Obtaining high resolution images from low data with clipped noise is algorithmically challenging due to the ill-posed nature of problem. So far such problems have hardly been tackled, and few existing approaches use simplistic regularisers. We show usefulness two adaptive regularisers based on anisotropic diffusion ideas: Apart evaluating classical edge-enhancing regulariser, we introduce a nov...

This note proposes a new algorithm for estimating spectral function from limited noisy Matsubara data. We consider both the molecule and condensed matter cases. In each case, constructs an interpolant of data uses conformal mapping Prony's method to estimate function. Numerical results are provided demonstrate performance algorithm.

In a recent article, we have shown how to learn controllers for unknown linear systems using finite-length noisy data by solving matrix inequalities. this extend approach deal with nonlinear polynomial formulating stability certificates in the form of data-dependent sum squares programs, whose solution directly provides stabilizing controller and Lyapunov function. We then derive variations res...