We characterize the solution of a broad class of convex optimization problems that address
the reconstruction of a function from a finite number of linear measurements. The underlying …

We introduce a general framework for the reconstruction of periodic multivariate functions
from finitely many and possibly noisy linear measurements. The reconstruction task is …

We study the problem of one-dimensional regression of data points with total-variation (TV)
regularization (in the sense of measures) on the second derivative, which is known to …

We formulate as an inverse problem the construction of sparse parametric continuous curve
models that fit a sequence of contour points. Our prior is incorporated as a regularization …

In this paper, we propose a unified theoretical and practical spherical approximation
framework for functional inverse problems on the hypersphere S d− 1. More specifically, we …

We study the problem of interpolating one-dimensional data with total variation
regularization on the second derivative, which is known to promote piecewise-linear …

Beside the minimizationof the prediction error, two of the most desirable properties of a
regression scheme are stability and interpretability. Driven by these principles, we propose …

This paper introduces a novel framework and corresponding methods for sampling and
reconstruction of sparse signals in shift-invariant (SI) spaces. We reinterpret the random …

We study the problem of recovering piecewise-polynomial periodic functions from their low-
frequency information. This means that we only have access to possibly corrupted versions …

We present a novel framework for the reconstruction of 1D composite signals assumed to be
a mixture of two additive components, one sparse and the other smooth, given a finite …