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This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems, which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using a radial basis function neural network, while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated, while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design, which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given, which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be summarized as follows: 1) an output entropy model is presented using a neural network; 2) a nonlinear filter design algorithm is developed as the main result; and 3) a solution of the entropy assignment problem is obtained, which is an extension of the presented framework. [ABSTRACT FROM AUTHOR]

This paper proposes a bound-based simulation budget allocation (BSBA) procedure for solving ranking and selection (R&S) problems in simulation optimization. For many practical applications, strict bounds on system performances can be obtained through empirical and theoretical approaches. These bounds provide additional information which may help solve R&S problems. In this paper, a new method of objective function estimation is proposed using both bound information and simulation outputs. This new estimation method is demonstrated to be particularly effective. To solve R&S problems, several asymptotic optimal allocation rules are also derived. Using these allocation rules, a BSBA procedure is proposed to achieve high efficiency in identifying the best design. Numerical experiments are provided to examine the performance of the proposed BSBA procedure. The computational results show that BSBA outperforms three compared allocation procedures, especially when bounds are tight or the simulation budget is small. [ABSTRACT FROM AUTHOR]

Many problems of interest for cyber-physical network systems can be formulated as mixed-integer linear programs in which the constraints are distributed among the agents. In this paper, we propose a distributed algorithmic framework to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. At each communication round, agents locally solve a small linear program, generate suitable cutting planes, and communicate a fixed number of active constraints. Within the distributed framework, we first propose an algorithm that, under the assumption of integer-valued optimal cost, guarantees finite-time convergence to an optimal solution. Second, we propose an algorithm for general problems that provides a suboptimal solution up to a given tolerance in a finite number of communication rounds. Both algorithms work under asynchronous, directed, unreliable networks. Finally, through numerical computations, we analyze the algorithm scalability in terms of the network size. Moreover, for a multi-agent multi-task assignment problem, we show, consistently with the theory, its robustness to packet loss. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of stochastic resource allocation problems where resources assigned to a task may fail probabilistically to complete assigned tasks. Failures to complete a task are observed before new resource allocations are selected. The resulting temporal resource allocation problem is a stochastic control problem, with a discrete state space and control space that grow in cardinality exponentially with the number of tasks. We modify this optimal control problem by expanding the admissible control space, and show that the resulting control problem can be solved exactly by efficient algorithms in time that grows nearly linear with the number of tasks. The approximate control problem also provides a bound on the achievable performance for the original control problem. The approximation is used as part of a model predictive control (MPC) algorithm to generate resource allocations over time in response to information on task completion status. We show in computational experiments that, for single resource class problems, the resulting MPC algorithm achieves nearly the same performance as the optimal dynamic programming algorithm while reducing computation time by over four orders of magnitude. In multiple resource class experiments involving 1000 tasks, the model predictive control performance is within 4% of the performance bound obtained by the solution of the expanded control space problem. [ABSTRACT FROM AUTHOR]

A fundamental component of the game-theoretic approach to distributed control is the design of local utility functions. Relative to resource allocation problems that are additive over the resources, Part I showed how to design local utilities so as to maximize the associated performance guarantees (Paccagnan, et al., 2020), which we measure by the price of anarchy. The purpose of this article is to specialize these results to the case of submodular, covering, and supermodular problems. In all these cases, we obtain tight expressions for the price of anarchy that often match or improve the guarantees associated with state-of-the-art approximation algorithms. Two applications and corresponding numerics are presented: the vehicle-target assignment problem and a coverage problem arising in wireless data caching. [ABSTRACT FROM AUTHOR]

A new linear method for solving the output feedback pole assignment problem of linear systems is introduced, and new sufficient conditions are obtained. [ABSTRACT FROM AUTHOR]