
 Description:
DAL is an efficient and flexibible MATLAB toolbox for solving the following optimization problem:
minimize f(Ax) + lambda*c(x)
where A (m x n) is a design matrix, f is a loss function, and c is a measure of sparsity.
DAL can handle your favorite (convex, smooth) loss functions (squared loss, logistic loss, etc).
DAL can handle A (and its transpose) provided as function handles.
DAL can handle several "sparsity" measures in an unified way. Currently L1, grouped L1, and trace norm (testing, requires PROPACK) measures are supported.
DAL is efficient when m<<n (m: #samples, n: #unknowns) or the matrix A is poorly conditioned.
DAL is fast when the solution is sparse but the matrix A can be dense.
DAL is written in MATLAB.
 Changes to previous version:
 35% faster group lasso.
 Sparse connectivity inference example added (s_test_hsgl.m).
 Nonnegative lasso (thanks to Shigeyuki Oba).
 Uses Mark Tygert's pca.m for SVD (PROPACK is not required anymore).
 BibTeX Entry: Download
 Corresponding Paper BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Agnostic
 Data Formats: Agnostic
 Tags: Optimization, Trace Norm, Group Lasso, Lasso, Sparse Learning, L1 Regularization, Logistic Regression
 Archive: download here
Other available revisons

Version Changelog Date 1.1  Supports weighted lasso (dalsqal1.m, dallral1.m)
 Supports weighted squared loss (dalwl1.m)
 Bug fixes (group lasso and elasticnetregularized logistic regression)
February 18, 2014, 19:07:06 1.05  35% faster group lasso.
 Sparse connectivity inference example added (s_test_hsgl.m).
 Nonnegative lasso (thanks to Shigeyuki Oba).
 Uses Mark Tygert's pca.m for SVD (PROPACK is not required anymore).
May 3, 2011, 07:00:43 1.01  Logistic loss: : dallrl1.m, dallrgl.m, dallrds.m
 Unequalsized blocks supported in Group lasso regularization
 eta: initial eta=0.01/lambda
 dallrds.m: tracenorm regularized logistic regression (requires PROPACK)
December 14, 2009, 09:43:50 0.97 Initial Announcement on mloss.org.
April 13, 2009, 09:39:59
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