Name | Modified | Size | Downloads / Week |
---|---|---|---|
gampmatlab20210328.zip | 2021-03-28 | 11.6 MB | |
gampmatlab20171011.zip | 2017-10-12 | 11.6 MB | |
gampmatlab20161005.zip | 2016-10-05 | 11.5 MB | |
gampmatlab20151222.zip | 2015-12-22 | 11.5 MB | |
gampmatlab20150901.zip | 2015-09-02 | 11.4 MB | |
gampmatlab20141030.zip | 2014-10-30 | 11.3 MB | |
gampmatlab20141018.zip | 2014-10-19 | 11.2 MB | |
gampmatlab20141001.zip | 2014-10-02 | 11.2 MB | |
gampmatlab20140521.zip | 2014-05-22 | 11.2 MB | |
gampmatlab20140130.zip | 2014-01-30 | 11.2 MB | |
gampmatlab20131031.zip | 2013-10-31 | 11.2 MB | |
gampmatlab20131019a.zip | 2013-10-19 | 11.2 MB | |
gampmatlab20131019.zip | 2013-10-19 | 11.2 MB | |
gampmatlab20131018.zip | 2013-10-18 | 11.2 MB | |
gampmatlab20131016.zip | 2013-10-16 | 11.2 MB | |
gampmatlab20130729.zip | 2013-07-29 | 2.6 MB | |
gampmatlab20130617.zip | 2013-06-17 | 2.6 MB | |
gampmatlab20130616.zip | 2013-06-16 | 2.6 MB | |
gampmatlab20130610.zip | 2013-06-10 | 2.4 MB | |
gampmatlab20130520.zip | 2013-05-21 | 2.4 MB | |
gampmatlab20121018.zip | 2012-10-19 | 2.1 MB | |
gampmatlab20120425.zip | 2012-05-25 | 608.0 kB | |
gampmatlab20120418.zip | 2012-04-18 | 602.8 kB | |
gampmatlab20120324.zip | 2012-03-25 | 569.1 kB | |
gampmatlab20120322.zip | 2012-03-22 | 571.6 kB | |
gampmatlab20111128.zip | 2011-11-28 | 400.2 kB | |
gampmatlab20111122.zip | 2011-11-22 | 390.3 kB | |
gampmatlab20111101.zip | 2011-11-02 | 816.7 kB | |
README.txt | 2011-10-27 | 1.3 kB | |
Totals: 29 Items | 188.5 MB | 8 |
Thank you for downloading Generalized Approximate Message Passing (GAMP) MATLAB package. If you have any questions or difficulties, please contact Sundeep Rangan at srangan@poly.edu. Installation ------------ 1. Downloaded the file gampmatlab.zip from the SourceForge web page for GAMP, http://gampmatlab.sourceforge.net/. 2. Unzip the file gampmatlab.zip in any directory where you wish the files to reside. 3. The MATLAB files are in a subdirectory, code/main. Add that directory to your MATLAB path. You can use the MATLAB addpath command. 4. Within MATLAB, cd to code/test and run estimTest. This will run the GAMP algorithm for a Bernoulli-Gaussian sparse vector and compare the performance against a linear least-squares estimator. Right now, you can take a look at the file code/test/estimTest.m for an example of different estimators. Reading further ---------------- For more information, consult the User's Guide at: http://gampmatlab.sourceforge.net/wiki/index.php/Users%27_Guide The User's Guide has very little right now. There is a reference to one example, but I am hoping to find more. Also, due to a server error we are trying to debug, the webpage may time several minutes to open the first time. So, please be patient...