A comparative study on using linear programming and simulated annealing in the optimal realization of a SC filter

摘要

The switched-capacitor (SC) circuit realization problem is traditionally solved by heuristic algorithms. However, an algorithm-like simulated annealing (SA) is stochastic, and its behavior in solving a non-convex optimization problem is unpredictable. In this paper, we make an investigation on using a deterministic and a stochastic optimization algorithm for solving the realization problem of the classical Fleischer-Laker SC filter. By considering minimum area as the design goal, we prove that the a linear programming-based deterministic algorithm is capable of finding a global minimum. With the global optimality established, we then use an SA algorithm to solve the same problem in purpose of investigating the search capability of the SA algorithm. We find that the stochastic SA algorithm cannot always reach a suboptimal solution with quality comparable with the linear programming result. Other issues like convergence speed and the percentage of arriving at the global minimum are examined as well. This research exposes that understanding the underlying optimization problem structure for the realization of SC circuits is of fundamental meaning for developing more efficient heuristic algorithms. Copyright (c) 2017 John Wiley & Sons, Ltd.