We discuss the relevance of coherent states based methods in signal processing applications, and illustrate with a couple of practical examples, namely problems of waveform detection in a noisy environment, and problems of spectral analysis for non stationary time series.

In this paper we present a linear programming solution for support recovery. Support recovery involves the estimation of sign pattern of a sparse signal from a set of randomly projected noisy measurements. Our solution of the problem amounts to solving min ‖Z‖1 s.t. Y = GZ, and quantizing/thresholding the resulting solution Z. We show that this scheme is guaranteed to perfectly reconstruct a di...

in this paper, we study the classical sensitivity analysis when the right - hand – side vector, and the coefficients of the objective function are allowed to vary.

Convex optimization problems arising in applications, possibly as approximations of intractable problems, are often structured and large scale. When the data are noisy, it is of interest to bound the solution error relative to the (unknown) solution of the original noiseless problem. Related to this is an error bound for the linear convergence analysis of first-order gradient methods for solvin...