further, for $r=\Omega (\log \log n)$ there are infinitely many prime degrees $n$ for which our algorithm is applicable and better than the best known, assuming GRH. Our methods build on the algebraic-combinatorial framework of $m$-schemes initiated by Ivanyos, Karpinski and Saxena (ISSAC 2009). We show that the $m$-scheme on $n$ points, implicitly appearing in our factoring algorithm, has an exceptional structure, leading us to the improved time complexity. Our structure theorem proves the existence of small intersection numbers in any association scheme that has many relations, and roughly equal valencies and indistinguishing numbers.

We will compute the subgroup permutability degree of the projective special linear groups PSL(2,pn).

Natural sufficient conditions for a polynomial to have a local minimum at a point are considered. These conditions tend to hold with probability 1. It is shown that polynomials satisfying these conditions at each minimum point have nice presentations in terms of sums of squares. Applications are given to optimization on a compact set and also to global optimization. In many cases, there are degree bounds for such presentations. These bounds are of theoretical interest, but they appear to be too large to be of much practical use at present. In the final section, other more concrete degree bounds are obtained which ensure at least that the feasible set of solutions is not empty.