Description:

• 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 mloss.org.

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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