Scipy Optimize Minimize: Optimization terminated successfully but not iterating at all
I am trying to code an optimizer finding the optimal constant parameters so as to minimize the MSE between an array y and a generic function over X. The generic function is given in pre-order, so for example if the function over X is x1 + c*x2 the function would be [+, x1, *, c, x2]. The objective in the previous example, would be minimizing:

sum_for_all_x  (y - (x1 + c*x2))^2

I show next what I have done to solve the problem. Some things that sould be known are:

X and y are torch tensors.
constants is the list of values to be optimized.


    def loss(self, constants, X, y):

        stack = []   # Stack to save the partial results
        const = 0    # Index of constant to be used
        for idx in self.traversal[::-1]:   # Reverse the prefix notation
            if idx &gt; Language.max_variables:   # If we are dealing with an operator
                function = Language.idx_to_token[idx]  # Get its associated function
                first_operand = stack.pop()    # Get first operand
                if function.arity == 1:   # If the arity of the operator is one (e.g sin)
                    stack.append(function.function(first_operand))   # Append result
                else:   # Same but if arity is 2
                    second_operand = stack.pop()  # Need a second operand
                    stack.append(function.function(first_operand, second_operand))
                
            elif idx == 0:  # If it is a constant -&gt; idx 0 indicates a constant
                stack.append(constants[const]*torch.ones(X.shape[0]))  # Append constant
                const += 1   # Update
            else:
                stack.append(X[:, idx - 1])   # Else append the associated column of X
        
        prediction = stack[0]
        return (y - prediction).pow(2).mean().cpu().numpy()


    def optimize_constants(self, X, y):
        '''
        # This function optimizes the constants of the expression tree.
        '''
        if 0 not in self.traversal:  # If there are no constants to be optimized return
            return self.traversal
        
        x0 = [0 for i in range(len(self.constants))]  # Initial guess
        ini = time.time()
        res = minimize(self.loss, x0, args=(X, y), method='BFGS', options={'disp': True})
        print(res)
        print('Time:', time.time() - ini)

The problem is that the optimizer theoretically terminates successfully but does not iterate at all. The output res would be something like that:
Optimization terminated successfully.
         Current function value: 2.920725
         Iterations: 0
         Function evaluations: 2
         Gradient evaluations: 1
      fun: 2.9207253456115723
 hess_inv: array([[1]])
      jac: array([0.])
  message: 'Optimization terminated successfully.'
     nfev: 2
      nit: 0
     njev: 1
   status: 0
  success: True
        x: array([0.])

So far I have tried to:

Change the method in the minimizer (e.g Nelder-Mead, SLSQP,...) but it happens the same with all of them.
Change the way I return  the result (e.g (y - prediction).pow(2).mean().item())


It seems that scipy optimize minimize does not work well with Pytorch. Changing the code to use numpy ndarrays solved the problem.
