On the growth factor in Gaussian elimination for generalized Higham matrices

A Higham matrix is a complex symmetric matrix A=B+iC, where both B and C are real, symmetric and positive definite. We prove that, for such A, the growth factor in Gaussian elimination is less than 3. Moreover, a slightly larger bound holds true for a broader class of complex matrices A=B+iC, where B and C are Hermitian and positive definite. Copyright © 2002 John Wiley & Sons, Ltd.     

Gaussian factor models futures and forward prices

Email: hyndman{at}mathstat.concordia.ca Received on 31 July 2006. Accepted on 19 March 2007. We completely characterize the futures price and forward priceof a risky asset (commodity) paying a stochastic dividend yield(convenience yield). The asset (commodity) price is modelledas an exponential affine function of a Gaussian factors process,while the interest rate and dividend yield are affine functionsof the factors process. The characterization we provide is basedon the method of stochastic flows. We believe this method leadsto simpler and more clear-cut derivations of the futures priceand forward price formulae than alternative methods. Hedginga long-term forward contract with shorter term futures contractsand bonds is also examined.     

Bounding the growth factor in Gaussian elimination for Buckley's class of complex symmetric matrices

A Buckley matrix is an n × n complex symmetric matrix A = I _n + iC, where C is real symmetric positive definite. We prove that, for such A the growth factor in Gaussian elimination is not greater than $${1 + \sqrt{17} \over 4} \simeq 1.28078\ldots$$ Copyright © 2000 John Wiley & Sons, Ltd.     

A Modification of Hermite Sampling with a Gaussian Multiplier

The Hermite sampling series is used to approximate bandlimited functions. In this article, we introduce two modifications of Hermite sampling with a Gaussian multiplier to approximate bandlimited and non-bandlimited functions. The convergence rate of those modifications is much higher than the convergence rate of Hermite sampling. Based on complex analysis, we establish some error bounds for approximating different classes of functions by these modifications. Theoretically and numerically, we demonstrate that the approximation by these modifications is highly efficient.     

Identification of source term for the Rayleigh‐Stokes problem with Gaussian random noise

In this paper, we study an inverse source problem for the Rayleigh‐Stokes problem for a generalized second‐grade fluid with a fractional derivative model. The problem is severely ill‐posed in the sense of Hadamard. To regularize the unstable solution, we apply a general filter method for constructing regularized solution, and the convergence rate of this method also has been investigated.     

Stability of a Pivoting Strategy for Parallel Gaussian Elimination

Gaussian elimination with partial pivoting achieved by adding the pivot row to the kth row at step k, was introduced by Onaga and Takechi in 1986 as means for reducing communications in parallel implementations. In this paper it is shown that the growth factor of this partial pivoting algorithm is bounded above by _n <#60; 3 ^n–1, as compared to 2 ^n–1 for the standard partial pivoting. This bound _n, close to 3 ^n–2, is attainable for class of near-singular matrices. Moreover, for the same matrices the growth factor is small under partial pivoting.This revised version was published online in October 2005 with corrections to the Cover Date.     

Gaussian estimates for elliptic operators with unbounded drift

We consider a strictly elliptic operator

     

Reduced-Load Equivalence for Queues with Gaussian Input

In this note, we consider a queue fed by a number of independent heterogeneous Gaussian sources. We study under what conditions a reduced load equivalence holds, i.e., when a subset of the sources becomes asymptotically dominant as the buffer size increases. For this, recent results on extremes of Gaussian processes [6] are combined with de Haan theory. We explain how the results of this note relate to square root insensitivity and moderately heavy tails.The research was supported by the Netherlands Organization for Scientific Research (NWO) under grant 631.000.002.This revised version was published online in June 2005 with corrected coverdate     

Gaussian elimination is stable for the inverse of a diagonally dominant matrix

Let be a row diagonally dominant matrix, i.e.,

where with We show that no pivoting is necessary when Gaussian elimination is applied to Moreover, the growth factor for does not exceed The same results are true with row diagonal dominance being replaced by column diagonal dominance.

     

Some error expansions for Gaussian quadrature

Complex-variable methods are used to obtain some expansions in the error in Gaussian quadrature formulae over the interval [– 1, 1]. Much of the work is based on an approach due to Stenger, and both circular and elliptical contours are used. Stenger's theorem on monotonicity of convergence of Gaussian quadrature formulae is generalized, and a number of error bounds are obtained.     

A General Lower Bound for Gaussian Chaos

We give in full generality a lower bound for the expected value of the supremum of a chaos process indexed by a set T, in terms of the structure of T for the two natural distances induced by the process.     

Gaussian quadratures for approximate of fuzzy integrals

In this paper the integration formulas of Gaussian methods with positive coefficient for fuzzy integrations are discussed and then are followed by convergence theorem. The proposed algorithms are illustrated and compared with Newton Cot’s methods [T. Allahviranloo, Newton Cot’s methods for integration of fuzzy functions, Appl. Math. Comp., in press] by solving some numerical examples.     

Error estimates for approximate approximations with Gaussian kernels on compact intervals

The aim of this paper is the investigation of the error which results from the method of approximate approximations applied to functions defined on compact intervals, only. This method, which is based on an approximate partition of unity, was introduced by Maz’ya in 1991 and has mainly been used for functions defined on the whole space up to now. For the treatment of differential equations and boundary integral equations, however, an efficient approximation procedure on compact intervals is needed.In the present paper we apply the method of approximate approximations to functions which are defined on compact intervals. In contrast to the whole space case here a truncation error has to be controlled in addition. For the resulting total error pointwise estimates and L₁-estimates are given, where all the constants are determined explicitly.     

Inequalities for the Gaussian hypergeometric function

we study the monotonicity of certain combinations of the Gaussian hypergeometric functions F(-1/2,1/2;1;1- xc) and F(-1/2- δ,1/2 + δ;1;1- xd) on(0,1) for given 0 c 5d/6 ∞ andδ∈(-1/2,1/2),and find the largest value δ1 = δ1(c,d) such that inequality F(-1/2,1/2;1;1- xc) F(-1/2- δ,1/2 + δ;1;1- xd) holds for all x ∈(0,1). Besides,we also consider the Gaussian hypergeometric functions F(a- 1- δ,1- a + δ;1;1- x3) and F(a- 1,1- a;1;1- x2) for given a ∈ [1/29,1) and δ∈(a- 1,a),and obtain the analogous results.     

A mixture-type limit theorem for nonlinear functions of Gaussian sequences

We show that, for a certain class of nonlinear functions of Gaussian sequences, the limiting distribution of normalized sums of the nonlinear function values of a sequence is the convolution of a Gaussian distribution with another non-Gaussian distribution.     

Dynamic systems identification with Gaussian processes

This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.     

Zero-one laws for polynomials in Gaussian random variables: A simple proof

Zero-one laws for polynomials in Gaussian random variables have already been studied.⁽⁷⁾ They are established here by very simple arguments: Fubini's theorem and the rotational invariance of centered Gaussian measures. The proof is built on the Polarization formula that has received much attention in Refs. 1 and 5. Our point of view derives from the deep work of Borell.⁽²⁾ In a natural way, these results extend to finite-order Gaussian chaos processes.     

Comparison results for the lower tail of Gaussian seminorms

Let =( _n ) be i.i.d.N(0, 1) random variables andq(x), q(x):R [0, ) be seminorms. We investigate necessary and sufficient conditions that the ratio ofP(q()<) andP(q()<) goes to a positive constant as 0⁺. We give satisfactory answers forl ₂-norms and also some results for sup-norms andl _p-norms. Some applications are given to the rate of escape of infinite dimensional Brownian motion, and we give the lower tail of the Ornstein-Uhlenbeck process and a weighted Brownian bridge under theL ₂-norms.     

多参数平稳增量高斯过程的若干极限结果

该文通过高斯过程的尾概率估计和Slepian引理,在较弱的条件下,研究了相当一般的平稳增量高斯过程的极限性质,得到的结果推广了已有文献中类似的结果．如文献[1]和[2]的结果．     

On the Robustness of Gaussian Elimination with Partial Pivoting

It has been recently shown that large growth factors might occur in Gaussian Elimination with Partial Pivoting (GEPP) also when solving some plausibly natural systems. In this note we argue that this potential problem could be easily solved, with much smaller risk of failure, by very small (and low cost) modifications of the basic algorithm, thus confirming its inherent robustness. To this end, we first propose an informal model with the goal of providing further support to the comprehension of the stability properties of GEPP. We then report the results of numerical experiments that confirm the viewpoint embedded in the model. Basing on the previous observations, we finally propose a simple scheme that could be turned into (even more) accurate software for the solution of linear systems.