A Probabilistic Approach to Carne’s Bound

Carne’s bound is a sharp inequality controlling the transition probabilities for a discrete reversible Markov chain (Section 1). Its ordinary proof uses spectral techniques which look as efficient as miraculous. Here we present a new proof, comparing a “drift” for ways “out” and “back”, to get the gaussian part of the bound (Section 2), and using a conditioning technique to get the flight factor (Section 4). Moreover we show how our proof is more “supple” than Carne’s one and may generalize (Section 3.2).      

Noniterative Reconstruction Method for an Inverse Potential Problem Modeled by a Modified Helmholtz Equation

This paper deals with an inverse potential problem posed in two dimensional space whose forward problem is governed by a modified Helmholtz equation. The inverse problem consists in the reconstruction of a set of anomalies embedded into a geometrical domain from partial measurements of the associated potential. Since the inverse problem, we are dealing with, is written in the form of an ill-posed boundary value problem, the idea is to rewrite it as a topology optimization problem. In particular, a shape functional is defined to measure the misfit of the solution obtained from the model and the data taken from the partial measurements. This shape functional is minimized with respect to a set of ball-shaped anomalies using the concept of topological derivatives. It means that the shape functional is expanded asymptotically and then truncated up to the desired order term. The resulting expression is trivially minimized with respect to the parameters under consideration which leads to a noniterative second-order reconstruction algorithm. As a result, the reconstruction process becomes very robust with respect to noisy data and independent of any initial guess. Finally, some numerical experiments are presented to show the effectiveness of the proposed reconstruction algorithm.     

Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach

In this paper, a procedure is presented which allows the optimal reconstruction of images from blurred noisy data. The procedure relies on a general Bayesian approach, which makes proper use of all the available information. Special attention is devoted to the informative content of the edges; thus, a preprocessing phase is included, with the aim of estimating the jump sizes in the gray level. The optimization phase follows; existence and uniqueness of the solution is secured. The procedure is tested against simple simulated data and real data.     

双复合泊松风险模型下的投资问题

在经典风险模型基础上,研究了保险公司保费收入和索赔均服从复合泊松过程的双复合泊松风险模型,针对最优投资策略和求解破产时刻惩罚金期望折现函数的问题,利用重期望公式和马氏性得到期望折现函数满足的带边界条件的二阶积分微分方程,通过高效的Sinc数值方法求出折现函数的近似数值解,从而由图像分析破产概率变化的趋势.     

基于CT图像重建的多重集合分裂可行性问题应用分析

为了较好地应用CQ算法解决稀疏角度CT 图像重建的问题,提出了一种新的实时的分块逐次混合算法．首先将稀疏角度CT 图像重建的重建问题转化成分裂可行性问题．其次,通过分析非空闭凸集C和Q的不同的定义,在N维实空间中分别针对不同的CQ算法给出了7种不同的实现方案．通过试验,分别对不同算法及其方案的重建精度和收敛速度进行了对比分析,并对多重集合分裂可行性问题算法中约束权因子的选取及其对输出的影响进行了研究,从而给出了CQ算法在稀疏角度CT图像重建问题中应用的最佳凸集定义方案．以此为基础,给出了所提出算法的最佳实现方案．试验结果表明,该算法收敛速度快,重建精度高,为多重集合分裂可行性问题及其改进算法在该重建问题上的应用提供了参考．     

一种改进边界元法在弹性扭转问题中的应用

本文用一种改进边界元法分析与计算了椭圆截面等直杆的扭转问题.并与边界元法的解进行比较,其结果极为符合.然而,改进边界元法较边界元法所需要的数据量少得多,计算时间也将大大减少了.因此,本文方法对求解Poisson方程问题是一种经济而行之有效的数值计算方法.     

不适定的部分Dirichlet问题广义解的右逆表示

对于曹伟平,马吉溥给出的部分Dirichlet问题的广义解的定义进行进一步研究,证得该问题的广义解等价于同一偏微分方程的齐次Dirichlet-Neumann混杂问题的弱解,并用算子右逆给出该广义解的表示.     

Novel Linear Image Denoising Approach Based on a Modified Gaussian Filter Kernel

A Gaussian noise reduction technique for grayscale images is proposed in this article. It uses a modified Gaussian filter kernel based on a hyperbolic second-order equation. The introduced mathematical model differs from the classic Gaussian model provided by the heat equations, by a localization property. Our filtering approach reduces the amount of Gaussian noise and also enhances the image contrast. Some image denoising experiments that prove the effectiveness of the proposed method are also described in this article.     

Constructing Solutions of an Ill-Posed Nonlinear Singularly Perturbed Problem for an Equation of Elliptic Type

Mathematical Notes -     

平面中Poisson方程的Dirichlet问题

基于向量旋转内积不变的特点,通过对Green积分公式的推广,得到与空间Green三个公式相似的平面Green公式.从而,得到平面中Poisson方程Robin问题的解和平面中Poisson方程Dirichlet问题的解.     

A Mathematical Programming Approach to Optimal Markovian Switching of Poisson Arrival Streams to Queueing Systems

Motivated by certain situations in manufacturing systems and communication networks, we look into the problem of maximizing the profit in a queueing system with linear reward and cost structure and having a choice of selecting the streams of Poisson arrivals according to an independent Markov chain. We view the system as a MMPP/GI/1 queue and seek to maximize the profits by optimally choosing the stationary probabilities of the modulating Markov chain. We consider two formulations of the optimization problem. The first one (which we call the PUT problem) seeks to maximize the profit per unit time whereas the second one considers the maximization of the profit per accepted customer (the PAC problem). In each of these formulations, we explore three separate problems. In the first one, the constraints come from bounding the utilization of an infinite capacity server; in the second one the constraints arise from bounding the mean queue length of the same queue; and in the third one the finite capacity of the buffer reflect as a set of constraints. In the problems bounding the utilization factor of the queue, the solutions are given by essentially linear programs, while the problems with mean queue length constraints are linear programs if the service is exponentially distributed. The problems modeling the finite capacity queue are non-convex programs for which global maxima can be found. There is a rich relationship between the solutions of the PUT and PAC problems. In particular, the PUT solutions always make the server work at a utilization factor that is no less than that of the PAC solutions.     

Continuous time shock markov decision processes with discounted criterion

This paper presents a new concept of Markov decision processes: continuous time shock Markov decision processes, which model Markovian controlled systems sequentially shocked by its environment. Between two adjacent shocks, the system can be modeled by continuous time Markov decision processes. But according to each shock, the system's parameters are changed and an instantaneous state transition occurs. After presenting the model, we prove that the optimality equation, which consists of countable equations, has a unique solution in some function space Ω     

A Probabilistic Approach to the Problem of the Defects of Admissible Sets in a Lattice

New estimates for the defect of the admissible set in a lattice are obtained for a sufficiently large class of sequences.     

求解一个整数方程的新解法

ni=1aixi =p是一个由实验数据问题抽象而出的整数方程求非负整数解的数学模型 .为了使该问题实现计算机求解的可能 ,本文首先将原问题转化为讨论一类整数规划最优解问题 .从对应松弛规划问题的目标函数值为 0的最优解出发 ,根据舍入凑整法原则 ,再次将问题转化为另一简化后的整数方程 ,这样大大缩小了解的范围 ,及进一步迅速降低了方程右端的 p值 ,使其在计算机上求解的运算量大大降低而能得以实现     

High-Resolution Color Image Reconstruction with Neumann Boundary Conditions

This paper studies the application of preconditioned conjugate gradient methods in high-resolution color image reconstruction problems. The high-resolution color images are reconstructed from multiple undersampled, shifted, degraded color frames with subpixel displacements. The resulting degradation matrices are spatially variant. To capture the changes of reflectivity across color channels, the weighted H ₁ regularization functional is used in the Tikhonov regularization. The Neumann boundary condition is also employed to reduce the boundary artifacts. The preconditioners are derived by taking the cosine transform approximation of the degradation matrices. Numerical examples are given to illustrate the fast convergence of the preconditioned conjugate gradient method.     

Self-Interacting Markov Chains

Abstract

In this article we study a class of self-interacting Markov chain models. We propose a novel theoretical basis based on measure-valued processes and semigroup techniques to analyze its asymptotic behavior as the time parameter tends to infinity. We exhibit different types of decays to equilibrium, depending on the level of interaction. We illustrate these results in a variety of examples, including Gaussian or Poisson self-interacting models. We analyze the long-time behavior of a new class of evolutionary self-interacting chain models. These genetic type algorithms can also be regarded as reinforced stochastic explorations of an environment with obstacles related to a potential function.     

On an Operator Theory Approach to the Corona Problem

This paper deals with an operator theory approach to the coronaconjecture for H(Dⁿ). Treil gave a counter-example to this conjecturein the case where n = 1 for operator-valued functions; thusone might hope to use this to disprove the corona conjecturefor H(Dⁿ) (for n 2). This paper shows that this natural approachtowards a negative answer fails. On the other hand, the secondresult here shows that ‘commutant lifting’ cannotbe true for more than two contractions for any constant. Thisobstructs a natural attempted proof of the corona conjecturefor H(Dⁿ) (for n 2) by our previous result.     

利用对偶树复数小波与全变差模型实现图像去噪的新方法

本文首先研究了一种三层小波系数相关萎缩的概念与性质,利用对偶树复数小波与全变差模型相结合,提出了一种新的图像去噪方法。实验结果表明,与现有的图像去噪方法相比,本文方法无论是在视觉还是在均方误差等方面均有更好的效果。     

Fast convergence to state-action frequency polytopes for MDPs

In the context of finite weakly communicating Markov Decision Processes, we tackle the problem of fast convergence of state-action frequency vectors to the polytope of stationary distributions on state-action frequencies. Using unichain policies, we derive bounds on the speed of convergence which are independent of the limit points.     

一类半线性椭圆方程的概率解法(英)

设D为Rd（d≥3）内的无界区域，本文以超布朗运动为工具证明了下述这值问题解的存在性和唯一性．