Work

Multiplicative updates for optimization problems with dynamics

Year: 2017

Type: conference-paper

Abstract: We consider the problem of optimizing general convex objective functions with nonnegativity constraints. Using the Karush-Kuhn-Tucker (KKT) conditions for the nonnegativity constraints we will derive fast multiplicative update rules for several problems of interest in signal processing, including non-negative deconvolution, point-process smoothing, ML estimation for Poisson Observations, nonnegative least squares and nonnegative matrix factorization (NMF). Our algorithm can also account for temporal and spatial structure and regularization. We will analyze the performance of our algorithm on simultaneously recorded neuronal calcium imaging and electrophysiology data.

Source: 2018 52nd Asilomar Conference on Signals, Systems, and Computers

DOI: 10.1109/acssc.2017.8335723

Authors: Abbas Kazemipour, Behtash Babadi, Min Wu, Kaspar Podgorski, Shaul Druckmann

Institutions: Howard Hughes Medical Institute, Janelia Research Campus, University of Maryland, College Park

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2949427653

FWCI: 1.255

Cites: 32

Cited by: 3

Related to: 10