马尔科夫过程的强遍历性和一致衰减性

本文证明了黎曼流形上的非爆炸正Harris常返扩散过程的强遍历性等价于某(任)一紧集击中时期望的一致有界性;而马尔科夫过程一致衰减当且仅当爆炸时的期望一致有界.     

带马氏切换的马氏过程的指数收敛速度

设 (X(t), Z(t))是以[0,∞) ×{1, 2,…, n₀} 为状态空间的强马氏过程, 其第一分量 X(t) 依赖于第二分量 Z(t), 而第二分量 Z(t) 是一个马氏链. 应用耦合方法, 估计了(X(t), Z(t)) 的转移概率依全变差范数收敛于其不变概率测度的指数收敛速度.     

Markov Skeleton Processes and Applications to Queueing Systems

In this paper, we apply the backward equations of Markov skeleton processes to qucueing systems. The transient distribution of the waiting time of a GI/G/1 queueing system, the transient distribution of the length of a GI/G/N queueing system and the transient distribution of the length of queueing networks are obtained.     

Stochastic Maximum Principle for Forward-Backward Regime Switching Jump Diffusion Systems and Applications to Finance

The authors prove a sufficient stochastic maximum principle for the optimal control of a forward-backward Markov regime switching jump diffusion system and show its connection to dynamic programming principle. The result is applied to a cash flow valuation problem with terminal wealth constraint in a financial market. An explicit optimal strategy is obtained in this example.     

Gaussian Scenario for the Heat Equation with Quadratic Potential and Weakly Dependent Data with Applications

For a suitable scaling of the solution to the one-dimensional heat equation with spatial-dependent coefficients and weakly dependent random initial conditions, the convergence to the Gaussian limiting distribution is proved. The scaling proposed and methodology followed allow us to obtain Gaussian scenarios for related equations such as the one-dimensional Burgers equation as well as for the multidimensional formulation of both the heat and Burgers equations. Furthermore, the investigation of non-Gaussian scenarios is opened with a different proposed scaling, proving the convergence of the second-order moments.      

Conic Linear Programming Duals for Classes of Quadratic Semi-Infinite Programs with Applications

Journal of Optimization Theory and Applications - In this paper, we first present strong conic linear programming duals for convex quadratic semi-infinite problems with linear constraints and...     

随机系数下线性二次控制问题的最优性条件及其应用*

本文旨在研究随机系数下随机微分方程的线性二次最优控制问题.本文从闭环最优控制/策略存在的必要性条件的角度开展研究. 若闭环最优控制/策略存在, 得到其显示反馈表示、带伪逆运算的倒向随机Riccati方程的适定性及不同系数间满足的一些本质性条件. 此处结论本质地推广和改进了文[Ait Rami M, Moore J, Zhou X. Indefinite stochastic linear quadratic control and generalized differential Riccati equation [J]. {\it SIAM J Control Optim,} 2001, 40:1296--1311;Sun J, Yong J. Linear quadratic stochastic differential games: open-loop and closed-loop saddle points [J]. {\it SIAM J Control Optim,} 2014, 52:4082--4121;L\"{u} Q, Wang T, Zhang X. Characterization of optimal feedback for stochastic linear quadratic control problems,Probab Uncertain Quant Risk, 2017, 2017, 2:11, DOI 10.1186/s41546-017-0022-7]的相应结论.此外, 本文得到了一个关于倒向随机Riccati方程和二阶伴随方程两类方程适应解之间的微妙关系. 注意到,这一结论在现有文献中首次出现. 最后, 本文讨论了在均值方差对冲问题中的应用.     

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Credit valuation adjustment is the price adjustment of financial contract considering possible default of counterparty and it is an important way to measure counterparty risk. It is the key to establish a reasonable default dependence structure model. We introduce an economic state variable and shot noise processes in a Markov copula model and establish a regime switching Markov copula model with shot noise, where we can not only describe the impact of common economic conditions characteristics but also describe the credit name's characteristic. In this proposed model, we study martingale property of the model and the collateralized CVA of credit default swaps, and furthermore, we perfer some numerical calculations on the collateralized CVA and examine the impact of some model parameters on the CVA.     

Controlled Markov Chains with Weak and Strong Interactions: Asymptotic Optimality and Applications to Manufacturing

This paper deals with the asymptotic optimality of a stochastic dynamic system driven by a singularly perturbed Markov chain with finite state space. The states of the Markov chain belong to several groups such that transitions among the states within each group occur much more frequently than transitions among the states in different groups. Aggregating the states of the Markov chain leads to a limit control problem, which is obtained by replacing the states in each group by the corresponding average distribution. The limit control problem is simpler to solve as compared with the original one. A nearly-optimal solution for the original problem is constructed by using the optimal solution to the limit problem. To demonstrate, the suggested approach of asymptotic optimal control is applied to examples of manufacturing systems of production planning.     

New Central Limit Theorems for Functionals of Gaussian Processes and their Applications

As a consequence of the seminal work of Nualart and Peccati in 2005 we have new central limit theorems for functional of Gaussian processes that have allowed us to elucidate the asymptotic behavior of the multipower variation of certain ambit processes, see Barndorff-Nielsen et al. (2009c). This survey intends to explain the role of the Malliavin calculus to reach these results.     

Large Deviation Theorems for Gaussian Processes and Their Applications in Information Theory

We discuss on the large deviation theorems for stationary Gaussian processes and their applications in information theory. The topics investigated here include error probability of string matching, error probabilities for random codings, and a conditional limit theorem which justifies the maximum entropy principle.     

具有Markov调制的随机种群系统半驯服Euler法的指数稳定性

根据半驯服Euler法讨论了具有Markov调制的随机年龄结构种群系统的数值解. 在非局部Lipschitz条件下, 利用~Burkholder-Davis-Gundy~不等式、It\^{o} 公式和~Gronwall~引理, 证明了半驯服Euler数值解不仅强收敛阶数为~0.5, 而且这种方法在时间步长一定的条件下有很好的均方指数稳定性. 最后通过数值例子对所给的结论进行了验证.     

Mixture Dynamics and Regime Switching Diffusions with Application to Option Pricing

In this paper we present a class of regime switching diffusion models described by a pair $(X(t), Y(t)) \in \mathbb{R}^n \times {\cal S}In this paper we present a class of regime switching diffusion models described by a pair (X(t), Y(t)) ? \mathbbRⁿ ×S(X(t), Y(t)) \in \mathbb{R}^n \times {\cal S}, S = {1,2,?, N }{\cal S} = \{1,2,\ldots, N \}, Y(t) being a Markov chain, for which the marginal probability of the diffusive component X(t) is a given mixture. Our main motivation is to extend to a multivariate setting the class of mixture models proposed by Brigo and Mercurio in a series of papers. Furthermore, a simple algorithm is available for simulating paths through a thinning mechanism. The application to option pricing is considered by proposing a mixture version for the Margrabe Option formula and the Heston stochastic volatility formula for a plain vanilla.     

Tensor-Free Proximal Methods for Lifted Bilinear/Quadratic Inverse Problems with Applications to Phase Retrieval

Foundations of Computational Mathematics - We propose and study a class of novel algorithms that aim at solving bilinear and quadratic inverse problems. Using a convex relaxation based on tensorial...     

Exponential and Strong Ergodicity for Markov Processes with an Application to Queues

For an ergodic continuous-time Markov process with a particular state in its space,the authors provide the necessary and sufficient conditions for exponential and strong ergodicity in terms of the moments of the first hitting time on the state.An application to the queue length process of M/G/1 queue with multiple vacations is given.     

Convexity Properties Associated with Nonconvex Quadratic Matrix Functions and Applications to Quadratic Programming

We establish several convexity results which are concerned with nonconvex quadratic matrix (QM) functions: strong duality of quadratic matrix programming problems, convexity of the image of mappings comprised of several QM functions and existence of a corresponding S-lemma. As a consequence of our results, we prove that a class of quadratic problems involving several functions with similar matrix terms has a zero duality gap. We present applications to robust optimization, to solution of linear systems immune to implementation errors and to the problem of computing the Chebyshev center of an intersection of balls. This research was partially supported by the Israel Science Foundation under Grant ISF 489/06.     

Control Improvement for Jump-Diffusion Processes with Applications to Finance

We consider stochastic control problems with jump-diffusion processes and formulate an algorithm which produces, starting from a given admissible control π, a new control with a better value. If no improvement is possible, then π is optimal. Such an algorithm is well-known for discrete-time Markov Decision Problems under the name Howard’s policy improvement algorithm. The idea can be traced back to Bellman. Here we show with the help of martingale techniques that such an algorithm can also be formulated for stochastic control problems with jump-diffusion processes. As an application we derive some interesting results in financial portfolio optimization.     

Quadratic Majorization for Nonconvex Loss with Applications to the Boosting Algorithm

Classical robust statistical methods dealing with noisy data are often based on modifications of convex loss functions. In recent years, nonconvex loss-based robust methods have been increasingly popular. A nonconvex loss can provide robust estimation for data contaminated with outliers. The significant challenge is that a nonconvex loss can be numerically difficult to optimize. This article proposes quadratic majorization algorithm for nonconvex (QManc) loss. The QManc can decompose a nonconvex loss into a sequence of simpler optimization problems. Subsequently, the QManc is applied to a powerful machine learning algorithm: quadratic majorization boosting algorithm (QMBA). We develop QMBA for robust classification (binary and multi-category) and regression. In high-dimensional cancer genetics data and simulations, the QMBA is comparable with convex loss-based boosting algorithms for clean data, and outperforms the latter for data contaminated with outliers. The QMBA is also superior to boosting when directly implemented to optimize nonconvex loss functions. Supplementary material for this article is available online.     

Averaging in Markov Models with Fast Markov Switches and Applications to Queueing Models

An approximation of Markov type queueing models with fast Markov switches by Markov models with averaged transition rates is studied. First, an averaging principle for two-component Markov process (x _n (t), _n (t)) is proved in the following form: if a component x _n () has fast switches, then under some asymptotic mixing conditions the component _n () weakly converges in Skorokhod space to a Markov process with transition rates averaged by some stationary measures constructed by x _n (). The convergence of a stationary distribution of (x _n (), _n ()) is studied as well. The approximation of state-dependent queueing systems of the type M _M,Q /M _M,Q /m/N with fast Markov switches is considered.     

Maximum Feasibility Problem for Continuous Linear Inequalities with Applications to Fuzzy Linear Programming

Systems of linear inequalities are important tools to formulate optimization problems. However, the feasibility of the whole system was often presumed true in most models. Even if an infeasible system could be detected, it is in general not easy to tell which part of the system caused it. This motivates the study of continuous linear inequalities, given no information whether it is feasible or not, what is the largest possible portion of the system that can be remained in consistency? We first propose a bisection-based algorithm which comes with an auxiliary program to answer the question. For further accelerating the algorithm, several novel concepts, one called “constraint weighting” and the other called “shooting technique”, are introduced to explore intrinsic problem structures. This new scheme eventually replaces the bisection method and its validity can be justified via a solid probabilistic analysis. Numerical examples and applications to fuzzy inequalities are reported to illustrate the robustness of our algorithm.