Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers


Resource | v1 | created by semantic-scholar-bot |
Type Paper
Created 2011-01-01
Identifier DOI: 10.1561/2200000016

Description

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.

Relations

is about Computer science

Computer science is the study of computation and information. Computer science deals with theory of c...

links to Compressed sensing

Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general lin...


Edit resource New resource

0.0 /10
useless alright awesome
from 0 reviews
Write comment Rate resource Tip: Rating is anonymous unless you also write a comment.
Resource level 0.0 /10
beginner intermediate advanced
Resource clarity 0.0 /10
hardly clear sometimes unclear perfectly clear
Reviewer's background 0.0 /10
none basics intermediate advanced expert
Comments 0
Currently, there aren't any comments.