scholarly journals Ergodic control of diffusions with random intervention times

2021 ◽  
Vol 58 (1) ◽  
pp. 1-21
Author(s):  
Harto Saarinen ◽  
Jukka Lempa
Keyword(s):  
Optimal Solution ◽  
Singular Control ◽  
Control Policy ◽  
Control Problems ◽  
Ergodic Control ◽  
Linear Diffusion ◽  
One Dimensional ◽  
And Diffusion ◽  
Independent Poisson Process ◽  
Exact Amount

AbstractWe study an ergodic singular control problem with constraint of a regular one-dimensional linear diffusion. The constraint allows the agent to control the diffusion only at the jump times of an independent Poisson process. Under relatively weak assumptions, we characterize the optimal solution as an impulse-type control policy, where it is optimal to exert the exact amount of control needed to push the process to a unique threshold. Moreover, we discuss the connection of the present problem to ergodic singular control problems, and illustrate the results with different well-known cost and diffusion structures.

Optimum Control of Distributed-Parameter Systems

1964 ◽  
Vol 86 (1) ◽  
pp. 67-79 ◽  
Author(s):  
P. K. C. Wang ◽  
F. Tung
Keyword(s):  
Distributed Systems ◽  
Dynamical Systems ◽  
Maximum Principle ◽  
Distributed Parameter Systems ◽  
Distributed Parameter ◽  
Control Problems ◽  
Optimum Control ◽  
Linear Diffusion ◽  
One Dimensional ◽  
Controllability And Observability

This paper presents a general discussion of the optimum control of distributed-parameter dynamical systems. The main areas of discussion are: (a) The mathematical description of distributed parameter systems, (b) the controllability and observability of these systems, (c) the formulation of optimum control problems and the derivation of a maximum principle for a particular class of systems, and (d) the problems associated with approximating distributed systems by discretization. In order to illustrate the applicability of certain general results and manifest some of the properties which are intrinsic to distributed systems, specific results are obtained for a simple, one-dimensional, linear-diffusion process.

scholarly journals A singular control problem with an expected and a pathwise ergodic performance criterion

2006 ◽  
Vol 2006 ◽  
pp. 1-19 ◽  
Author(s):  
Andrew Jack ◽  
Mihail Zervos
Keyword(s):  
Singular Control ◽  
Performance Criterion ◽  
Control Problems ◽  
Singular Stochastic Control ◽  
Finite Variation ◽  
Control Effort ◽  
One Dimensional ◽  
State Process ◽  
Variation Process

We consider the problem of controlling a general one-dimensional Itô diffusion by means of a finite-variation process. The objective is to minimise a long-term average expected criterion as well as a long-term pathwise criterion that penalise deviations of the underlying state process from a given nominal point as well as the expenditure of control effort. We solve the resulting singular stochastic control problems under general assumptions by identifying an optimal strategy that is explicitly characterised.

Velocity and diffusion coefficient of a random asymmetric one-dimensional hopping model

1989 ◽  
Vol 50 (8) ◽  
pp. 899-921 ◽  
Author(s):  
C. Aslangul ◽  
N. Pottier ◽  
D. Saint-James
Keyword(s):  
Diffusion Coefficient ◽  
One Dimensional ◽  
And Diffusion ◽  
Hopping Model

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

scholarly journals LATTICE THREE-SPECIES MODELS OF THE SPATIAL SPREAD OF RABIES AMONG FOXES

1999 ◽  
Vol 10 (06) ◽  
pp. 1025-1038 ◽  
Author(s):  
A. BENYOUSSEF ◽  
N. BOCCARA ◽  
H. CHAKIB ◽  
H. EZ-ZAHRAOUY
Keyword(s):  
Carrying Capacity ◽  
Short Range ◽  
Lattice Models ◽  
Infection Process ◽  
Spatial Spread ◽  
One Dimensional ◽  
Sense Of Direction ◽  
Speed Of Propagation ◽  
Automata Network ◽  
And Diffusion

Lattice models describing the spatial spread of rabies among foxes are studied. In these models, the fox population is divided into three-species: susceptible (S), infected or incubating (I), and infectious or rabid (R). They are based on the fact that susceptible and incubating foxes are territorial while rabid foxes have lost their sense of direction and move erratically. Two different models are investigated: a one-dimensional coupled-map lattice model, and a two-dimensional automata network model. Both models take into account the short-range character of the infection process and the diffusive motion of rabid foxes. Numerical simulations show how the spatial distribution of rabies, and the speed of propagation of the epizootic front depend upon the carrying capacity of the environment and diffusion of rabid foxes out of their territory.

Dynamic Path Synthesis and Optimal Control

1974 ◽  
Vol 96 (1) ◽  
pp. 19-24
Author(s):  
P. J. Starr
Keyword(s):  
Optimal Control ◽  
Control Problem ◽  
Fourth Order ◽  
Optimal Control Problems ◽  
Singular Control ◽  
Physical Nature ◽  
Linear Control ◽  
Control Problems ◽  
Dynamic Path ◽  
Path Synthesis

Dynamic Path Synthesis refers to a class of linkage synthesis problems in which constraint paths between specified positions are determined in such a way as to optimize some measure of the resulting dynamic behavior. These problems can be transformed into nonlinear optimal control problems which are generally non-autonomous. The physical nature of the system allows general comments to be made regarding uniqueness, controllability, and singular control. The ideas are developed in the context of a two-link device yielding a fourth order non-linear control problem, for which a numerical example is presented.

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