Weekly Seminar – 11/18/2016

Gauri Jagatap, from Dr. Chinmay Hegde’s group will be presenting an overview of phase retrieval problems in signal processing, this Friday. She will primarily speak on a popular phase recovery strategy called AltMinPhase, based on the paper “Phase Retrieval Using Alternating Minimization” by Praneeth Netrapalli, Prateek Jain, and Sujay Sanghavi.

She will later also introduce a newer approach for phase retrieval of sparse signals, called “Efficient Compressive Phase Retrieval with Constrained Sensing Vectors“, by Sohail Bahmani, Justin Romberg.

Phase retrieval is essentially the problem of recovering the phase of a signal from magnitude measurements. In several applications in crystallography, optics, spectroscopy and tomography, it is harder or infeasible to record the phase of measurements, while recording the magnitudes is significantly easier.

Date: 11/18/2016

Time: 3:00pm – 4:00pm

Venue: 2222, Coover

Slides:Phase Retrieval (updated)

Weekly Seminar – 11/11/2016

Davood Hajinezhad from Dr. Mingyi Hong’s group will be presenting this week on “A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization”. Details of the talk are as given below:

Abstract: We study a stochastic and distributed algorithm for nonconvex  problems whose objective consists of a sum of $latex N$ nonconvex $latex L_i/N$-smooth functions, plus a  nonsmooth regularizer. The proposed NonconvEx primal-dual SpliTTing (NESTT) algorithm splits the problem into $latex N$ subproblems, and utilizes an augmented Lagrangian based primal-dual scheme to solve it in a distributed and stochastic manner. With a special non-uniform sampling, a version of NESTT achieves $latex \epsilon$-stationary solution  using $latex O((\sum_{i=1}^N\sqrt{L_i/N})^2/\epsilon)$ gradient evaluations, which can be up to $latex O(N)$ times better than the (proximal) gradient descent methods. It also achieves Q-linear convergence rate for nonconvex $latex \ell_1$ penalized quadratic problems with polyhedral constraints. Further, we reveal  a fundamental connection between primal-dual based methods and a few primal only methods such as IAG/SAG/SAGA.

Date: 11th November
Venue: 2222, Coover
Time: 3:00pm to 4:00pm
Slides: NESTT

Weekly Seminar – 11/04/2016

Han Guo from Dr. Namrata Vaswani’s group will be presenting this week, on the topic of Video Denoising and Enhancement via Dynamic Sparse and Low-rank Matrix Decomposition. Details of the talk are as follows:

Abstract: Video denoising refers to the problem of removing “noise” from a video sequence. Here the term “noise” is used in a broad sense to refer to any corruption or outlier or interference that is not the quantity of interest. In this work, we develop a novel approach to video denoising that is based on the idea that many noisy or corrupted videos can be split into three parts – the “low-rank layer”, the “sparse layer”, and everything else (which is small and bounded). We show, using extensive experiments, that our denoising approach ReLD (ReProCS-based Layering Denoising) outperforms the state-of-the art denoising algorithms.
Date: 4th November
Venue: 2222, Coover
Time: 3:00pm to 4:00pm
You can find the slides here.