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scipy.optimize.fim Error: need more than 1 value to unpack

I know that there are plenty of posts similar to mine but I really don't manage to apply them to my situation so please I ask for your help. Basically, my code is the following:

def Black_min(f, k, ann, vol, ex, cp):
    d1=(math.log(f/k)+0.5*vol**2*ex)/math.sqrt(vol**2*ex)
    d2=(math.log(f/k)-0.5*vol**2*ex)/math.sqrt(vol**2*ex)
    Price=cp*(f*norm.cdf(cp*d1)-k*norm.cdf(cp*d2))*ann*20000
    return Price

banana = lambda x: (Price_Cube[0][4]-Black_min(F[0], K[0][4], Annuity[0], x, Expiry[0], CP[4]))**2
xopt, fopt = scipy.optimize.fmin(func=banana, x0=[Vol_Cube[0][0]],  disp=False)

Price_Cube, K are lists of lists with numbers inside, Annuity, Expiry and CP are simply lists. When I run the code, I obtain this. 

    xopt, fopt = scipy.optimize.fmin(func=banana, x0=[Vol_Cube[0][0]],  disp=False)

ValueError: need more than 1 value to unpack

I'm aware that it is a silly mistake, but I really don't see it. Any help is appreciated, thanks in advance.

Solution

  • The problem is that you try to assign the return value of fmin to two variables, xopt and fopt, but by default fmin only returns xopt. You should try 

    xopt = scipy.optimize.fmin(func=banana, x0=[Vol_Cube[0][0]])

    If you also want the function value at the minimum, you have to set full_output to True, and then you also get some more diagnostics:

    xopt, fopt, iter, funccalls, warnflags = scipy.optimize.fmin(func=banana, x0=[Vol_Cube[0][0]], full_output=True)