def as_gpconstr(self, x0):
"Returns locally-approximating GP constraint"
# Creating a default constraint for the first solve
if self.x not in x0:
return (self.y >= self.x)
# Otherwise calls external code at the current position...
x_star = x0[self.x]
res = external_code(x_star)
# ...and returns a linearized posy <= 1
return (self.y >= res * self.x / x_star)
def as_gpconstr(self, x0):
# Unpacking the GPkit variables
x = self.x
y = self.y
# Creating a default constraint for the first solve
if not x0:
return (y >= x)
# Returns constraint updated with new call to the external code
else:
# Unpack Design Variables at the current point
x_star = x0["x"]
# Call external code
res = external_code(x_star)
# Return linearized constraint
return (y >= res*x/x_star)
def as_gpconstr(self, x0, substitutions=None):
# Unpacking the GPkit variables
x = self.x
y = self.y
# Creating a default constraint for the first solve
if not x0:
return (y >= x)
# Returns constraint updated with new call to the external code
else:
# Unpack Design Variables at the current point
x_star = x0["x"]
# Call external code
res = external_code(x_star)
# Return linearized constraint
return (y >= res*x/x_star)
def as_gpconstr(self, x0):
"Returns locally-approximating GP constraint"
# Unpacking the GPkit variables
x = self.x
y = self.y
# Creating a default constraint for the first solve
if not x0:
return (y >= x)
# Returns constraint updated with new call to the external code
else:
# Unpack Design Variables at the current point
x_star = x0["x"]
# Call external code
res = external_code(x_star)
# Return linearized constraint
posynomial_constraint = (y >= res*x/x_star)
posynomial_constraint.sgp_parent = self
return posynomial_constraint