Sorry, you need to enable JavaScript to visit this website.

On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere

Citation Author(s):
Trung Vu, Raviv Raich, Xiao Fu
Submitted by:
Trung Vu
Last updated:
26 October 2019 - 2:40pm
Document Type:
Presentation Slides
Document Year:
2019
Event:
Presenters Name:
Trung Vu
Paper Code:
MLSP-L6.2

Abstract 

Abstract: 

Unit sphere-constrained quadratic optimization has been studied extensively over the past decades. While state-of-art algorithms for solving this problem often rely on relaxation or approximation techniques, there has been little research into scalable first-order methods that tackle the problem in its original form. These first-order methods are often more well-suited for the big data setting. In this paper, we provide a novel analysis of the simple projected gradient descent method for minimizing a quadratic over a sphere. When the gradient step size is sufficiently small, we show that convergence is locally linear and provide a closed-form expression for the rate. Moreover, a careful selection of the step size can stimulate convergence to the global solution while preventing convergence to local minima.

up
0 users have voted:

Dataset Files

MLSP2019.pdf

(202)