Lévy density estimation via information projection onto wavelet subspaces

This paper proposes a nonparametric method for producing smooth and positive estimates of the density of a Lévy process, which is widely used in mathematical finance. We use the method of logwavelet density estimation to estimate the Lévy density with discretely sampled observations. Since Lévy densities are not necessarily probability densities, we introduce a divergence measure similar to Kullback–Leibler information to measure the difference between two Lévy densities. Rates of convergence are established over Besov spaces.     

Sup-norm convergence rates for Lévy density estimation

In this paper, we consider projection estimates for Lévy densities in high-frequency setup. We give a unified treatment for different sets of basis functions and focus on the asymptotic properties of the maximal deviation distribution for these estimates. Our results are based on the idea to reformulate the problems in terms of Gaussian processes of some special type and to further analyze these Gaussian processes. In particular, we construct a sequence of excursion sets, which guarantees the convergence of the deviation distribution to the Gumbel distribution. We show that the exact rates of convergence presented in previous articles on this topic are logarithmic and construct the sequence of accompanying laws, which approximate the deviation distribution with polynomial rate.     

The square root Diffie–Hellman problem

Many cryptographic schemes are based on computationally hard problems. The computational Diffie–Hellman problem is the most well-known hard problem and there are many variants of it. Two of them are the square Diffie–Hellman problem and the square root Diffie–Hellman problem. There have been no known reductions from one problem to the other in either direction. In this paper we show that these two problems are polynomial time equivalent under a certain condition. However, this condition is weak, and almost all of the parameters of cryptographic schemes satisfy this condition. Therefore, our reductions are valid for almost all cryptographic schemes.     

Paley–Wiener–Schwartz type theorem for the wavelet transform

In the present paper, we discuss about extension of the wavelet transform on distribution space of compact support and develop the Paley–Wiener–Schwartz type theorem for the wavelet transform on the same. Furthermore, Paley–Wiener–Schwartz type theorem for the wavelet transform is also established using the relation between the wavelet transform and double Fourier transform.     

The convergence of a new parallel algorithm for the Navier–Stokes equations

This paper deals with the convergence and stability of a new parallel algorithm and the error estimates for a particular case of the new parallel algorithm, which is used to solve the incompressible nonstationary Navier–Stokes equations. The theoretical results show that the scheme is (at least) conditionally stable and convergent.     

Birnbaum–Saunders power-exponential kernel density estimation and Bayes local bandwidth selection for nonnegative heavy tailed data

In this paper, we study the performance of the Birnbaum–Saunders-power-exponential (BS-PE) kernel and Bayesian local bandwidth selection in the context of kernel density estimation for nonnegative heavy tailed data. Our approach considers the BS-PE kernel estimator and treats locally the bandwidth h as a parameter with prior distribution. The posterior density of h at each point x (point where the density is estimated) is derived in closed form, and the Bayesian bandwidth selector is obtained by using popular loss functions. The performance evaluation of this new procedure is carried out by a simulation study and real data in web-traffic. The proposed method is very quick and very competitive in comparison with the existing global methods, namely biased cross-validation and unbiased cross-validation.     

The Bohnenblust–Spitzer algorithm and its applications

The familiar bijections between the representations of permutations as words and as products of cycles have a natural class of “data driven” extensions that permit us to use purely combinatorial means to obtain precise probabilistic information about the geometry of random walks. In particular, we show that the algorithmic bijection of Bohnenblust and Spitzer can be used to obtain means, variances, and concentration inequalities for several random variables associated with a random walk including the number of vertices and length of the convex minorant, concave majorant, and convex hull.     

A primal–dual algorithm for risk minimization

Mathematical Programming - In this paper, we develop an algorithm to efficiently solve risk-averse optimization problems posed in reflexive Banach space. Such problems often arise in many practical...     

The root class residuality of Baumslag–Solitar groups

Given a homomorphically closed root class K of groups, we find a criterion for a Baumslag–Solitar group to be a residually K-group. In particular, we establish that all Baumslag–Solitar groups are residually soluble and a Baumslag–Solitar group is residually finite soluble if and only if it is residually finite.     

Peterson–Gorenstein–Zierler algorithm for skew RS codes

We design a non-commutative version of the Peterson–Gorenstein–Zierler decoding algorithm for a class of codes that we call skew RS codes. These codes are left ideals of a quotient of a skew polynomial ring, which endow them of a sort of non-commutative cyclic structure. Since we work over an arbitrary field, our techniques may be applied both to linear block codes and convolutional codes. In particular, our decoding algorithm applies for block codes beyond the classical cyclic case.     

Tower-of-sets analysis for the Kise–Ibaraki–Mine algorithm

Kise et al. (Oper. Res. 26:121–126, 1978) give an O(n ²) time algorithm to find an optimal schedule for the single-machine number of late jobs problem with agreeable job release dates and due dates. Li et al. (Oper. Res. 58:508–509, 2010a) point out that their proof of optimality for their algorithm is incorrect by giving a counter-example. In this paper, using the concept of “tower-of-sets” from Lawler (Math. Comput. Model. 20:91–106, 1994), we construct the tower-of-sets of the early job set generated by the algorithm. Then we give a correct proof of optimality for the algorithm and show a new result that the early job set by the algorithm obtains not only the maximum number of jobs but also the smallest total processing time among all optimal schedules. The result can be applied to study the problems of the hard real-time systems.     

The Moore–Penrose inverse of block magic rectangles

As is known, a semi-magic square is an n?×?n matrix having the sum of entries in each row and each column equal to a constant. This note generalizes this notion and introduce a special class of block matrices called block magic rectangles. It is proved that the Moore–Penrose inverse of a block magic rectangle is also a block magic rectangle.     

A projected–gradient interior–point algorithm for complementarity problems

Interior–point algorithms are among the most efficient techniques for solving complementarity problems. In this paper, a procedure for globalizing interior–point algorithms by using the maximum stepsize is introduced. The algorithm combines exact or inexact interior–point and projected–gradient search techniques and employs a line–search procedure for the natural merit function associated with the complementarity problem. For linear problems, the maximum stepsize is shown to be acceptable if the Newton interior–point search direction is employed. Complementarity and optimization problems are discussed, for which the algorithm is able to process by either finding a solution or showing that no solution exists. A modification of the algorithm for dealing with infeasible linear complementarity problems is introduced which, in practice, employs only interior–point search directions. Computational experiments on the solution of complementarity problems and convex programming problems by the new algorithm are included.     

An adaptive algorithm for the Thomas–Fermi equation

A free boundary value problem is introduced to approximate the original Thomas–Fermi equation. The unknown truncated free boundary is determined iteratively. We transform the free boundary value problem to a nonlinear boundary value problem defined on [0,1]. We present an adaptive algorithm to solve the problem by means of the moving mesh finite element method. Comparison of our numerical results with those obtained by other approaches shows high accuracy of our method.     

Optimal space–time adaptive wavelet methods for degenerate parabolic PDEs

We analyze parabolic PDEs with certain type of weakly singular or degenerate time-dependent coefficients and prove existence and uniqueness of weak solutions in an appropriate sense. A localization of the PDEs to a bounded spatial domain is justified. For the numerical solution a space?Ctime wavelet discretization is employed. An optimality result for the iterative solution of the arising systems can be obtained. Finally, applications to fractional Brownian motion models in option pricing are presented.     

A Lagrange–Newton algorithm for sparse nonlinear programming

Mathematical Programming - The sparse nonlinear programming (SNP) problem has wide applications in signal and image processing, machine learning and finance, etc. However, the computational...