DAL 1.1

Ryota Tomioka — Tue, 18 Feb 2014 19:07:06 -0000

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.

mloss.org DAL