The quadratic Wasserstein metric for inverse data matching

This work characterizes, analytically and numerically, two major effects of the quadratic Wasserstein ( W 2 ) distance as the measure of data discrepancy in computational solutions of inverse problems. First, we show, in the infinite-dimensional setup, that the W 2 distance has a smoothing effect on the inversion process, making it robust against high-frequency noise in the data but leading to a reduced resolution for the reconstructed objects at a given noise level. Second, we demonstrate that, for some finite-dimensional problems, the W 2 distance leads to optimization problems that have better convexity than the classical L 2 and H ̇ − 1 distances, making it a more preferred distance to use when solving such inverse matching problems.