On Near-Optimal Mean-Field Stochastic Singular Controls: Necessary and Sufficient Conditions for Near-Optimality

Near-optimization is as sensible and important as optimization for both theory and applications. This paper deals with necessary and sufficient conditions for near-optimal singular stochastic controls for nonlinear controlled stochastic differential equations of mean-field type, which is also called McKean–Vlasov-type equations. The proof of our main result is based on Ekeland’s variational principle and some estimates of the state and adjoint processes. It is shown that optimal singular control may fail to exist even in simple cases, while near-optimal singular controls always exist. This justifies the use of near-optimal stochastic controls, which exist under minimal hypotheses and are sufficient in most practical cases. Moreover, since there are many near-optimal singular controls, it is possible to select among them appropriate ones that are easier for analysis and implementation. Under an additional assumptions, we prove that the near-maximum condition on the Hamiltonian function is a sufficient condition for near-optimality. This paper extends the results obtained in (Zhou, X.Y.: SIAM J. Control Optim. 36(3), 929–947, 1998) to a class of singular stochastic control problems involving stochastic differential equations of mean-field type. An example is given to illustrate the theoretical results.     

Singular mean-field control games

This article studies singular mean field control problems and singular mean field two-players stochastic differential games. Both sufficient and necessary conditions for the optimal controls and for the Nash equilibrium are obtained. Under some assumptions the optimality conditions for singular mean-field control are reduced to a reflected Skorohod problem, whose solution is proved to exist uniquely. Motivations are given as optimal harvesting of stochastic mean-field systems, optimal irreversible investments under uncertainty and mean-field singular investment games. In particular, a simple singular mean-field investment game is studied, where the Nash equilibrium exists but is not unique.     

Near-optimal control of a stochastic vegetation-water system with reaction diffusion

In this article, we study the near-optimal control of a class of stochastic vegetation-water model. The near-optimal control is one problem in which the density of vegetation and water is higher at the lowest cost. We have provided a priori estimates of the vegetation and water densities and obtained the sufficient and necessary conditions for the system's near-optimal control problem by applying the maximum condition of the Hamiltonian function and the Ekeland principle. A numerical simulation is presented to verify our theoretical results.     

Sufficient and necessary conditions for stochastic near-optimal controls: A stochastic chemostat model with non-zero cost inhibiting

Near-optimal controls are as important as optimal controls for both theory and applications. Meanwhile, using inhibitor to control harmful microorganisms and ensure maximum growth of beneficial microorganisms (target microorganisms) is a very interesting topic in the chemostat. Thus, in this paper, we consider a stochastic chemostat model with non-zero cost inhibiting in finite time. The near-optimal control problem was constructed by minimizing the number of harmful microorganisms and minimizing the cost of inhibitor. We find that the Hamiltonian function is key to estimate objective function, and according to the adjoint equation, we obtain some error estimations of the near-optimality. Finally, we establish sufficient and necessary conditions for stochastic near-optimal controls of this model and numerical simulations and some conclusions are given.     

On near-optimal necessary and sufficient conditions for forward-backward stochastic systems with jumps,with applications to finance

We establish necessary and sufficient conditions of near-optimality for nonlinear systems governed by forward-backward stochastic differential equations with controlled jump processes (FBSDEJs in short). The set of controls under consideration is necessarily convex. The proof of our result is based on Ekeland’s variational principle and continuity in some sense of the state and adjoint processes with respect to the control variable. We prove that under an additional hypothesis, the near-maximum condition on the Hamiltonian function is a sufficient condition for near-optimality. At the end, as an application to finance, mean-variance portfolio selection mixed with a recursive utility optimization problem is given. Mokhtar Hafay     

A sufficient condition for near-optimal stochastic controls and its application to manufacturing systems

This paper presents an asymptotic analysis of control models governed by stochastic ordinary differential equations. A sufficient condition of near-optimal controls is given based on Ekeland's principle. It is shown that, under some concavity assumptions, the-maximum condition in terms of the Hamiltonian implies the -optimality. By applying this result to a general manufacturing system, the common practice of hierarchical controls employed in order to reduce the overall complexity of the system is justified on a rigorous basis. A near-optimal control for the operational level is constructed from a near-optimal control at the corporate level, and an asymptotic error bound is obtained. A stochastic extension of the classical HMMS model is treated as a specific example. The approach of this paper is different from those in the literature, and it allows us to handle some previously unsolved problems with nonlinear state equations as well as nonseparable cost functions.This work was partly supported by NSERC Grant A4619, URIF, and the Manufacturing Research Corporation of Ontario.     

Generalized solution in singular stochastic control: The nondegenerate problem

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.     

Optimality necessary conditions in singular stochastic control problems with nonsmooth data

The present paper studies the stochastic maximum principle in singular optimal control, where the state is governed by a stochastic differential equation with nonsmooth coefficients, allowing both classical control and singular control. The proof of the main result is based on the approximation of the initial problem, by a sequence of control problems with smooth coefficients. We, then apply Ekeland's variational principle for this approximating sequence of control problems, in order to establish necessary conditions satisfied by a sequence of near optimal controls. Finally, we prove the convergence of the scheme, using Krylov's inequality in the nondegenerate case and the Bouleau-Hirsch flow property in the degenerate one. The adjoint process obtained is given by means of distributional derivatives of the coefficients.     

Near-Optimal Controls of a Class of Volterra Integral Systems

In a recent paper by Zhou (Ref. 1), the concept of near-optimal controls was introduced for a class of optimal control problems involving ordinary differential equations. Necessary and sufficient conditions for near-optimal controls were derived. This paper extends the results obtained by Zhou to a class of optimal control problems involving Volterra integral equations. The results are applied to study near-optimal controls obtained by the control parametrization method.     

Robust finite-time H∞ control of singular stochastic systems via static output feedback

This paper addresses the problem of robust finite-time stabilization of singular stochastic systems via static output feedback. Firstly, sufficient conditions of singular stochastic finite-time boundedness on static output feedback are obtained for the family of singular stochastic systems with parametric uncertainties and time-varying norm-bounded disturbance. Then the results are extended to singular stochastic H_∞ finite-time boundedness for the class of singular stochastic systems. Designed algorithm for static output feedback controller is provided to guarantee that the underlying closed-loop singular stochastic system is singular stochastic H_∞ finite-time boundedness in terms of strict linear matrix equalities with a fixed parameter. Finally, an illustrative example is presented to show the validity of the developed methodology.     

Some results on pointwise second-order necessary conditions for stochastic optimal controls

The purpose of this paper is to derive some pointwise second-order necessary conditions for stochastic optimal controls in the general case that the control variable enters into both the drift and the diffusion terms. When the control region is convex, a pointwise second-order necessary condition for stochastic singular optimal controls in the classical sense is established; while when the control region is allowed to be nonconvex, we obtain a pointwise second-order necessary condition for stochastic singular optimal controls in the sense of Pontryagin-type maximum principle. It is found that, quite different from the first-order necessary conditions, the correction part of the solution to the second-order adjoint equation appears in the pointwise second-order necessary conditions whenever the diffusion term depends on the control variable, even if the control region is convex.     

奇异型随机控制中的平稳问题

随机控制中的平稳问题也称平均期望费用问题,笔者曾在[1]中研究了一类脉冲型平稳随机控制问题.本文再研究一类推广的奇异型平稳问题.奇异型随机控制问题最初大约由[2]引进,由于它在宇航及卫星发射等高科技领域有着重要应用(参看[3],Introduc-tion),故以后研究的文献很多,而在[3]中定型为较一般的模型.笔者在[4]中对[3]中的折扣费用模型做了推广,本文则相应对其中的平稳问题进行推广.本文的主要结果已     

Singular optimal controls of stochastic recursive systems and Hamilton–Jacobi–Bellman inequality

In this paper, we study the optimal singular controls for stochastic recursive systems, in which the control has two components: the regular control, and the singular control. Under certain assumptions, we establish the dynamic programming principle for this kind of optimal singular controls problem, and prove that the value function is a unique viscosity solution of the corresponding Hamilton–Jacobi–Bellman inequality, in a given class of bounded and continuous functions. At last, an example is given for illustration.     

条件平均场随机微分方程的最优控制问题

作者研究了一个条件平均场随机微分方程的最优控制问题.这种方程和某些部分信息下的随机最优控制问题有关,并且可以看做是平均场随机微分方程的推广.作者以庞特里雅金最大值原理的形式给出最优控制满足的必要和充分条件.此外,文中给出一个线性二次最优控制问题来说明理论结果的应用.     

Special Issue: Optimization and Stochastic Control in Finance,Journal of Optimization Theory and Applications

In this paper, we study the near-optimal control for systems governed by forward–backward stochastic differential equations via dynamic programming principle. Since the nonsmoothness is inherent in this field, the viscosity solution approach is employed to investigate the relationships among the value function, the adjoint equations along near-optimal trajectories. Unlike the classical case, the definition of viscosity solution contains a perturbation factor, through which the illusory differentiability conditions on the value function are dispensed properly. Moreover, we establish new relationships between variational equations and adjoint equations. As an application, a kind of stochastic recursive near-optimal control problem is given to illustrate our theoretical results.     

Generalization of Cauchy’s characteristics method to construct smooth solutions to Hamilton-Jacobi-Bellman equations in optimal control problems with singular regimes

An approach to constructing optimal control synthesis, based on studying the allocation of characteristics to the Cauchy problem for the Hamilton-Jacobi-Bellman (HJB) equation (i.e., determining how the extended phase space is filled with these characteristics), is proposed. A method for finding a global solution to the Cauchy problem for the HJB equation by setting boundary conditions on the surface of singular characteristics corresponding to singular optimal controls is developed. Control is considered to be one-dimensional and linear within the system. In describing the method, it is assumed that this surface is unique, and that the switching of any admissible process satisfying Pontryagin’s maximum principle can occur only on it and not more than once. The corresponding sufficient conditions are obtained, and the smoothness of the cost function constructed in this way is verify. The resulting approach is demonstrated via the example of a mathematical model for the treatment of viral infections.     

Equilibrium analysis of disaggregate facility choice systems subject to congestion-elastic demand.

This paper considers the impact of congestion on the spatial distribution of customer utilization of service facilities in a stochastic-dynamic environment. Previous research has assumed that the rate of demand for service is independent of the attributes of the facilities. We consider the more general case in which facility utilization is determined both by individual facility choice (based on the stochastic disaggregate choice mechanism) and by the rate of demand for service. We develop generalized results for proving that equilibria exist and describe sufficient conditions for the uniqueness and global stability of these equilibria. These conditions depend upon the elasticity of demand with respect to the level of congestion at the facilities, and on whether customers are congestion-averse or are congestion-loving. Finally, we examine special cases when these conditions are satisfied.     

The newton filtration and d-determination of bifurcation problems related to C 0 contact equivalence

In this paper, from the Newton filtration’s point of view, we construct the singular Riemannian metric and use the method in singular theory to study the bifurcation problems, and give the sufficient condition of d-determination of bifurcation problems with respect to C ⁰ contact equivalence. The special cases of the main result in this paper are the results of Sun Weizhi and Zou Jiancheng.     

一类具有13个参数的7次系统的极限环分支

研究了一类具有幂零奇点的7次多项式微分系统的极限环分支与中心问题.借助于数学软件MATHEMATICA,推导出系统在原点的前14个拟Lyapunov常数,从而得到了系统的原点为中心的充要条件,证明了系统在3阶幂零奇点处可以分支出14个极限环,给出了7次李雅谱诺夫系统在3阶幂零奇点处的环性数的下界.     

Imprecise parameters for near-optimal control of stochastic SIV epidemic model

The change of parameters may influence the dynamic behaviors of epidemic diseases. Biological system parameters can also be changed due to diverse uncertainties such as lack of data and errors in the statistical approach. The problem of how to define and decide the optimal-control strategies of epidemic diseases with imprecise parameters deserves further researches. The paper presents a stochastic susceptible, infected, and vaccinated (SIV) system that includes imprecise parameters. Firstly, we give the method of parameter estimates of the SIV model. Then, by using Ekeland's principle and Hamiltonian function, we obtain the sufficient conditions and necessary conditions of near-optimal control of the SIV epidemic model with imprecise parameters. At last, numerical examples prove our theoretical results.