求解矩阵方程AXB+CXD=F参数迭代法的最优参数分析

本文针对求矩阵方程AXB+CXD=F唯一解的参数迭代法，分析当矩阵A，B，C，D均是Hermite正（负）定矩阵时，迭代矩阵的特征值表达式，给出了最优参数的确定方法，并提出了相应的加速算法.     

σ2 Hessian方程的Pogorelov型C2 内估计及应用

提出利用拉格朗日乘子法重新证明σ2算子的最优凹性,并定义了一个凸锥Γ3?=λ=(λ1,λ2,?,λn)∈Rn:σ1(λ)>0,σ2(λ|i)>0,1≤i≤n。利用σ2算子的最优凹性，给出了σ2Hessian方程Pogorelov型C2内估计，进而证明了σ2(D2u(x))=1,x∈Rn的满足二次多项式增长条件的Γ3?-凸整解为二次多项式。     

Free boundary problem for a fully nonlinear and degenerate parabolic equation in an angular domain

This paper concerns with the problem of how to running an insurance company to maximize his total discounted expected dividends. In our model, the dividend rate is limited in $[0,M]$ and the company is allowed to transfer any proportion of risk by reinsuring. So there are two strategies which we call dividend strategy and reinsurance strategy. The objective function and the corresponding optimal two strategies are the solution and the two free boundaries of the following Barenblatt parabolic equation

$v_t?\underset{0\le a\le 1}{\max }?(\frac{1}{2}{a}^2{\sigma }^2{v}_{xx}+a\mu v_x)+cv?\underset{0\le l\le M}{\max }?[(1?v_x)l]=0$

under certain boundary conditions on an angular domain

$Q_T=\{(x,t)|0<x<Mt,0<t\le T\}.$

The main effort is to analyze the properties of the solution and the free boundaries to show the optimal decision for the insurance company.     

The Riemann–Hilbert analysis to the Pollaczek–Jacobi type orthogonal polynomials

In this paper, we study polynomials orthogonal with respect to a Pollaczek–Jacobi type weight The uniform asymptotic expansions for the monic orthogonal polynomials on the interval (0,1) and outside this interval are obtained. Moreover, near , the uniform asymptotic expansion involves Airy function as , and Bessel function of order α as in the neighborhood of , the uniform asymptotic expansion is associated with Bessel function of order β as . The recurrence coefficients and leading coefficient of the orthogonal polynomials are expressed in terms of a particular Painlevé III transcendent. We also obtain the limit of the kernel in the bulk of the spectrum. The double scaled logarithmic derivative of the Hankel determinant satisfies a σ‐form Painlevé III equation. The asymptotic analysis is based on the Deift and Zhou's steepest descent method.     

On pointwise decay rates of time‐periodic solutions to the Navier–Stokes equation

We study the existence of a time‐periodic solution with pointwise decay properties to the Navier–Stokes equation in the whole space. We show that if the time‐periodic external force is sufficiently small in an appropriate sense, then there exists a time‐periodic solution $\{u,p\}$ of the Navier–Stokes equation such that $|{?}^j{u(t,x)|=O(|x|}^{1?n?j})$ and $|{?}^j{p(t,x)|=O(|x|}^{?n?j})(j=0,1,\dots )$ uniformly in $t\in \mathbb{R}$ as $|x|\to \infty$. Our solution decays faster than the time‐periodic Stokes fundamental solution and the faster decay of its spatial derivatives of higher order is also described.     

Asymptotic growth bounds for the 3‐D Vlasov‐Poisson system with point charges

In this article, we consider the asymptotic behavior of the classical solution to the 3‐dimensional Vlasov‐Poisson plasma interacting repulsively with N point charges. The large time behavior in terms of diameters of its velocity‐spatial supports is improved to O(t^2/3+ϵ) for any ϵ>0.     

Morris-Lecar系统中通道噪声诱导的自发性动作电位研究

在生物物理学中, 越来越多的现象是由于分段确定性的动力系统与连续时间马氏过程之间的耦合作用而产生的. 因为这种耦合性, 相关的数学模型更适合取为随机混合系统而不是扩散过程(基于It?随机微分方程). 本文从理论上和数值上研究了在弱噪声条件下无鞍点状态的随机混合Morris-Lecar系统中, 由通道噪声诱导的自发性放电现象. 一个动作电位的初始阶段可视为噪声诱导的逃逸事件, 其最优路径和拟势可由辅助Hamilton系统给出. 由于系统不存在鞍点, 因此可选择虚拟分界线(ghost separatrix)为阈值, 研究噪声诱导的自静息态的逃逸事件. 通过计算在阈值处的拟势, 便可发现其值有一个明显的最小值, 其作用类似于鞍点. 通过改进的Monte Carlo模拟方法, 计算了历程概率分布, 其结果对初始阶段和兴奋阶段的理论解均给出了验证. 此外, 基于前人将拟势等高线作为阈值的另一种选择, 我们对两种阈值取法的优劣性进行了比较. 最后, 本文研究了钠离子和钾离子通道噪声的不同组合对最优路径和拟势的影响. 结果表明: 钾离子通道噪声在自发性放电过程中起主导作用, 且两种噪声强度存在一个最优比例能使总的噪声强度达到最小.      

估计感度分布中位数的两阶段优化序贯试验设计方法研究

感度试验在爆炸科学领域有非常广泛的应用,其中位数是含能材料的一个关键性能指标.高效估计感度分布中位数是实际工程应用中的重要问题.文章提出一种两阶段的优化序贯试验设计方法来估计感度分布的中位数,并将正交设计与两阶段优化序贯试验设计方法相结合给出了筛选影响含能材料安定性重要因子的试验设计方法.广泛的模拟比较验证了所提方法在小样本情况下可以获得感度分布中位数的准确估计,并且对模型假设和初始参数猜测具有一定的稳健性.当因子效应不是太小的时候,因子筛选方法可以准确地筛选出有显著影响的因子.