  In this paper, we focus on a method based on optimal control to address the
optimization problem. The objective is to find the optimal solution that
minimizes the objective function. We transform the optimization problem into
optimal control by designing an appropriate cost function. Using Pontryagin's
Maximum Principle and the associated forward-backward difference equations
(FBDEs), we derive the iterative update gain for the optimization. The steady
system state can be considered as the solution to the optimization problem.
Finally, we discuss the compelling characteristics of our method and further
demonstrate its high precision, low oscillation, and applicability for finding
different local minima of non-convex functions through several simulation
examples.
