Project details for DAL

Logo DAL 0.97

by ryota - April 13, 2009, 09:39:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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  • DAL is an efficient and flexibible 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.

  • DAL can handle A (and its transpose) provided as function handles.

  • DAL can handle several "sparsity" measures in an unified way. Currently L1 and grouped L1 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:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Binary
Tags: Optimization, Group Lasso, Lasso, Sparse Learning
Archive: download here


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