The maximum principle for partially observed optimal control problems of mean-field FBSDEs

This paper is concerned with a partially observed optimal control problem described by mean-field forward and backward stochastic differential equations. Moreover, the control variable enters the diffusion coefficient and the control domain is non-convex. Utilising Girsanov's theorem as well as extended Ekeland's variational principle, a maximum principle is established in the form of Pontryagin's type. As an application, a linear-quadratic control problem is studied in terms of the stochastic filtering.     

最优控制的计算原理:评论最小值原理

提出连续最优控制计算原理和离散最优控制计算原理,两者都可用于最优控制的数值优化. Pontryagin最小值原理和离散最小值原理,由于其含有的信息不完整,形式特殊,所以都不能用于最优控制的数值优化.此外Pontryagin最小值原理和离散最小值原理分别为连续最优控制计算原理和离散最优控制计算原理的特殊情况.     

A Newton method for solving continuous multiple material minimum compliance problems

This paper presents an implementation of an active-set line-search Newton method intended for solving large-scale instances of a class of multiple material minimum compliance problems. The problem is modeled with a convex objective function and linear constraints. At each iteration of the Newton method, one or two linear saddle point systems are solved. These systems involve the Hessian of the objective function, which is both expensive to compute and completely dense. Therefore, the linear algebra is arranged such that the Hessian is not explicitly formed. The main concern is to solve a sequence of closely related problems appearing as the continuous relaxations in a nonlinear branch and bound framework for solving discrete minimum compliance problems. A test-set consisting of eight discrete instances originating from the design of laminated composite structures is presented. Computational experiments with a branch and bound method indicate that the proposed Newton method can, on most instances in the test-set, take advantage of the available starting point information in an enumeration tree and resolve the relaxations after branching with few additional function evaluations. Discrete feasible designs are obtained by a rounding heuristic. Designs with provably good objective functions are presented.     

On the regularized Lagrange principle in iterative form and its application for solving unstable problems

For a convex programming problem in a Hilbert space with operator equality constraints, the Lagrange principle in sequential nondifferential form or, in other words, the regularized Lagrange principle in iterative form, that is resistant to input data errors is proved. The possibility of its applicability for direct solving unstable inverse problems is discussed. As an example of such problem, we consider the problem of finding the normal solution of the Fredholm integral equation of the first kind. The results of the numerical calculations are shown.     

On the existence of solutions to a class of minimum time control problems and applications to Fermat's Principle and to the Brachystocrone

We prove a theorem for the existence of solutions to minimum time control problems, under assumptions that do not require the convexity of the images and that weaken the assumption of upper semicontinuity. Our result applies to Fermat's Principle and to the Brachystocrone.     

Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions

The minimum cost flow problem (MCFP) is the most generic variation of the network flow problem which aims to transfer a commodity throughout the network to satisfy demands. The problem size (in terms of the number of nodes and arcs) and the shape of the cost function are the most critical factors when considering MCFPs. Existing mathematical programming techniques often assume the cost functions to be linear or convex. Unfortunately, the linearity and convexity assumptions are too restrictive for modelling many real-world scenarios. In addition, many real-world MCFPs are large-scale, with networks having a large number of nodes and arcs. In this paper, we propose a probabilistic tree-based genetic algorithm (PTbGA) for solving large-scale minimum cost integer flow problems with nonlinear non-convex cost functions. We first compare this probabilistic tree-based representation scheme with the priority-based representation scheme, which is the most commonly-used representation for solving MCFPs. We then compare the performance of PTbGA with that of the priority-based genetic algorithm (PrGA), and two state-of-the-art mathematical solvers on a set of MCFP instances. Our experimental results demonstrate the superiority and efficiency of PTbGA in dealing with large-sized MCFPs, as compared to the PrGA method and the mathematical solvers.     

On the transformation of nonlinear dynamical systems into the extended nonlinear observable canonical form

This article deals with the transformation of a class of nonlinear systems into an extended nonlinear observable canonical form (ENOCF) by dynamic extension. The dynamic extension is obtained by adding auxiliary dynamics and virtual outputs to the original system, thus giving rise to a higher dimensional system in an extended state space. Sufficient geometrical conditions to guarantee the existence of a local diffeomorphism which allow the transformation of the extended system into the ENOCF are given. In particular, an algorithm that permits us to compute such a diffeomorphism is derived.     

Existence of optimal controls for partially observed linear diffusions

A unified framework for the separation approach to the control of partially observed linear diffusions is presented, which covers the case of bounded as well as unbounded controls.     

加热炉最小能耗优化控制策略的研究

以某钢厂推钢式连续加热炉为背景 ,建立了加热炉的动态数学模型 ,并在此基础上解决了加热炉最小能耗优化控制问题。文中针对不同钢种 ,尤其是特种钢进行仿真计算 ,得出了各自的最佳炉温设定曲线 ,并获得了满意的效果。     

Near optimality conditions in stochastic control of jump diffusion processes

This paper is concerned with necessary as well as sufficient conditions for near-optimality of controlled jump diffusion processes. Necessary conditions for a control to be near-optimal are derived, using Ekeland’s variational principle and some stability results on the state and adjoint processes, with respect to the control variable. In a second step, we show that the necessary conditions for near-optimality, are in fact sufficient for near-optimality provided some concavity conditions are fulfilled. Finally, as an illustration some examples are solved explicitly.     

Conditions when minimum variance control is the optimal experiment for identifying a minimum variance controller

It is well known that if we intend to use a minimum variance control strategy, which is designed based on a model obtained from an identification experiment, the best experiment which can be performed on the system to determine such a model (subject to output power constraints, or for some specific model structures) is to use the true minimum variance controller. This result has been derived under several circumstances, first using asymptotic (in model order) variance expressions but also more recently for ARMAX models of finite order. In this paper we re-approach this problem using a recently developed expression for the variance of parametric frequency function estimates. This allows a geometric analysis of the problem and the generalization of the aforementioned finite model order ARMAX results to general linear model structures.     

Maximum principle for the stochastic optimal control problem with delay and application

In this paper, we consider an optimal control problem for the stochastic system described by stochastic differential equations with delay. We obtain the maximum principle for the optimal control of this problem by virtue of the duality method and the anticipated backward stochastic differential equations. Our results can be applied to a production and consumption choice problem. The explicit optimal consumption rate is obtained.     

A nonsmooth Newton’s method for control-state constrained optimal control problems

We investigate optimal control problems subject to mixed control-state constraints. The necessary conditions are stated in terms of a local minimum principle. By use of the Fischer–Burmeister function the minimum principle is transformed into an equivalent nonlinear and nonsmooth equation in appropriate Banach spaces. This nonlinear and nonsmooth equation is solved by a nonsmooth Newton’s method. We will show the local quadratic convergence under certain regularity conditions and suggest a globalization strategy based on the minimization of the squared residual norm. A numerical example for the Rayleigh problem concludes the article.     

The relaxed optimal control problem for Mean-Field SDEs systems and application

The present study deals with a new approach of optimal control problems where the state equation is a Mean-Field stochastic differential equation, and the set of strict (classical) controls need not be convex and the diffusion coefficient depends on the term control. Our consideration is based on only one adjoint process, and the necessary conditions as well as a sufficient condition for optimality in the form of a relaxed maximum principle are obtained, with application to Linear quadratic stochastic control problem with mean-field type.     

Variational Bayesian learning of nonlinear hidden state-space models for model predictive control

This paper studies the identification and model predictive control in nonlinear hidden state-space models. Nonlinearities are modelled with neural networks and system identification is done with variational Bayesian learning. In addition to the robustness of control, the stochastic approach allows for various control schemes, including combinations of direct and indirect controls, as well as using probabilistic inference for control. We study the noise-robustness, speed, and accuracy of three different control schemes as well as the effect of changing horizon lengths and initialisation methods using a simulated cart–pole system. The simulations indicate that the proposed method is able to find a representation of the system state that makes control easier especially under high noise.     

On the expressive power of the relational algebra with partially ordered domains

Assuming data domains are partially ordered, we define the partially ordered relational algebra (PORA) by allowing the ordering predicate ? to be used in formulae of the selection operator σ. We apply Paredaens and Bancilhon's Theorem to examine the expressiveness of the PORA, and show that the PORA expresses exactly the set of all possible relations which are invariant under order-preserving automorphisms of databases. The extension is consistent with the two important extreme cases of unordered and linearly ordered domains. We also investigate the three hierarchies of: (1) computable queries, (2) query languages and (3) partially ordered domains, and show that there is a one-to-one correspondence between them.     

平面粘性流体扰动问题的变分原理及双正交关系

通过引入不同的对偶变量,将粘性流体的扰动问题化为具有良好结构特性的可解耦Hamilton系统.利用可解耦Hamilton系统微分形式与积分形式的等价性,导出了粘性流体扰动问题的Hamilton混合能变分原理,并建立了本征函数系之间的双正交关系.     

An efficient approach for solving the straightness and the flatness problems at large number of data points

The construction of the convex hull as a critical step of solving the straightness and the flatness errors needs a great amount of computation, especially, when the number of data point is large, that limits the computational efficiency. To establish an efficient algorithm to solve these problems, three theorems are developed in this paper to show that the straightness and the flatness errors can be obtained without the construction of the whole convex hull. Theorem 1 shows how to identify the redundant data points that gives an efficient way to reduce the unnecessary computations. The optimum criterion defined in Theorem 2 shows that the optimum solution can be obtained by a small number of data points if these points meet the criterion. Theorem 3 offers an easy way to identify the potential candidates of the solution holders, which are data points on the vertices of the convex hull. The efficiency of the proposed algorithm is validated at the end of this paper. The validation results show that the computational efficiency of solving the straightness and the flatness problems is improved significantly through the proposed algorithm, especially, when the number of data point is large.