Optimal project selection: Stochastic knapsack with finite time horizon

A time-constrained capital-budgeting problem arises when projects, which can contribute to achieving a desired target state before a specified deadline, arrive sequentially. We model such problems by treating projects as randomly arriving requests, each with a funding cost, a proposed benefit, and a known probability of success. The problem is to allocate a non-renewable initial budget to projects over time so as to maximise the expected benefit obtained by a certain time, T, called the deadline, where T can be either a constant or a random variable. Each project must be accepted or rejected as soon as it arrives. We developed a stochastic dynamic programming formulation and solution of this problem, showing that the optimal strategy is to dynamically determine ‘acceptance intervals’ such that a project of type i is accepted when, and only when, it arrives during an acceptance interval for projects of type i.     

Optimal infrastructure design and expansion of broadband wireless access networks

This paper investigates the important infrastructure design and expansion problem for broadband wireless access networks subject to user demand constraints and system capacity constraints. For the problem, an integer program is derived and a heuristic solution procedure is proposed based on Lagrangean relaxation. In the computational experiments, our Lagrangean relaxation based algorithm can solve this complex design and expansion problem quickly and near optimally. Based on the test results, it is suggested that the proposed algorithm may be practically used for the infrastructure design and expansion problem for broadband wireless access networks.     

Tree knapsack approaches for local access network design

In the process of solving many forms of the local access network design problem, the basic model of the tree knapsack problem (TKP) is used as a building block for the search engine of the solution strategy. Various solution strategies can be used to solve this problem. An approach that use standard software coupled with enhanced modelling is presented for the TKP. Enhanced modelling is used to partition the TKP into sub-problems that is easier to solve using standard off the shelve software. The basic approach is described and empirical work is presented. Empirical comparisons are also given relating this approach with some algorithms suggested by other authors.     

Optimal information acquisition for a linear quadratic control problem

This paper develops a generalization of the linear quadratic control problem with partial information. As in the standard partial information setting, it is assumed that the state variable is only observed with noise. The idea in this paper is that the information level may be chosen optimally. In real life information is costly to acquire. It is therefore a trade off between the costs of getting detailed information and the increased value this information gives. We believe that the technique we present should have potential for application within both economics and engineering.     

Stochastic dominance with linear partial information

This paper is concerned with expanding the range of application of stochastic dominance as a basis for choosing between alternative decision strategies. Unlike most work in this area, it does so on the assumption that the decision maker is not willing to specify a unique subjective probability distribution for future states of nature, but is only able to articulate a fuzzy, inexact set of beliefs, which may be summarized by appropriate linear constraints on probabilities of events. It is shown that the concept of stochastic dominance readily transfers to this decision environment and that the relevant calculations are quite straightforward. Additionally, the requirements for stochastic dominance and for statistical dominance are compared; the existence of the former is shown always to imply the latter.     

Optimal investment and risk control for an insurer under inside information

This paper is devoted to the study of the optimal investment and risk control strategy for an insurer who has some inside information on the financial market and the insurance business. The insurer’s risk process and the risky asset process in the financial market are assumed to be very general jump diffusion processes. The two processes are supposed to be correlated. Under the criterion of logarithmic utility maximization of the terminal wealth, we solve our problem by using forward integral approach. Some interesting particular cases are studied in which the explicit expressions of the optimal strategy are derived by using enlargement of filtration techniques.     

Alternating control tree search for knapsack/covering problems

The Multidimensional Knapsack/Covering Problem (KCP) is a 0–1 Integer Programming Problem containing both knapsack and weighted covering constraints, subsuming the well-known Multidimensional Knapsack Problem (MKP) and the Generalized (weighted) Covering Problem. We propose an Alternating Control Tree Search (ACT) method for these problems that iteratively transfers control between the following three components: (1) ACT-1, a process that solves an LP relaxation of the current form of the KCP. (2) ACT-2, a method that partitions the variables according to 0, 1, and fractional values to create sub-problems that can be solved with relatively high efficiency. (3) ACT-3, an updating procedure that adjoins inequalities to produce successively more constrained versions of KCP, and in conjunction with the solution processes of ACT-1 and ACT-2, ensures finite convergence to optimality. The ACT method can also be used as a heuristic approach using early termination rules. Computational results show that the ACT-framework successfully enhances the performance of three widely different heuristics for the KCP. Our ACT-method involving scatter search performs better than any other known method on a large set of KCP-instances from the literature. The ACT-based methods are also found to be highly effective on the MKP.     

Optimal Stochastic Impulse Control with Delayed Reaction

We study impulse control problems of jump diffusions with delayed reaction. This means that there is a delay δ>0 between the time when a decision for intervention is taken and the time when the intervention is actually carried out. We show that under certain conditions this problem can be transformed into a sequence of iterated no-delay optimal stopping problems and there is an explicit relation between the solutions of these two problems. The results are illustrated by an example where the problem is to find the optimal times to increase the production capacity of a firm, assuming that there are transaction costs with each new order and the increase takes place δ time units after the (irreversible) order has been placed.     

提前预知信息的在线分批排序问题

研究工件可提前预知信息的在线分批排序问题, 工件的预知信息时间依时间到达, 目标为极小化最大完工时间. 已知从工件的信息可预知到该工件可加工需要时间~$a$, 所有工件的最大加工时间为~$p_{{\rm max}}$, 多个工件可以作为一批被机器同时加工, 批的加工时间为该批工件中最长加工时间. 对于批容量无限的单机问题给出一个在线算法~$\gamma H^\infty$, 并证明其竞争比和问题的下界都为~$1+\gamma$, 其中~$\gamma=\left(-1+\sqrt{1+\frac{4p_{{\rm max}}}{p_{{\rm max}}+a}}\right)/2$, 进而算法是最优的.     

Meshfree Approximation for Stochastic Optimal Control Problems

In this work,we study the gradient projection method for solving a class of stochastic control problems by using a mesh free approximation ap-proach to implement spatial dimension approximation.Our main contribu-tion is to extend the existing gradient projection method to moderate high-dimensional space.The moving least square method and the general radial basis function interpolation method are introduced as showcase methods to demonstrate our computational framework,and rigorous numerical analysis is provided to prove the convergence of our meshfree approximation approach.We also present several numerical experiments to validate the theoretical re-sults of our approach and demonstrate the performance meshfree approxima-tion in solving stochastic optimal control problems.     

Optimal Control for Two-Dimensional Stochastic Navier-Stokes Equations

Loeb space methods are used to prove the existence of an optimal control for the two-dimensional stochastic Navier--Stokes equations in a variety of settings—including that of control based on digital observations of the evolution of the solution.     

Stochastic control problems with delay

We consider optimal control problems for systems described by stochastic differential equations with delay. We state conditions for certain classes of such systems under which the stochastic control problems become finite-dimensional. These conditions are illustrated with three applications. First, we solve some linear quadratic problems with delay. Then we find the optimal consumption rate in a financial market with delay. Finally, we solve explicitly a deterministic fluid problem with delay which arises from admission control in ATM communication networks.     

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??Under inflation influence, this paper investigate a stochastic differential game with reinsurance and investment. Insurance company chose a strategy to minimizing the variance of the final wealth, and the financial markets as a game ``virtual hand' chosen a probability measure represents the economic ``environment' to maximize the variance of the final wealth. Through this double game between the insurance companies and the financial markets, get optimal portfolio strategies. When investing, we consider inflation, the method of dealing with inflation is: Firstly, the inflation is converted to the risky assets, and then constructs the wealth process. Through change the original based on the mean-variance criteria stochastic differential game into unrestricted cases, then application linear-quadratic control theory obtain optimal reinsurance strategy and investment strategy and optimal market strategy as well as the closed form expression of efficient frontier are obtained; finally get reinsurance strategy and optimal investment strategy and optimal market strategy as well as the closed form expression of efficient frontier for the original stochastic differential game.     

Sufficient Maximum Principle for Stochastic Optimal Control Problems with General Delays

Journal of Optimization Theory and Applications - This paper is to establish a sufficient maximum principle for one kind of stochastic optimal control problem with three types of delays: a discrete...     

随机波动率市场存在股票误价时的最优投资策略

本文研究了随机波动率市场中存在股票误价(mispricing)时的最优投资组合选择问题.假设投资者的目标是最大化终端财富的期望幂效用;其可投资于无风险资产、市场指数和两支相同权益或近似度极高的股票,其中至少有一支股票存在误价;市场收益的波动率和股票系统风险由Heston随机波动率模型刻画.运用动态规划方法和Lagrange乘子法,分别得到不存在/存在有限卖空约束时,投资者的最优投资策略及最优值函数的解析式,并通过理论分析和数值算例,阐述了投资时间水平和价格随机误差对最优投资策略的影响.     

Optimal control for evolution equations with memory

In this paper, we investigate the existence and regularity of solutions for Bolza optimal control problems in infinite dimension governed by a class of semilinear evolution equations. Our results apply to systems exhibiting hereditary properties, as heat propagation in real conductors and isothermal viscoelasticity, described by equations with memory terms which account for the past history of the variables in play.