On the complexity of piecewise affine system identification
This addresses a fundamental computational bottleneck in hybrid system identification for control theory and machine learning, though it is incremental by extending prior results to a broader class of maps.
The paper tackles the computational complexity of identifying piecewise affine (PWA) maps from input-output data, showing that global optimality can be achieved for a more general class of PWA maps with polynomial complexity in data number but exponential complexity in data dimension, and proves the problem is NP-hard.
The paper provides results regarding the computational complexity of hybrid system identification. More precisely, we focus on the estimation of piecewise affine (PWA) maps from input-output data and analyze the complexity of computing a global minimizer of the error. Previous work showed that a global solution could be obtained for continuous PWA maps with a worst-case complexity exponential in the number of data. In this paper, we show how global optimality can be reached for a slightly more general class of possibly discontinuous PWA maps with a complexity only polynomial in the number of data, however with an exponential complexity with respect to the data dimension. This result is obtained via an analysis of the intrinsic classification subproblem of associating the data points to the different modes. In addition, we prove that the problem is NP-hard, and thus that the exponential complexity in the dimension is a natural expectation for any exact algorithm.