This is not the latest version of this item. The latest version can be found here.
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms
Name
4754-convergence-rate-analysis-of-map-coordinate-minimization-algorithms.pdf
Description
Published version
Size
333.83 KB
Format
Adobe PDF
Checksum (MD5)
7fa1e69fc13af1c54be8d43c2a05dfa7
Author(s)
Meshi, Ofer
Jaakkola, Tommi
Globerson, Amir
Date Issued
2012
Citation
Meshi, Ofer, Jaakkola, Tommi and Globerson, Amir. 2012. "Convergence Rate Analysis of MAP Coordinate Minimization Algorithms."
Version
Final published version
Abstract
Finding maximum a posteriori (MAP) assignments in graphical models is an important task in many applications. Since the problem is generally hard, linear programming (LP) relaxations are often used. Solving these relaxations efficiently is thus an important practical problem. In recent years, several authors have proposed message passing updates corresponding to coordinate descent in the dual LP. However, these are generally not guaranteed to converge to a global optimum. One approach to remedy this is to smooth the LP, and perform coordinate descent on the smoothed dual. However, little is known about the convergence rate of this procedure. Here we perform a thorough rate analysis of such schemes and derive primal and dual convergence rates. We also provide a simple dual to primal mapping that yields feasible primal solutions with a guaranteed rate of convergence. Empirical evaluation supports our theoretical claims and shows that the method is highly competitive with state of the art approaches that yield global optima.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/4754-convergence-rate-analysis-of-map-coordinate-minimization-algorithms
