Tensor Decomposition and High-Performance Computing for Solving High-Dimensional Stochastic Control System Numerically

The paper presents a numerical method for solving a class of high-dimensional stochastic control systems based on tensor decomposition and parallel computing. The HJB solution provides a globally optimal controller to the associated dynamical system. Variable substitution is used to simplify the nonlinear HJB equation. The curse of dimensionality is avoided by representing the HJB equation using separated representation. Alternating least squares (ALS) is used to reduced the separation rank. The experiment is conducted and the numerical solution is obtained. A high-performance algorithm is designed to reduce the separation rank in the parallel environment, solving the high-dimensional HJB equation with high efficiency.

     

一国碳减排的最小费用研究

本文研究一国碳排放量的最优控制问题. 假设这个国家的总碳排放量由国内生产总值（GDP）和人口决定，而GDP满足几何布朗运动模型，国内人口数量满足Logistic模型. 国家采取一些策略降低国内的碳排放量，这些措施也会产生相应的成本. 这个国家需要在降低碳排放量的同时，使得控制过程中的总费用最小. 利用随机控制理论的相关结论，可以对这一问题进行建模，并得到相应的Hamilton-Jacobi-Bellman（HJB）方程. 由此得出的半线性方程可以通过Cole-Hopf变换变为线性并得出显式解，从而得出相应的最小成本和最优控制策略的表达式. 我们对解进行数值计算，得出了值函数与不同参数的关系图.     

Interest rate risk premium and equity valuation

<正> The authors employ the recent stochastic-control-based approach to financial mathematicsto solve a problem of determination of the risk premium for a stochastic interest rate model,andthe corresponding problem of equity valuation.The risk premium is determined explicitly,by meansof solving a corresponding partial differential equation (PDE),in two forms:one,time-dependent,corresponding to a finite time contract expiration,and the simpler version corresponding to perpetualcontracts.As stocks are perpetual contracts,when solving the problem of equity valuation,the latterform of the risk premium is used.By means of solving the general pricing PDE,an efficient equityvaluation method was developed that is a combination of some sophisticated explicit formulas,and anumerical procedure.     

Optimal Reinsurance and Investment Strategies Under Mean-Variance Criteria: Partial and Full Information

This paper is concerned with an optimal reinsurance and investment problem for an insurance firm under the criterion of mean-variance. The driving Brownian motion and the rate in return of the risky asset price dynamic equation cannot be directly observed. And the short-selling of stocks is prohibited. The problem is formulated as a stochastic linear-quadratic control problem where the control variables are constrained. Based on the separation principle and stochastic filtering theory, the partial information problem is solved. Efficient strategies and efficient frontier are presented in closed forms via solutions to two extended stochastic Riccati equations. As a comparison, the efficient strategies and efficient frontier are given by the viscosity solution to the HJB equation in the full information case. Some numerical illustrations are also provided.

     

考虑人寿保险的最优金融决策

不同于以前的最优消费、投资问题研究，本文研究个人投资者的最优金融决策问题——如何决定最优的证券组合、消费和购买人寿保险，使其期望效用最大化。假设投资者死亡事件是一个独立的泊松过程，对投资者生存期间的最优金融决策问题构造了一数学随机模型，运用动态规划原理和随机分析方法，解决对应的最优控制问题，最优策略可通过对应的HJB方程得到。对于效用函数为CRRA(常数相对风险厌恶)类型，显式地得到具有反馈形式的最优投资过程、消费过程及人寿保险购买过程。     

Boundary Control of Coupled Non-Constant Parameter Systems of Time Fractional PDEs with Different-Type Boundary Conditions

This paper addresses a boundary state feedback control problem for a coupled system of time fractional partial differential equations(PDEs) with non-constant(space-dependent) coefficients and different-type boundary conditions(BCs). The BCs could be heterogeneous-type or mixed-type.Specifically, this coupled system has different BCs at the uncontrolled side for heterogeneous-type and the same BCs at the uncontrolled side for mixed-type. The main contribution is to extend PDE backstepping to the ...     

切向加速度与弦误差约束的CNC系统时间最优轨迹规划

针对计算机数控(CNC)系统给定参数化路径, 给出了一种求解时间最优轨迹规划问题的凸优化方法. 轨迹规划问题考虑切向加速度约束与弦误差约束. 通过建立两种约束下的状态容许空间, 分析约束对时间最优轨迹的影响. 通过非线性变量代换, 时间最优轨迹规划问题被表述为一个与时间无关的凸最优控制问题. 基于控制向量参数化(CVP)方法, 问题被进一步转化为易于求解的凸优化问题. 以路径参数对时间的二阶导数(参数加速度)为优化变量, 序列二次规划(SQP)方法获得问题数值解. 文末通过求解两个测试路径的时间最优轨迹规划问题, 验证方法的有效性.     

Convergence rates of Markov chain approximation methods for controlled diffusions with stopping

<正> This work is concerned with rates of convergence of numerical methods using Markov chainapproximation for controlled diffusions with stopping (the first exit time from a bounded region).In lieuof considering the associated finite difference schemes for Hamilton-Jacobi-Bellman (HJB) equations,a purely probabilistic approach is used.There is an added difficulty due to the boundary condition,which requires the continuity of the first exit time with respect to the discrete parameter.To prove theconvergence of the algorithm by Markov chain approximation method,a tangency problem might arise.A common approach uses certain conditions to avoid the tangency problem.Here,by modifying thevalue function,it is demonstrated that the tangency problem will not arise in the sense of convergencein probability and in L~1.In addition,controlled diffusions with a discount factor is also treated.     

常弹性方差模型下保险人的最优投资策略

假设风险资产价格服从常弹性方差(CEV)模型, 保险人面临的风险过程是带漂移的布朗运动. 投资过程与承保风险过程完全相关. 根据随机最优控制理论, 建立保险基金投资问题的HJB方程. 由于该方程是非线性偏微分方程, 不易求解, 因此采用Legendre变换将其转换成对偶问题进行研究. 最后针对特定参数值分别得到以CARA和CRRA效用函数为目标的保险人的最优投资策略, 这样的投资策略更符合金融市场的实际要求.     

多变量非线性系统的有约束模糊预测解耦控制

针对多变量非线性系统提出了一种带约束输入的广义预测解耦控制算法:首先对多变量非线性系统建立T-S模糊模型,然后在每个采样点对系统进行局部动态线性化,将得到的系统线性化模型进行对角解耦,然后对其设计带输入约束的GPC算法.该算法充分考虑了控制输入及其增量受约束的情况,而且不必求Dio-phantine方程,减小了计算量,且削弱了变量之间的耦合程度.最后的仿真结果说明了该算法对多变量非线性系统的有效性.     

特殊剩余价值形式的牛熊证定价研究

牛熊证是在香港交易所交易的新型金融衍生产品,能追踪相关资产的表现而无须支付购入实际资产的全数金额.本文在Black-Scholes期权定价模型的基础上,根据牛证与熊证的价值性质,推导出满足相应条件的偏微分方程,并应用热传导方程的求解方法,得出特殊剩余价值形式的牛证与熊证的模型定价公式.通过对比分析得知,该模型定价低于其理论定价.     

借贷利率限制下的效用投资组合

应用动态规划得到不同借贷利率情形下动态资产分配问题的HJB方程,并对指数效用、幂效用以及对数效用函数下的最优投资策略进行研究.通过求解相应的HJB方程和定义借入曲线得出最优投资组合的解析表达式,并对不同效用函数下投资者的借贷情况进行了说明.最后,给出算例对所得结论进行分析.     

Controlled mean-field backward stochastic differential equations with jumps involving the value function

This paper discusses mean-field backward stochastic differential equations (mean-field BSDEs) with jumps and a new type of controlled mean-field BSDEs with jumps, namely mean-field BSDEs with jumps strongly coupled with the value function of the associated control problem. The authors first prove the existence and the uniqueness as well as a comparison theorem for the above two types of BSDEs. For this the authors use an approximation method. Then, with the help of the notion of stochastic backward semigroups introduced by Peng in 1997, the authors get the dynamic programming principle (DPP) for the value functions. Furthermore, the authors prove that the value function is a viscosity solution of the associated nonlocal Hamilton-Jacobi-Bellman (HJB) integro-partial differential equation, which is unique in an adequate space of continuous functions introduced by Barles, et al. in 1997.     

半群方法在发汗控制问题中的应用

本文研究在高速飞行体飞行中出现的热防护发汗控制模型导出的自由边界问题，引入变换将其化为发展方程，研究其相应产生的发展系统，用半群方法证明了发汗控制边值问题解的存在性，唯一性。其方法对一般抛物算子非线性自由边界问题及高维问题也适用     

Application of Neurocomputing in Adaptive Control of Large-Scale Aerospace Systems

We are engaged in solving two difficult problems in adaptive control of the large-scale time-variant aerospace system. One is parameter identification of time-variant continuous-time state-space modei; the other is how to solve algebraic Riccati equation (ARE) of large order efficiently. In our approach, two neural networks are employed to independently solve both the system identification problem and the ARE associated with the optimal control problem. Thus the identification and the control computation are combined in closed-loop, adaptive, real-time control system . The advantage of this approach is that the neural networks converge to their solutions very quickly and simultaneously.     

带有风险规避的证券投资最优策略

运用随机最优控制理论,建立了带有风险规避的证券投资最优策略问题的数学模型;然后,给出了值函数和风险规避系数的定义,并通过对值函数进行非线性变换,证明了变换后的值函数满足带有风险规避系数的HJB偏微分方程,特别当风险规避系数无限大时,给出了证券投资最优策略;最后给出了一个算例.     

Heston模型下考虑工资风险和保费退还条款的DC型养老金时间一致性投资策略

DC型养老金投资者通常可以将保费投资于金融市场中无风险资产、风险资产以及通胀指数化债券3种资产。在均值-方差准则下，为了充分考虑市场风险因素和保障参保人利益，将通胀风险、波动风险、工资风险以及保费退还条款引进投资模型中。利用博弈论思想和随机最优控制技术，通过求解一个扩展HJB方程组，得到了最优时间一致性投资策略和有效前沿的解析表达。最后，通过数值分析比较了有、无保费退还条款两种情形下的最优投资策略。此外，还分析了一些主要参数对最优投资策略和有效前沿的影响，并给出了经济意义上的解释。     

固定消费模式下的最优投资组合

研究了当投资者的消费为固定模式时的最优投资组合问题．投资者的目的是：在保证固定消费正常进行的条件下，使最终财富的期望效用最大．把现金流分成两部分来考虑：一部分保证消费的正常进行，一部分用于投资．假设投资者的消费是时间的连续函数或者分段连续函数，应用随机最优控制的方法得到了这两种情形下一般效用函数的最优投资策略并导出了值函数满足的HJB方程,最后，分析了消费对投资决策的影响.     

Agent’s Optimal Compensation Under Inflation Risk by Using Dynamic Contract Model

This paper studies the problem of principal-agent with moral hazard in continuous time. The firm’s cash flow is described by geometric Brownian motion (hereafter GBM). The agent affects the drift of the firm’s cash flow by her hidden effort. Meanwhile, the firm rewards the agent with corresponding compensation and equity which depend on the output. The model extends dynamic optimal contract theory to an inflation environment. Firstly, the authors obtain the dynamic equation of the firm’s real cash flow under inflation by using the Itô formula. Then, the authors use the martingale representation theorem to obtain agent’s continuation value process. Moreover, the authors derive the Hamilton-Jacobi-Bellman (HJB) equation of investor’s value process, from which the authors derive the investors’ scaled value function by solving the second-order ordinary differential equation. Comparing with He^[1], the authors find that inflation risk affects the agent’s optimal compensation depending on the firm’s position in the market.

     

Real-Time Pricing for Smart Grid with Multiple Companies and Multiple Users Using Two-Stage Optimization

In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing(RTP) as a two-stage optimization problem. At the first stage, based on cost function, we propose a continuous supply function bidding mechanism to model the utility companies' profit maximization problem, by which the analytic expression of electricity price is further derived. At the second stage, considering that individually optimal solution may not be socially optimal, we employ convex optimization with linear constraints to model the price anticipating users' daily payoff maximum. Substitute the analytic expression of electricity price obtained at the first stage into the optimization problem at the second stage. Using customized proximal point algorithm(C-PPA), the optimization problem at the second stage is solved and electricity price is obtained accordingly. We also prove the existence and uniqueness of the Nash equilibrium in the mentioned twostage optimization and the convergence of C-PPA. In addition, in order to make the algorithm more practical, a statistical approach is used to obtain the function of price only through online information exchange, instead of solving it directly. The proposed approach offers RTP, power production and load scheduling for multiple utility companies and multiple users in smart grid. Statistical approach helps to protect the company's privacy and avoid the interference of random factors, and C-PPA has an advantage over Lagrangian algorithm because the former need not obtain the objection function of the dual optimization problem by solving an optimization problem with parameters. Simulation results show that the proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.