Approximation has been shown to be an eective method for reducing the time and space costs of solving various oorplan area minimization problems. In this paper, we present several approximation techniques for solving
oorplan area minimization problems. These new techniques enable us to reduce both the time and space complexities of the previously best known approximation algorithms by more than a factor of n and n 2 for rectangular and L-shaped sub-
oorplans, respectively (where n is the number of given implementations). The eciency in the time and space complexities is critical to the applicability of such approximation techniques in
oorplan area minimization algorithms. We also give a technique for enhancing the quality of approximation results.
Danny Z. Chen, Xiaobo Hu