What does Desired error not necessarily achieved due to precision loss mean in the context of the scipy_fmin methods? I can't seem to find an explanation anywhere.Here is my code:import mathimport numpyimport randomimport scipy.optimize as optimport matplotlib.pyplot as pltfrom numpy import arrayfrom numpy import dotfrom random import randintfrom numpy import matriximport sysns = []st = []lam_funtrix = []time_steps = 1000delta_t = 0.1mu = -0.7def gen_st():    global st    st = []    for i in range(0, time_steps):        st.append(random.normalvariate(0,1) * math.sqrt(delta_t))def f(val):    return math.exp(val)def get_lam(t):    rate = mu    return pow(delta_t, -1) * f(rate)def white_noise():    global ns    for i in range(0, time_steps):        lam = get_lam(i) * delta_t        spike_at_bin = numpy.random.poisson(lam)        ns.append(spike_at_bin)def gen_lam_log(i, mu):    rate = mu    return pow(delta_t, -1) * f(rate)def gen_lam_fun(mu):    global lam_funtrix    lam_funtrix = []    for i in range(0, time_steps):        lam_funtrix.append(gen_lam_log(i, mu))def log_like(t):    mu = t    gen_lam_fun(mu)    sum = 0    for i in range(0,time_steps):        val = lam_funtrix[i]        sum = sum - ((ns[i] * math.log(val*delta_t)) - (val*delta_t))    return sumdef der_mu():    sum = 0.0    for i in range(0, time_steps):        sum -= (ns[i] - lam_funtrix[i] * delta_t)    return sumdef first_der(t):    mu = t    gen_lam_fun(mu)    dm = der_mu()    return dmgen_st()white_noise()init_guess = array([0])vals = opt.fmin_cg(log_like, init_guess, fprime=first_der)print valsThe code is a  crumby since I pared it down a bit for the question.Warning: Desired error not necessarily achieved due to precision loss.         Current function value: 822.835581         Iterations: 1         Function evaluations: 18         Gradient evaluations: 6[-0.7943019]
Scipy min warning meanings?
python;numpy;scipy