Erzeugende Funktionen bei Spline-Interpolation mit äquidistanten Knoten

Using generating functions we obtain in the case ofn+1 equidistant data points a method for the calculation of the interpolating spline functions(x) of degree 2k+1 with boundary conditionss ^(κ) (x₀)=y ₀ ^(κ) ,s ^(κ) (x _n )=y _n ^(κ) , κ=1(1)k, which only needs the inversion of a matrix of orderk. The applicability of our method in the case of general boundary conditions is also mentioned.     

Linear differential and difference systems: EGδ- and EGσ- eliminations

Systems of linear ordinary differential and difference equations of the form $A_r (x)\xi ^r y(x) + \ldots + A_1 (x)\xi y(x) + A_0 (x)y(x) = 0,\xi \in \left\{ {\frac{d} {{dx}},E} \right\}$ , where E is the shift operator, Ey(x) = y(x + 1), are considered. The coefficients A _i (x), i = 0, ..., r, are square matrices of order m, and their entries are polynomials in x over a number field K, with A _r (x) and A ₀(x) being nonzero matrices. The equations are assumed to be independent over K[x, ξ]. For any system S of this form, algorithms EG_δ (in the differential case) and EG_σ (in the difference case) construct, in particular, the l-embracing system $\bar S$ of the same form. The determinant of the leading matrix $\bar A_r (x)$ of this system is a nonzero polynomial, and the set of solutions of system $\bar S$ contains all solutions of system S. (Algorithm EG_δ provides also a number of additional possibilities.) Examples of problems that can be solved with the help of EG_δ and EG_σ are given. The package EG implementing the proposed algorithms in Maple is described.     

An algorithm for exponential integrals of real order

The solution of many problems in radiative transfer and transport theory, requires calculations of real-order exponential integrals,E _s (x). We consider a composite algorithm based on a recursive evaluation ofE _s (x), \(s \in \mathbb{R}\) ,x>0, starting from a suitable initial value in the region of positives andx arguments, which ensures the numerical stability of the involved recurrences.     

Eine stets quadratisch konvergente Modifikation des Steffensen-Verfahrens

It is shown that the following modification of the Steffensen procedurex _n+1=x _n ?k _s (x _n )f(x _n ) (f[x _n ,x _n ?f(x _n )])^?1 (n=0,1,...) withk _s (x)=(1?z _s (x))^?1,z _s (x)=f(x) 2f[x?f(x),x,x+f(x)]×(f[x,x?f(x)])^?2 is quadratically convergent to the root of the equation \(f(x) = (x - \bar x)^p g(x) = 0(p > 0,g(\bar x) \ne 0)\) . Furthermore \(\mathop {\lim }\limits_{n \to \infty } k_s (x_n ) = p\) holds.     

Compact Numerical Quadrature Formulas for Hypersingular Integrals and Integral Equations

In the first part of this work, we derive compact numerical quadrature formulas for finite-range integrals $I[f]=\int^{b}_{a}f(x)\,dx$ , where f(x)=g(x)|x?t| ^?? , ?? being real. Depending on the value of ??, these integrals are defined either in the regular sense or in the sense of Hadamard finite part. Assuming that g??C ^??[a,b], or g??C ^??(a,b) but can have arbitrary algebraic singularities at x=a and/or x=b, and letting h=(b?a)/n, n an integer, we derive asymptotic expansions for ${T}^{*}_{n}[f]=h\sum_{1\leq j\leq n-1,\ x_{j}\neq t}f(x_{j})$ , where x _j =a+jh and t??{x ₁,??,x _n?1}. These asymptotic expansions are based on some recent generalizations of the Euler?CMaclaurin expansion due to the author (A.?Sidi, Euler?CMaclaurin expansions for integrals with arbitrary algebraic endpoint singularities, in Math. Comput., 2012), and are used to construct our quadrature formulas, whose accuracies are then increased at will by applying to them the Richardson extrapolation process. We pay particular attention to the case in which ??=?2 and f(x) is T-periodic with T=b?a and $f\in C^{\infty}(-\infty,\infty)\setminus\{t+kT\}^{\infty}_{k=-\infty}$ , which arises in the context of periodic hypersingular integral equations. For this case, we propose the remarkably simple and compact quadrature formula $\widehat{Q}_{n}[f]=h\sum^{n}_{j=1}f(t+jh-h/2)-\pi^{2} g(t)h^{-1}$ , and show that $\widehat{Q}_{n}[f]-I[f]=O(h^{\mu})$ as h??0 ???>0, and that it is exact for a class of singular integrals involving trigonometric polynomials of degree at most n. We show how $\widehat{Q}_{n}[f]$ can be used for solving hypersingular integral equations in an efficient manner. In the second part of this work, we derive the Euler?CMaclaurin expansion for integrals $I[f]=\int^{b}_{a} f(x)dx$ , where f(x)=g(x)(x?t) ^?? , with g(x) as before and ??=?1,?3,?5,??, from which suitable quadrature formulas can be obtained. We revisit the case of ??=?1, for which the known quadrature formula $\widetilde{Q}_{n}[f]=h\sum^{n}_{j=1}f(t+jh-h/2)$ satisfies $\widetilde{Q}_{n}[f]-I[f]=O(h^{\mu})$ as h??0 ???>0, when f(x) is T-periodic with T=b?a and $f\in C^{\infty}(-\infty,\infty)\setminus\{t+kT\}^{\infty}_{k=-\infty}$ . We show that this formula too is exact for a class of singular integrals involving trigonometric polynomials of degree at most n?1. We provide numerical examples involving periodic integrands that confirm the theoretical results.     

Numerische Lösung von Anfangswertaufgaben gewöhnlicher Differentialgleichungen n-ter Ordnung

In the present paper a new method is given for the numerical treatment of the initial problemsy ⁽ⁿ⁾=f(x,y,y′, ...,y ^(n?1),y ⁽ⁱ⁾ (x _o )=y _o ⁽ⁱ⁾ , i=0, 1, ...,n?1. This method is an one-step process of order four. For a class of linear differential equations the exact solution is obtained. Moreover some numerical results are presented.     

A new method for automatical computation of error bounds for the set of all solutions of nonlinear boundary value problems

We consider nonlinear boundary value problems with arbitrarily many solutionsuεC ² [a, b]. In this paper an Algorithm will be established for a priori bounds \(\bar u,\bar d \in C[a,b]\) with the following properties:

1. For every solutionu of the nonlinear problem we obtain $$\bar u(x) \leqslant u(x) \leqslant \bar u(x), - \bar d(x) \leqslant u'(x) \leqslant \bar d(x)$$ for any,xε[a, b].
2. The bounds \(\bar u\) and % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGabmizayaara% aaaa!36EE!\[\bar d\] are defined by the use of the functions exp, sin and cos.
3. We use neither the knowledge of solutions nor the number of solutions.

     

Quadrature rules for Prandtl's integral equation

In this paper we construct an interpolatory quadrature formula of the type $$\mathop {\rlap{--} \smallint }\limits_{ - 1}^1 \frac{{f'(x)}}{{y - x}}dx \approx \sum\limits_{i = 1}^n {w_{ni} (y)f(x_{ni} )} ,$$ wheref(x)=(1?x)^α(1+x)^β f _o(x), α, β>0, and {x _ni} are then zeros of then-th degree Chebyshev polynomial of the first kind,T _n (x). We also give a convergence result and examine the behavior of the quantity \( \sum\limits_{i = 1}^n {|w_{ni} (y)|} \) asn→∞.     

Symbolic solution of nonhomogeneous linear ordinary differential equations in terms of power series

For a nonhomogeneous linear ordinary differential equation Ly(x) = f(x) with polynomial coefficients and a holonomic right-hand side, a set of points x = a is found where a power series solution $y(x) = \sum\nolimits_{n = 0}^\infty {c_n (x - a)} ^n $ with hypergeometric coefficients exists (starting from some number, the ratio c _{n + 1}/c _n is a rational function of n).     

Algorithms forn-th root approximation

Fromf(x)=x ⁿ ?r and a polynomialQ _p (y)=∑ _i=0 ^p a _i y ⁱ , we consider Newton's method to solveF _p (x)=Q _p (f(x))=0. We obtain convergent iterative methods of orderp+1 to findr ^1/n for arbitraryp.     

Global wire routing in two-dimensional arrays

We examine the problem of routing wires of a VLSI chip, where the pins to be connected are arranged in a regular rectangular array. We obtain tight bounds for the worst-case “channel-width” needed to route ann×n array, and develop provably good heuristics for the general case. Single-turn routings are proved to be near-optimal in the worst-case. A central result of our paper is a “rounding algorithm” for obtaining integral approximations to solutions of linear equations. Given a matrix A and a real vector x, then we can find an integral x such that for alli, ¦x _i -x _i ¦ <1 and (Ax) _i -(Ax) _i <Δ. Our error bound Δ is defined in terms of sign-segregated column sums of A: $$\Delta = \mathop {\max }\limits_j \left( {\max \left\{ {\sum\limits_{i:a_{ij} > 0} {a_{ij} ,} \sum\limits_{i:a_{ij}< 0} { - a_{ij} } } \right\}} \right).$$      

Modeling of subjective judgments made by a researcher-modeler about the model of the research object

Methods are considered for the mathematic modeling of incomplete and unreliable knowledge about the model M(x) of the research object expressed in the form of subjective judgments made by the researcher-modeler (r-m) about the possible values of the unknown parameter x ∈ X defining the model. The mathematical model of subjective judgments is defined as the space (X, P(X), $Pl^{\tilde x} $ , $Bel^{\tilde x} $ ), in which the indeterminate element (i.el.) $\tilde x$ characterizes (as an undefined propositional variable) the subjective judgments made by the r-m about the validity of each value x ∈ X by the values of measures such as the plausibility $Pl^{\tilde x} $ of the equality $\tilde x$ = x, and belief $Bel^{\tilde x} $ in the inequality $\tilde x$ ≠ x. If there are observational data on the subject, available to the r-m he can use them to construct an empirical estimate of the i.el. $\tilde x$ and an empirical model (X, P(X), $Pl^{\tilde x} $ , $Bel^{\tilde x} $ ) of the subjective judgments about possible values of x ∈ X.     

Fine structure of a one-dimensional discrete point system

We consider a system of N points x ₁ < ... < x _N on a segment of the real line. An ideal system (crystal) is a system where all distances between neighbors are the same. Deviation from idealness is characterized by a system of finite differences ? _i ¹ = x _x+1 ? x _i , ? _i ^k+1 = ? _i+1 ^k ? ? _i ^k , for all possible i and k. We find asymptotic estimates as N ?? ??, k????, for a system of points minimizing the potential energy of a Coulomb system in an external field.     

Conservative difference schemes for diffusion problems with boundary and interface conditions

For 0≤x≤1, 0≤t≤T we consider the diffusion equation $$\gamma (x)u_t (x, t) - (B u)_x (x, t) = f(x, t)$$ with (alternatively)B u:=(a(x)u) _x +b(x)u ora(x)u _x +β(x)u. There are given initial valuesu(x,0), influx rates?(B u) (0,t) and (B u) (1,t) across the lateral boundaries and an influx rate (B u) (ζ?0,t)?(B u) (ζ+0,t) at an interface ζ∈(0, 1) where the elsewhere smooth functions γ,a, b, β are allowed to have jump discontinuities.a and γ are assumed to be positive. Interpretingu(x, t) as temperature and γ(x) u (x, t) as energy density we can easily express the total energy \(E(t) = \int\limits_0^1 {\gamma (x) u (x, t)} \) in terms of integrals of the given data. We describe and analyse explicit and implicit one-step difference schemes which possess a discrete quadrature analogue exactly matchingE(t) at the time grid points. These schemes also imitate the isotonic dependence of the solution on the data. Hence stability can be proved by Gerschgorin's method and, under appropriate smoothness assumptions, convergence is 0 ((Δx)²+Δt).     

ε-Optimality for bicriteria programs and its application to minimum cost flows

A subsetS?X of feasible solutions of a multicriteria optimization problem is called ε-optimal w.r.t. a vector-valued functionf:X→Y \( \subseteq \) ? ^K if for allx∈X there is a solutionz _x∈S so thatf _k(z _x)≤(1+ε)f _k (x) for allk=1,...,K. For a given accuracy ε>0, a pseudopolynomial approximation algorithm for bicriteria linear programming using the lower and upper approximation of the optimal value function is given. Numerical results for the bicriteria minimum cost flow problem on NETGEN-generated examples are presented.     

Association Fields via Cuspless Sub-Riemannian Geodesics in SE(2)

To model association fields that underly perceptional organization (gestalt) in psychophysics we consider the problem P _curve of minimizing $\int _{0}^{\ell} \sqrt{\xi^{2} +\kappa^{2}(s)} {\rm d}s $ for a planar curve having fixed initial and final positions and directions. Here κ(s) is the curvature of the curve with free total length ?. This problem comes from a model of geometry of vision due to Petitot (in J. Physiol. Paris 97:265–309, 2003; Math. Inf. Sci. Humaines 145:5–101, 1999), and Citti & Sarti (in J. Math. Imaging Vis. 24(3):307–326, 2006). In previous work we proved that the range $\mathcal{R} \subset\mathrm{SE}(2)$ of the exponential map of the underlying geometric problem formulated on SE(2) consists of precisely those end-conditions (x _fin,y _fin,θ _fin) that can be connected by a globally minimizing geodesic starting at the origin (x _in,y _in,θ _in)=(0,0,0). From the applied imaging point of view it is relevant to analyze the sub-Riemannian geodesics and $\mathcal{R}$ in detail. In this article we

• show that $\mathcal{R}$ is contained in half space x≥0 and (0,y _fin)≠(0,0) is reached with angle π,
• show that the boundary $\partial\mathcal{R}$ consists of endpoints of minimizers either starting or ending in a cusp,
• analyze and plot the cones of reachable angles θ _fin per spatial endpoint (x _fin,y _fin),
• relate the endings of association fields to $\partial\mathcal {R}$ and compute the length towards a cusp,
• analyze the exponential map both with the common arc-length parametrization t in the sub-Riemannian manifold $(\mathrm{SE}(2),\mathrm{Ker}(-\sin\theta{\rm d}x +\cos\theta {\rm d}y), \mathcal{G}_{\xi}:=\xi^{2}(\cos\theta{\rm d}x+ \sin\theta {\rm d}y) \otimes(\cos\theta{\rm d}x+ \sin\theta{\rm d}y) + {\rm d}\theta \otimes{\rm d}\theta)$ and with spatial arc-length parametrization s in the plane $\mathbb{R}^{2}$ . Surprisingly, s-parametrization simplifies the exponential map, the curvature formulas, the cusp-surface, and the boundary value problem,
• present a novel efficient algorithm solving the boundary value problem,
• show that sub-Riemannian geodesics solve Petitot’s circle bundle model (cf. Petitot in J. Physiol. Paris 97:265–309, [2003]),
• show a clear similarity with association field lines and sub-Riemannian geodesics.

     

Formule di quadratura «chiuse» di tipo gaussiano: Tabulzione dei nodi e dei coefficienti

The coefficientsA _hi and the nodesx _mi for «closed” Gaussian-type quadrature formulae $$\int\limits_{ - 1}^1 {f(x)dx = \sum\limits_{h = 0}^{2_8 } {\sum\limits_{i = 0}^{m + 1} {A_{hi} f^{(h)} (x_{mi} ) + R\left[ {f(x)} \right]} } } $$ withx _m0 =?1,x _{m, m+1} =1 andR[f(x)]=0 iff(x) is a polinomial of degree at most2m(s+1)+2(2s+1)?1, have been tabulated for the cases: $$\left\{ \begin{gathered} s = 1,2 \hfill \\ m = 2,3,4,5 \hfill \\ \end{gathered} \right.$$ .     

Space Functions and Space Complexity of the Word Problem in Semigroups

We introduce the space function s(n) of a finitely presented semigroup ${S =\langle A \mid R \rangle}$ . To define s(n) we consider pairs of words w,w′ over A of length at most n equal in S and use relations from R for the derivations ${w = w_0 \rightsquigarrow \dots \rightsquigarrow w_t = w'; s(n)}$ bounds from above the lengths of the words w _i at intermediate steps, i.e., the space sufficient to implement all such transitions ${w \rightsquigarrow \dots \rightsquigarrow w'}$ . One of the results obtained is the following criterion: A finitely generated semigroup S has decidable word problem of polynomial space complexity if and only if S is a subsemigroup of a finitely presented semigroup H with polynomial space function.     

Interpolation that leads to the narrowest intervals and its application to expert systems and intelligent control

In many real-life situations, we want to reconstruct the dependencyy=f(x ₁,…, x_n) from the known experimental resultsx _i ^(k) , y^(k). In other words, we want tointerpolate the functionf from its known valuesy ^(k)=f(x ₁ ^(k) ,…, x _n ^(k) ) in finitely many points $\bar x^{(k)} = (x_1^{(k)} , \ldots ,x_n^{(k)} )$ , 1≤k≤N There are many functions that go through given points. How to choose one of them? The main goal of findingf is to be able to predicty based onx _i. If we getx _i from measurements, then usually, we only getintervals that containx _i. As a result of applyingf, we get an interval y of possible values ofy. It is reasonable to choosef for which the resulting interval is the narrowest possible. In this paper, we formulate this choice problem in mathematical terms, solve the corresponding problem for several simple cases, and describe the application of these solutions to intelligent control.     

A note on the complexity of program evaluation

A simple problem concerning evaluation of programs is shown to be nonelementary recursive. The problem is the following: Given an input-free programP (i.e. all variables are initially 0) without nested loops using only instructions of the formx ← 1, x ← x + y, \(x \leftarrow x\dot - y\) ,do x... end, doesP output 0? This problem has time complexity \(2^{2^{ {\mathinner{\mkern2mu\raise1pt\hbox{.}\mkern2mu \raise4pt\hbox{.}\mkern2mu\raise7pt\hbox{.}\mkern1mu}} ^2 } } \) }cn-levels for some constantc. Other results are presented which show how the complexity of the 0-evaluation problem changes when the nonlooping instructions are varied. For example, it is shown that 0-evaluation is PSPACE-complete even for the case when the nonlooping instructions are onlyx ← x + 1,if x = 0then y ←y \(y \leftarrow y\dot - 1\) .