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1.
本文研究了带罚函数的对偶模型的最优分红问题.假设当公司的盈余资金为负值时,公司不会发生破产,但是会进行相应的惩罚,惩罚金额取决于公司的余额水平.利用随机最优控制方法和动态规划原则,得到了最优化问题的HJB方程及其验证定理.最后,当收益服从指数分布时,得到了带指数罚函数和带线性罚函数两种情形各自的最优分红策略及最优值函数的解析式.  相似文献   

2.
该文主要在有界红利率的条件下讨论复合二项对偶模型的周期性分红问题.通过对值函数进行变换,得到了最优红利策略的一些性质,并且证明了最优值函数是一个HJB方程的唯一解.从而得到了最优策略和最优值函数的一个简单计算方法.根据最优红利策略的一些性质,该文还得到了最优值函数的可无限逼近的上界和下界.最后提供一些数值计算实例来说明该算法.  相似文献   

3.
本文研究了具有停止损失再保险和最终值的最优分红和融资策略问题.通过运用近似扩散和动态规划及构造次最优问题的方法,得到了解决一般最优问题所应符合的HJB方程和验证定理.假设有比例和固定交易费用以及在破产时刻产生最终值,得到了相应的最优值函数,最优分红策略,再保险策略以及融资策略.  相似文献   

4.
运用动态规划和组合优化理论,建立了基于在线捆绑的易逝品动态标价模型.针对是否补充缺货分别建立了紧急补货模型和失销模型.将有限的销售期限分割成N个时间决策单元,提出了每个决策单元的捆绑结构和捆绑包价格的确定方法.证明了紧急补货模型价值函数的可分解性、最优捆绑价格在时间上的非递减性和在存量上的非递增性;证明了失销模型最优期望收益在存量上的非递减性.该模型有助于实践中运用在线捆绑策略的易逝品生产和服务企业对捆绑包结构和捆绑包价格做出正确的决策.  相似文献   

5.
考虑了替代产品的动态库存决策与控制问题,建立了替代产品的多周期动态库存决策与控制模型.得到了目标函数的一些重要性质,给出了系统最优参数的求解算法,利用动态规划方法对系统的库存参数进行了优化求解.  相似文献   

6.
在马尔科夫机制转换谱正Levy风险模型中,研究最优分红问题.通过构造辅助的最优化问题,利用动态规划准则和Levy过程的漂移理论,证明了调节有界分红策略是最优策略,通过迭代方法得到了值函数和最优分红水平.  相似文献   

7.
假设保险公司的盈余过程和金融市场的资产价格过程均由可观测的连续时间马尔科夫链所调节,以最大化终端财富的状态相依的期望指数效用为目标,研究了保险公司的超额损失再保险-投资问题.运用动态规划方法,得到最优再保险-投资策略的解析解以及最优值函数的半解析式.最后,通过数值例子,分析了模型各参数对最优值函数和最优策略的影响.  相似文献   

8.
邓丽  谭激扬 《经济数学》2014,(4):102-106
研究复合二项对偶模型的最优分红问题,通过分析HJB方程得到了最优分红策略和相应的最优值函数之间的关系以及最优值函数的简单计算方法.通过讨论最优红利策略的一些性质得到了最优值函数的可无限逼近的上界和下界.  相似文献   

9.
对于给定的一个集合,分组测试问题是通过一系列的测试去确定这个集合的一个子集. 在文中, 作者首先运用动态规划的理论与方法, 建立了一个近似控制标准, 目的是对分组测试算法的构建过程进行有效控制, 使所构建的算法达到最优. 其次, 应用该近似控制标准研究了在n个硬币集合中确定一个伪硬币的最小平均测试数的问题. 文中所涉及的近似控制问题, 给出了在一个给定集合中去确定这个集合的一个子集的最优分组测试算法, 该最优分组测试算法是在平均测试步骤最少意义下的最优分组测试算法.  相似文献   

10.
以条件期望体现风险资产收益的相关性,建立了资产收益序列相关时资产-负债管理的动态均值-方差模型.采用Li和Ng(2000)的嵌入法,构造了一个具有二次效用函数的辅助问题,利用动态规划方法及原问题与辅助问题最优策略之间的关系,得到了原问题的最优投资组合策略和有效边界.  相似文献   

11.
根据灰色系统理论,建立了动态投入产出问题的灰色最优控制模型.利用灰集合理论,把灰色最优控制问题转化为以隶属度为目标函数的(非灰色的)非线性规划问题,从而可利用非线性规划的方法求解这个灰色最优控制问题.  相似文献   

12.
This paper is concerned with singular stochastic control for non-degenerate problems. It generalizes the previous work in that the model equation is nonlinear and the cost function need not be convex. The associated dynamic programming equation takes the form of variational inequalities. By combining the principle of dynamic programming and the method of penalization, we show that the value function is characterized as a unique generalized (Sobolev) solution which satisfies the dynamic programming variational inequality in the almost everywhere sense. The approximation for our singular control problem is given in terms of a family of penalized control problems. As a result of such a penalization, we obtain that the value function is also the minimum cost available when only the admissible pairs with uniformly Lipschitz controls are admitted in our cost criterion.  相似文献   

13.
This paper considers a new optimal location problem, called defensive location problem (DLP). In the DLPs, a decision maker locates defensive facilities in order to prevent her/his enemies from reaching an important site, called a core; for example, “a government of a country locates self-defense bases in order to prevent her/his aggressors from reaching the capital of the country.” It is assumed that the region where the decision maker locates her/his defensive facilities is represented as a network and the core is a vertex in the network, and that the facility locater and her/his enemy are an upper and a lower level of decision maker, respectively. Then the DLPs are formulated as bilevel 0-1 programming problems to find Stackelberg solutions. In order to solve the DLPs efficiently, a solving algorithm for the DLPs based upon tabu search methods is proposed. The efficiency of the proposed solving methods is shown by applying to examples of the DLPs. Moreover, the DLPs are extended to multi-objective DLPs that the decision maker needs to defend several cores simultaneously. Such DLPs are formulated as multi-objective programming problems. In order to find a satisfying solution of the decision maker for the multi-objective DLP, an interactive fuzzy satisfying method is proposed, and the results of applying the method to examples of the multi-objective DLPs are shown.  相似文献   

14.

We investigate an infinite horizon investment-consumption model in which a single agent consumes and distributes her wealth between a risk-free asset (bank account) and several risky assets (stocks) whose prices are governed by Lévy (jump-diffusion) processes. We suppose that transactions between the assets incur a transaction cost proportional to the size of the transaction. The problem is to maximize the total utility of consumption under Hindy-Huang-Kreps intertemporal preferences. This portfolio optimisation problem is formulated as a singular stochastic control problem and is solved using dynamic programming and the theory of viscosity solutions. The associated dynamic programming equation is a second order degenerate elliptic integro-differential variational inequality subject to a state constraint boundary condition. The main result is a characterization of the value function as the unique constrained viscosity solution of the dynamic programming equation. Emphasis is put on providing a framework that allows for a general class of Lévy processes. Owing to the complexity of our investment-consumption model, it is not possible to derive closed form solutions for the value function. Hence, the optimal policies cannot be obtained in closed form from the first order conditions for the dynamic programming equation. Therefore, we have to resort to numerical methods for computing the value function as well as the associated optimal policies. In view of the viscosity solution theory, the analysis found in this paper will ensure the convergence of a large class of numerical methods for the investment-consumption model in question.  相似文献   

15.
This paper investigates a dynamic event-triggered optimal control problem of discrete-time (DT) nonlinear Markov jump systems (MJSs) via exploring policy iteration (PI) adaptive dynamic programming (ADP) algorithms. The performance index function (PIF) defined in each subsystem is updated by utilizing an online PI algorithm, and the corresponding control policy is derived via solving the optimal PIF. Then, we adopt neural network (NN) techniques, including an actor network and a critic network, to estimate the iterative PIF and control policy. Moreover, the designed dynamic event-triggered mechanism (DETM) is employed to avoid wasting additional resources when the estimated iterative control policy is updated. Finally, based on the Lyapunov difference method, it is proved that the system stability and the convergence of all signals can be guaranteed under the developed control scheme. A simulation example for DT nonlinear MJSs with two system modes is presented to demonstrate the feasibility of the control design scheme.  相似文献   

16.
The envelope theorem is a statement about derivatives along an optimal trajectory. In dynamic programming the envelope theorem can be used to characterize and compute the optimal value function from its derivatives. We illustrate this here for the linear-quadratic control problem, the resource allocation problem, and the inverse problem of dynamic programming.  相似文献   

17.
In this paper, a neural network model is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle to solve geometric programming (GP) problems. The main idea is to convert the GP problem into an equivalent convex optimization problem. A neural network model is then constructed for solving the obtained convex programming problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient.  相似文献   

18.
动态投入产出最优控制模型   总被引:1,自引:1,他引:0  
本文建立了一个新的具有上下限约束的投入产出问题的最优控制模型 ,并把最优控制问题转化为动态规划问题 ,利用动态最优化的方法给出了该问题的求解方法  相似文献   

19.
We study a stochastic optimal control problem for a partially observed diffusion. By using the control randomization method in Bandini et al. (2018), we prove a corresponding randomized dynamic programming principle (DPP) for the value function, which is obtained from a flow property of an associated filter process. This DPP is the key step towards our main result: a characterization of the value function of the partial observation control problem as the unique viscosity solution to the corresponding dynamic programming Hamilton–Jacobi–Bellman (HJB) equation. The latter is formulated as a new, fully non linear partial differential equation on the Wasserstein space of probability measures. An important feature of our approach is that it does not require any non-degeneracy condition on the diffusion coefficient, and no condition is imposed to guarantee existence of a density for the filter process solution to the controlled Zakai equation. Finally, we give an explicit solution to our HJB equation in the case of a partially observed non Gaussian linear–quadratic model.  相似文献   

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