scholarly journals Linearization of a Matrix Riccati Equation Associated to an Optimal Control Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Foued Zitouni ◽  
Mario Lefebvre
Keyword(s):  
Optimal Control ◽  
Riccati Equation ◽  
Stochastic Optimal Control ◽  
Optimal Control Problems ◽  
Control Problems ◽  
Dimensional Problem ◽  
Matrix Riccati Equation ◽  
One Dimensional ◽  
The Matrix ◽  
The One

The matrix Riccati equation that must be solved to obtain the solution to stochastic optimal control problems known as LQG homing is linearized for a class of processes. The results generalize a theorem proved by Whittle and the one-dimensional case already considered by the authors. A particular two-dimensional problem is solved explicitly.

A New Approach to the Solution of Linear Optimal Control Problems

1969 ◽  
Vol 91 (2) ◽  
pp. 149-154 ◽  
Author(s):  
W. J. Rugh ◽  
G. J. Murphy
Keyword(s):  
Optimal Control ◽  
Riccati Equation ◽  
Optimal Control Problems ◽  
Control Problems ◽  
Phase Variable ◽  
Linear Optimal Control ◽  
Matrix Riccati Equation ◽  
Terminal Time ◽  
Forcing Functions ◽  
Linear Optimal Control Problems

A simplified approach to the solution of linear optimal control problems with quadratic performance indexes is described in this paper. The phase-variable canonical form is used to develop a new type of optimal system equivalence. This concept leads to a substantial simplification of the matrix Riccati equation. The simplified matrix Riccati equation is of the same form for any problem of a given order, say, n, and contains only n nonzero forcing functions. That is, it always corresponds to a set of constant-coefficient scalar differential equations; in various nth-order problems the n nonzero forcing functions and the terminal conditions simply assume different forms. In a very strong sense, this simplified matrix Riccati equation is the simplest possible Riccati equation arising from optimization problems. The method is developed for general time-varying systems with finite terminal time. It is developed also for the important special case of time-invariant systems with infinite terminal time.

scholarly journals General LQG Homing Problems in One Dimension

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Mario Lefebvre ◽  
Foued Zitouni
Keyword(s):  
Optimal Control ◽  
Optimal Control Problems ◽  
Diffusion Processes ◽  
Approximate Solutions ◽  
One Dimension ◽  
Control Problems ◽  
One Dimensional ◽  
Controlled Processes ◽  
Dimensional Diffusion ◽  
The One

Optimal control problems for one-dimensional diffusion processes in the interval (d1,d2) are considered. The aim is either to maximize or to minimize the time spent by the controlled processes in (d1,d2). Exact solutions are obtained when the processes are symmetrical with respect to d∗∈(d1,d2). Approximate solutions are derived in the asymmetrical case. The one-barrier cases are also treated. Examples are presented.

scholarly journals A Novel High Dimensional Fitted Scheme for Stochastic Optimal Control Problems

2021 ◽  
Author(s):  
Christelle Dleuna Nyoumbi ◽  
Antoine Tambue
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Optimal Control ◽  
Finite Difference Method ◽  
Stochastic Optimal Control ◽  
Optimal Control Problems ◽  
Hjb Equation ◽  
High Dimensional ◽  
Control Problems ◽  
Volume Method

AbstractStochastic optimal principle leads to the resolution of a partial differential equation (PDE), namely the Hamilton–Jacobi–Bellman (HJB) equation. In general, this equation cannot be solved analytically, thus numerical algorithms are the only tools to provide accurate approximations. The aims of this paper is to introduce a novel fitted finite volume method to solve high dimensional degenerated HJB equation from stochastic optimal control problems in high dimension ($$ n\ge 3$$ n ≥ 3 ). The challenge here is due to the nature of our HJB equation which is a degenerated second-order partial differential equation coupled with an optimization problem. For such problems, standard scheme such as finite difference method losses its monotonicity and therefore the convergence toward the viscosity solution may not be guarantee. We discretize the HJB equation using the fitted finite volume method, well known to tackle degenerated PDEs, while the time discretisation is performed using the Implicit Euler scheme.. We show that matrices resulting from spatial discretization and temporal discretization are M-matrices. Numerical results in finance demonstrating the accuracy of the proposed numerical method comparing to the standard finite difference method are provided.

scholarly journals A fitted finite volume method for stochastic optimal control problems in finance

2021 ◽  
Vol 6 (4) ◽  
pp. 3053-3079
Author(s):  
Christelle Dleuna Nyoumbi ◽  
◽  
Antoine Tambue ◽  
◽  
Keyword(s):  
Optimal Control ◽  
Finite Volume Method ◽  
Finite Volume ◽  
Stochastic Optimal Control ◽  
Optimal Control Problems ◽  
Control Problems ◽  
Volume Method
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