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| author | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-02-04 10:03:24 -0500 |
|---|---|---|
| committer | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-02-04 10:03:24 -0500 |
| commit | 92ea33105921065e52bce2c1ec12b81bd5ad0fd9 (patch) | |
| tree | d155d986f9153096b91aef97cf308b9302fb0e94 /src/convex_optimization.py | |
| parent | d91db65bf9c3db5e3dd53c41576b0fc1b866c8ce (diff) | |
| download | cascades-92ea33105921065e52bce2c1ec12b81bd5ad0fd9.tar.gz | |
adding figure
Diffstat (limited to 'src/convex_optimization.py')
| -rw-r--r-- | src/convex_optimization.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/src/convex_optimization.py b/src/convex_optimization.py index ed99e6b..3111e50 100644 --- a/src/convex_optimization.py +++ b/src/convex_optimization.py @@ -48,7 +48,7 @@ def sparse_recovery(lbda, n_cascades): return f_x, f_xz -def type_lasso(lbda): +def type_lasso(lbda, n_cascades): """ Solves: min - sum_j theta_j + lbda*|e^{M*theta} - (1 - w)|_2 @@ -90,7 +90,7 @@ def diff_and_opt(M_val, w_val, f_x, f_xz): def F(x=None, z=None): if x is None: - return 0, cvxopt.matrix(-.1, (n,1)) + return 0, cvxopt.matrix(-.00001, (n,1)) elif z is None: y, y_diff = f_x(x, M_val, w_val) return cvxopt.matrix(float(y), (1, 1)),\ |