Hello, I have written a very simple code that computes the gradient by finite differences of any general function. Keeping the same idea, I would like modify the code using numpy to make it faster. Any ideas? Thanks. def grad_finite_dif(self,x,user_data = None): assert len(x) == self.number_variables points=[] for j in range(self.number_variables): points.append(x.copy()) points[len(points)-1][j]=points[len(points)-1][j]+0.0000001 delta_f = [] counter=0 for j in range(self.number_variables): delta_f.append((self.eval(points[counter])-self.eval(x))/0.0000001) counter = counter + 1 return array(delta_f) -- View this message in context: http://old.nabble.com/Improvement-of-performance-tp28452458p28452458.html Sent from the Numpy-discussion mailing list archive at Nabble.com.