updates to some canons

Author: dance858

cvxpy/reductions/dnlp2smooth/canonicalizers/div_canon.py

@@ -17,39 +17,64 @@
 import numpy as np
 
 from cvxpy.atoms.affine.binary_operators import multiply
+from cvxpy.atoms.elementwise.power import power
 from cvxpy.expressions.variable import Variable
+from cvxpy.utilities.warn import warn
 
 MIN_INIT = 1e-3
 
-# We canonicalize div(f(x), g(x)) as z * y = f(x), y = g(x), y >= 0.
-# In other words, it assumes that the denominator is nonnegative.
+# We canonicalize div(f(x), g(x)) as f(x) * power(z, -1), z = g(x), z >= 0.
 def div_canon(expr, args):
 
-    # raise an error if the denominator is not nonnegative
     if not args[1].is_nonneg():
-        raise ValueError("The denominator of a division must be nonnegative. "
-                          "Did you forget to specify bounds?")
+        warn(
+            "CVXPY (DNLP) could not verify that the denominator of a division "
+            "appearing in your problem is nonnegative. The solver will proceed "
+            "under the assumption that the denominator is nonnegative. If this "
+            "assumption is incorrect, the solution may be invalid. "
+        )
 
-    dim = args[0].shape
-    sgn_z = args[0].sign
-
-    if sgn_z == 'NONNEGATIVE':
-        z = Variable(dim, nonneg=True)
-    elif sgn_z == 'NONPOSITIVE':
-        z = Variable(dim, nonpos=True)
-    else:
-        z = Variable(dim)
-    
-    y = Variable(args[1].shape, nonneg=True)
+    z = Variable(args[1].shape, nonneg=True)
 
-    if args[0].value is not None and args[1].value is not None:
-        y.value = np.maximum(args[1].value, MIN_INIT)
-        val = args[0].value / y.value
-    
-        # dimension hack
-        if dim == () and val.shape == (1,):
-            z.value = val[0]
-        else:
-            z.value = val
+    if args[1].value is not None:
+        z.value = np.maximum(args[1].value, MIN_INIT)
 
-    return z, [multiply(z, y) == args[0], y == args[1]]
+    # TODO (dance858): should. multiply and power be other canons?
+    return multiply(args[0], power(z, -1)), [z == args[1]]
+
+
+# ----------------------------------------------------------------------------------
+#                                Old version
+# ----------------------------------------------------------------------------------
+# We canonicalize div(f(x), g(x)) as z * y = f(x), y = g(x), y >= 0.
+# In other words, it assumes that the denominator is nonnegative.
+#def div_canon_old(expr, args):
+#
+#    # raise an error if the denominator is not nonnegative
+#    if not args[1].is_nonneg():
+#        raise ValueError("The denominator of a division must be nonnegative. "
+#                          "Did you forget to specify bounds?")
+#
+#    dim = args[0].shape
+#    sgn_z = args[0].sign
+#
+#    if sgn_z == 'NONNEGATIVE':
+#        z = Variable(dim, nonneg=True)
+#    elif sgn_z == 'NONPOSITIVE':
+#        z = Variable(dim, nonpos=True)
+#    else:
+#        z = Variable(dim)
+#
+#    y = Variable(args[1].shape, nonneg=True)
+#
+#    if args[0].value is not None and args[1].value is not None:
+#        y.value = np.maximum(args[1].value, MIN_INIT)
+#        val = args[0].value / y.value
+#
+#        # dimension hack
+#        if dim == () and val.shape == (1,):
+#            z.value = val[0]
+#        else:
+#            z.value = val
+#
+#    return z, [multiply(z, y) == args[0], y == args[1]]

cvxpy/reductions/dnlp2smooth/canonicalizers/geo_mean_canon.py

@@ -25,9 +25,6 @@
 MIN_INIT = 1e-3
 
 def geo_mean_canon(expr, args):
-    """
-    Canonicalization for the geometric mean function.
-    """
     t = Variable(expr.shape, nonneg=True)
 
     if args[0].value is not None:

cvxpy/reductions/dnlp2smooth/canonicalizers/multiply_canon.py

@@ -18,28 +18,10 @@
 from cvxpy.expressions.variable import Variable
 
 
-# If a user insert x * x where x is a variable it gets canonicalized to
-# square(x) before this function is called.
+# TODO: (dance858) do we need to copy things? Do we need to canonicalize at all?
+# Can we have a common canonicalizer for binary operators with full domain?
 def multiply_canon(expr, args):
-    t1 = args[0]
-    t2 = args[1]
-    constraints = []
-
-    # if either is constant, no canonicalization needed
-    #if t1.is_constant() or t2.is_constant():
-    #    return expr.copy([t1, t2]), []
-#
-    #if not isinstance(t1, Variable):
-    #    t1 = Variable(t1.shape)
-    #    constraints += [t1 == args[0]]
-    #    t1.value = args[0].value
-#
-    #if not isinstance(t2, Variable):
-    #    t2 = Variable(t2.shape)
-    #    constraints += [t2 == args[1]]
-    #    t2.value = args[1].value
-
-    return expr.copy([t1, t2]), constraints
+    return expr.copy(args), []
 
 def matmul_canon(expr, args):
     t1 = args[0]
@@ -61,3 +43,27 @@ def matmul_canon(expr, args):
         t2.value = args[1].value
 
     return expr.copy([t1, t2]), constraints
+
+# ----------------------------------------------------------------------------------
+#                                Old versions
+# ----------------------------------------------------------------------------------
+#def multiply_canon_old(expr, args):
+#    t1 = args[0]
+#    t2 = args[1]
+#    constraints = []
+#
+#    # if either is constant, no canonicalization needed
+#    if t1.is_constant() or t2.is_constant():
+#        return expr.copy([t1, t2]), []
+#
+#    if not isinstance(t1, Variable):
+#        t1 = Variable(t1.shape)
+#        constraints += [t1 == args[0]]
+#        t1.value = args[0].value
+#
+#    if not isinstance(t2, Variable):
+#        t2 = Variable(t2.shape)
+#        constraints += [t2 == args[1]]
+#        t2.value = args[1].value
+#
+#    return expr.copy([t1, t2]), constraints

cvxpy/reductions/dnlp2smooth/canonicalizers/pnorm_canon.py

@@ -25,8 +25,10 @@ def pnorm_canon(expr, args):
     shape = expr.shape
     t = Variable(shape, nonneg=True)
 
-    # expression will always have a value here in DNLP
-    t.value = expr.value
+    # in DNLP, we expect the expression to always have a value here,
+    # but we add this check to get an informative error message later.
+    if expr.value is not None:
+        t.value = expr.value
 
     # we canonicalize 2-norm as follows:
     # ||x||_2 <= t  <=>  quad_over_lin(x, t) <= t

cvxpy/reductions/dnlp2smooth/canonicalizers/power_canon.py

@@ -33,14 +33,6 @@ def power_canon(expr, args):
         return x, []
     elif isinstance(p, int) and p > 1:
         return expr.copy([x]), []
-        if isinstance(x, Variable):
-            return expr.copy(args), []
-
-        t = Variable(shape)
-        if x.value is not None:
-            t.value = x.value
-        
-        return expr.copy([t]), [t == x]
     elif p > 0:
         t = Variable(shape, nonneg=True)