Composite optimization offers a powerful modeling tool for a variety of applications and is
often numerically solved by means of proximal gradient methods. In this paper, we consider …

In this paper we consider low-rank estimation of room impulse responses (RIRs). Inspired by
a physics-driven room-acoustical model, we propose an estimator of RIRs that promotes a …

We consider the problem of tracking the minimum of a time-varying convex optimization
problem over a dynamic graph. Motivated by target tracking and parameter estimation …

Learned iterative shrinkage thresholding algorithm (LISTA), which adopts deep learning
techniques to optimize algorithm parameters from labeled training data, can be successfully …

We present FrankWolfe. jl, an open-source implementation of several popular Frank–Wolfe
and conditional gradients variants for first-order constrained optimization. The package is …

This paper investigates the recovery of a spectrally sparse signal (SSS) from partially
observed entries, with particular emphasis on computational efficiency for large scaled …

Multi-channel short-time Fourier transform (STFT) domain-based processing of reverberant
microphone signals commonly relies on power-spectral-density (PSD) estimates of early …

Block-sparse signal recovery has drawn increasing attention in many areas of signal
processing, where the goal is to recover a high-dimensional signal whose non-zero …

Due to their simplicity and versatility, splitting algorithms are often the methods of choice for
many optimization problems arising in engineering.“Splitting” complex problems into simpler …

Optimization problems with composite functions deal with the minimization of the sum of a
smooth function and a convex nonsmooth function. In this thesis several numerical methods …