added negativity constraints

1 files changed, 10 insertions, 6 deletions

diff --git a/jpa_test/convex_optimization.py b/jpa_test/convex_optimization.py
index ebd68e3..fdd321e 100644
--- a/jpa_test/convex_optimization.py
+++ b/jpa_test/convex_optimization.py
@@ -8,8 +8,8 @@ import cvxopt
 def l1regls(M_val, w_val):
     """
     Solves:
-        min - sum theta
-        s.t theta <= 0
+        min - sum_j theta_j
+        s.t theta_j <= 0
             |e^{M*theta} - (1 - w)|_2 <= 1
     """
     m, n = M_val.shape
@@ -25,23 +25,27 @@ def l1regls(M_val, w_val):
     y_diff = tensor.grad(y, theta_)
     y_hess = z[0] * theano.gradient.hessian(y, theta_)
     f_x = theano.function([theta], [y, y_diff], allow_input_downcast=True)
-    f_xz = theano.function([theta, z], [y, y_diff, y_hess])
+    f_xz = theano.function([theta, z], [y, y_diff, y_hess], allow_input_downcast=True)
 
     def F(x=None, z=None):
         if x is None:
             return 1, cvxopt.matrix(1.0, (n,1))
         elif z is None:
             y, y_diff = f_x(x)
-            return cvxopt.matrix(float(y), (1, 1)), cvxopt.matrix(y_diff.astype("float64")).T
+            return cvxopt.matrix(float(y), (1, 1)),\
+                    cvxopt.matrix(y_diff.astype("float64")).T
         else:
             y, y_diff, y_hess = f_xz(x, z)
             return cvxopt.matrix(float(y), (1, 1)), \
                     cvxopt.matrix(y_diff.astype("float64")).T, \
                     cvxopt.matrix(y_hess.astype("float64"))
 
-    return cvxopt.solvers.cpl(c,F)['x']
+    G = cvxopt.spdiag([1 for i in xrange(n)])
+    h = cvxopt.matrix(0.0, (n,1))
+
+    return cvxopt.solvers.cpl(c,F, G, h)['x']
 
 if __name__=="__main__":
     M_val = np.random.rand(100, 20)
     w_val = np.random.rand(100)
-    l1regls(M_val, w_val)
+    print l1regls(M_val, w_val)