div canon stuff

Author: dance858

cvxpy/reductions/expr2smooth/canonicalizers/div_canon.py

@@ -16,11 +16,21 @@
 
 from cvxpy.expressions.variable import Variable
 
-
+# We canonicalize div(x, y) as z * y = x.
 def div_canon(expr, args):
-    denom = Variable(args[1].shape)
-    if args[1].value is not None:
-        denom.value = args[1].value
+    # TODO: potential bounds here?
+    # TODO: wrong dimension for z here
+    z = Variable(args[1].shape) 
+    y = Variable(args[1].shape, bounds=[0, None])
+
+    if args[1].value is not None and args[1].value != 0.0:
+        y.value = args[1].value
+    else:
+        y.value = expr.point_in_domain()
+
+    if args[0].value is not None:
+        z.value = args[0].value / y.value 
     else:
-        denom.value = expr.point_in_domain()
-    return expr.copy([args[0], denom]), [denom == args[1]]
+        z.value = expr.point_in_domain()
+    # TODO: should we also include y >= 0 here?
+    return z, [z * y == args[0], y == args[1]]#], #y >= 0, z >= 0]

cvxpy/reductions/solvers/nlp_solvers/ipopt_nlpif.py

@@ -153,6 +153,7 @@ def solve_via_data(self, data, warm_start: bool, verbose: bool, solver_opts, sol
         )
         nlp.add_option('mu_strategy', 'adaptive')
         nlp.add_option('tol', 1e-7)
+        nlp.add_option('honor_original_bounds', 'yes')
         nlp.add_option('hessian_approximation', "limited-memory")
         _, info = nlp.solve(x0)
         return info

cvxpy/tests/test_nlp_solvers.py

@@ -90,10 +90,10 @@ def test_mle(self):
         np.random.seed(1234)
         data = np.random.randn(n)
 
-        mu = cp.Variable(name="mu")
-        mu.value = np.array(0.0)
-        sigma = cp.Variable(name="sigma")
-        sigma.value = np.array(1.0)
+        mu = cp.Variable((1, ), name="mu")
+        mu.value = np.array([0.0])
+        sigma = cp.Variable((1, ), name="sigma")
+        sigma.value = np.array([1.0])
 
         constraints = [mu == sigma**2]
         residual_sum = cp.sum_squares(data - mu)