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Computational Galois theory, in particular the problem of computing the Galois group of a given polynomial, is a very old problem. Currently, the best algorithmic solution is Stauduhar’s method. Computationally, one of the key challenges in the application of Stauduhar’s method is to find, for a given pair of groups $H<G$, a $G$-relative $H$-invariant, that is a multivariate polynomial $F$ that is $H$-invariant, but not $G$-invariant. While generic, theoretical methods are known to find such $F$, in general they yield impractical answers. We give a general method for computing invariants of large degree which improves on previous known methods, as well as various special invariants that are derived from the structure of the groups. We then apply our new invariants to the task of computing the Galois groups of polynomials over the rational numbers, resulting in the first practical degree independent algorithm.
We analyse the mask associated with the $2n$-point interpolatory Dubuc–Deslauriers subdivision scheme $S_{a^{[n]}}$. Sharp bounds are presented for the magnitude of the coefficients $a^{[n]}_{2i-1}$ of the mask. For scales $i \in [1,\sqrt{n}]$ it is shown that $|a^{[n]}_{2i-1}|$ is comparable to $i^{-1}$, and for larger power scales, exponentially decaying bounds are obtained. Using our bounds, we may precisely analyse the summability of the mask as a function of $n$ by identifying which coefficients of the mask contribute to the essential behaviour in $n$, recovering and refining the recent result of Deng–Hormann–Zhang that the operator norm of $S_{a^{[n]}}$ on $\ell ^\infty $ grows logarithmically in $n$.
We consider the classical problem of finding the best uniform approximation by polynomials of $1/(x-a)^2,$ where $a>1$ is given, on the interval $[-\! 1,1]$. First, using symbolic computation tools we derive the explicit expressions of the polynomials of best approximation of low degrees and then give a parametric solution of the problem in terms of elliptic functions. Symbolic computation is invoked then once more to derive a recurrence relation for the coefficients of the polynomials of best uniform approximation based on a Pell-type equation satisfied by the solutions.
We show how one can obtain an asymptotic expression for some special functions with a very explicit error term starting from appropriate upper bounds. We will work out the details for the Bessel function $J_\nu (x)$ and the Airy function ${\rm Ai}(x).$ In particular, we answer a question raised by Olenko and find a sharp bound on the difference between $J_\nu (x)$ and its standard asymptotics. We also give a very simple and surprisingly precise approximation for the zeros ${\rm Ai}(x).$
This paper contains some applications of the description of knot diagrams by genus, and Gabai’s methods of disk decomposition. We show that there exists no genus one knot of canonical genus 2, and that canonical genus 2 fiber surfaces realize almost every Alexander polynomial only finitely many times (partially confirming a conjecture of Neuwirth).
As a contribution to an eventual solution of the problem of the determination of the maximal subgroups of the Monster we prove that the Monster does not contain any subgroup isomorphic to $\mathrm{PSL}_2(27)$.
We describe algorithms that allow the computation of fundamental domains in the Bruhat–Tits tree for the action of discrete groups arising from quaternion algebras. These algorithms are used to compute spaces of rigid modular forms of arbitrary even weight, and we explain how to evaluate such forms to high precision using overconvergent methods. Finally, these algorithms are applied to the calculation of conjectural equations for the canonical embedding of p-adically uniformizable rational Shimura curves. We conclude with an example in the case of a genus 4 Shimura curve.
Let $Q(N;q,a)$ be the number of squares in the arithmetic progression $qn+a$, for $n=0$,$1,\ldots,N-1$, and let $Q(N)$ be the maximum of $Q(N;q,a)$ over all non-trivial arithmetic progressions $qn + a$. Rudin’s conjecture claims that $Q(N)=O(\sqrt{N})$, and in its stronger form that $Q(N)=Q(N;24,1)$ if $N\ge 6$. We prove the conjecture above for $6\le N\le 52$. We even prove that the arithmetic progression $24n+1$ is the only one, up to equivalence, that contains $Q(N)$ squares for the values of $N$ such that $Q(N)$ increases, for $7\le N\le 52$ ($N=8,13,16,23,27,36,41$and $52$).
We show that if a Barker sequence of length $n>13$ exists, then either n $=$ 3 979 201 339 721749 133 016 171 583 224 100, or $n > 4\cdot 10^{33}$. This improves the lower bound on the length of a long Barker sequence by a factor of nearly $2000$. We also obtain eighteen additional integers $n<10^{50}$ that cannot be ruled out as the length of a Barker sequence, and find more than 237 000 additional candidates $n<10^{100}$. These results are obtained by completing extensive searches for Wieferich prime pairs and using them, together with a number of arithmetic restrictions on $n$, to construct qualifying integers below a given bound. We also report on some updated computations regarding open cases of the circulant Hadamard matrix problem.
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.
Let $G(q)$ be a finite Chevalley group, where $q$ is a power of a good prime $p$, and let $U(q)$ be a Sylow $p$-subgroup of $G(q)$. Then a generalized version of a conjecture of Higman asserts that the number $k(U(q))$ of conjugacy classes in $U(q)$ is given by a polynomial in $q$ with integer coefficients. In [S. M. Goodwin and G. Röhrle, J. Algebra 321 (2009) 3321–3334], the first and the third authors of the present paper developed an algorithm to calculate the values of $k(U(q))$. By implementing it into a computer program using $\mathsf{GAP}$, they were able to calculate $k(U(q))$ for $G$ of rank at most five, thereby proving that for these cases $k(U(q))$ is given by a polynomial in $q$. In this paper we present some refinements and improvements of the algorithm that allow us to calculate the values of $k(U(q))$ for finite Chevalley groups of rank six and seven, except $E_7$. We observe that $k(U(q))$ is a polynomial, so that the generalized Higman conjecture holds for these groups. Moreover, if we write $k(U(q))$ as a polynomial in $q-1$, then the coefficients are non-negative.
Under the assumption that $k(U(q))$ is a polynomial in $q-1$, we also give an explicit formula for the coefficients of $k(U(q))$ of degrees zero, one and two.
The zeros of certain different sequences of orthogonal polynomials interlace in a well-defined way. The study of this phenomenon and the conditions under which it holds lead to a set of points that can be applied as bounds for the extreme zeros of the polynomials. We consider different sequences of the discrete orthogonal Meixner and Kravchuk polynomials and use mixed three-term recurrence relations, satisfied by the polynomials under consideration, to identify bounds for the extreme zeros of Meixner and Kravchuk polynomials.
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg
In chemical engineering, mathematical modeling is crucial in order to design equipment, choose proper operating conditions, regulate processes, etc. It is almost always necessary to use experimental data for model development. Figure 7.1(a) shows a data set and linear fit for these data. From this result, is easy to see that this line describes these data. However, from the data set shown in Figure 7.1(b), this is not so clear. The solid line represents the linear fit for these data, which is derived from regression analysis. By simply observing the data, it can be seen that either of the dashed lines could be possible fits. These results clearly show that it is not possible to determine parameters for models only by which line looks a good fit, but that a detailed statistical analysis is needed.
In this chapter, we start by describing linear regression, which is a method for determining parameters in a model. The accuracy of the parameters can be estimated by confidence intervals and regions, which will be discussed in Section 7.5. Correlation between parameters is often a major problem for large mathematical models, and the determination of so-called correlation matrices will be described. In more complex chemical engineering models, non-linear regression is required, and this is also described in this chapter.
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg
A mathematical model can never give an exact description of the real world, and the basic concept in all engineering modeling is, “All models are wrong – some models are useful.” Reformulating or simplifying the models is not tampering with the truth. You are always allowed to change the models, as long as the results are within an acceptable range. It is the objective of the modeling that determines the required accuracy: Is it a conceptual study limited to order of magnitude estimations? Or is it design modeling in which you will add 10–25% to the required size in order to allow for inaccuracies in the models and future increase in production? Or is it an academic research work that you will publish with as accurate simulations as possible?
A simulation may contain both errors and uncertainties. An error is defined as a recognizable deficiency that is not due to lack of knowledge, and an uncertainty is a potential deficiency that is due to lack of knowledge. All simulations must be validated and verified in order to avoid errors and uncertainties. Validation and verification are two important concepts in dealing with errors and uncertainties. Validation means making sure that the model describes the real world correctly, and verification is a procedure to ensure that the model has been solved in a correct way.
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg
Differential equations play a dominant role in mathematical modeling. In practical engineering applications, only a very limited number of them can be solved analytically. The purpose of this chapter is to give an introduction to the numerical methods needed to solve differential equations, and to explain how solution accuracy can be controlled and how stability can be ensured by selecting the appropriate methods. The mathematical framework needed to solve both ordinary and partial differential equations is presented. A guideline for selecting numerical methods is presented at the end of the chapter.
Ordinary differential equations
A characteristic of a differential equation is that it involves an unknown function and one or more of the function’s derivatives. If the unknown function depends on only one independent variable, it is classified as an ordinary differential equation (ODE). The order of the differential equation is simply the order of the highest derivative that appears in the equations. Consequently, a first-order ODE contains only first derivatives, whilst a second-order ODE may contain both second and first derivatives. The ODEs can also be classified as linear or non-linear. Linear ODEs are the ones in which all dependent variables and their derivatives appear in a linear form. This implies that they cannot be multiplied or divided by each other, and they must be raised to the power of 1. An ODE has an infinite number of solutions, but with the appropriate conditions that describe systems, i.e. the initial value or the boundary value, the solutions can be determined uniquely.
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg
Many phenomena in engineering are very complex and we do not have sufficient knowledge at the moment to develop a model from first principles; instead, we have to rely on empirical correlations. Today most process development is done using empirical or semi-empirical models. These models are usually accurate and very useful. The drawback is that they are only valid for specific equipment within an experimental domain where the parameters are determined.
In developing a correlation, we need first to identify all the variables that may have an influence on it. There are different approaches to finding important variables. One approach is to formulate the governing equations even if we do not have sufficient knowledge or computer resources to solve the equations. These equations and the corresponding boundary conditions provide information about which variables are important in formulating an empirical correlation. A second approach, used by experienced engineers, is to list all variables that are believed to be important. The final correlation is then obtained by experimenting and model fitting using experimental design to obtain reliable results and to minimize correlations between the parameters in the model.
Anders Rasmuson, Chalmers University of Technology, Gothenberg,Bengt Andersson, Chalmers University of Technology, Gothenberg,Louise Olsson, Chalmers University of Technology, Gothenberg,Ronnie Andersson, Chalmers University of Technology, Gothenberg