This paper analyzes the limiting behavior of stochastic linear-quadratic optimal control problems in finite time-horizon [0, T] as T → ∞. The so-called turnpike properties are established for such problems, under stabilizability condition which is weaker than controllability, normally imposed similar problem ordinary differential systems. In dealing with problem, a crucial issue to determine co...

<p style='text-indent:20px;'>In this work, we have proposed a new approach for solving the linear-quadratic optimal control problem, where quality criterion is quadratic function, which can be convex or non-convex. In approach, transform continuous problem into optimization using Cauchy discretization technique, then solve it with active-set method. order to study efficiency and accuracy ...

Abstract This paper studies a continuous‐time stochastic linear‐quadratic (SLQ) optimal control problem on infinite‐horizon. Combining the Kronecker product theory with an existing policy iteration algorithm, data‐driven algorithm is proposed to solve problem. In contrast most methods that need all information of system coefficients, eliminates requirement three matrices by utilizing data syste...

We present a spatial domain decomposition (DD) method for the solution of discretized parabolic linear–quadratic optimal control problems. Our DD preconditioners are extensions of Neumann-Neumann DD methods, which have been successfully applied to the solution of single elliptic partial differential equations and of linear–quadratic optimal control problems governed by elliptic equations. We us...

  Semidefinite optimization relaxations are among the widely used approaches to find global optimal or approximate solutions for many nonconvex problems. Here, we consider a specific quadratically constrained quadratic problem with an additional linear constraint. We prove that under certain conditions the semidefinite relaxation approach enables us to find a global optimal solution of the unde...