Different behaviour between MATLAB fmincon and scipy optimize minimize

问题

I'm translating some code from MATLAB to python. This code simulate the behaviour of a model and I want to estimate parameters from it. The problem is that results obtained with python and with MATLAB are very different. I've tought it was related to the difference between the MATLAB's fmincon and the python scipy.optimize.minimize function, but according to this tutorial that I've found on youtube (https://www.youtube.com/watch?v=SwogAa1719M) the results are almost the same,so problems must be in my code but I can't find them. I report a minimum example of my code

def cost_function(parameters, measured, t, x0, total_active):
    Tp = simulate(parameters, t, x0, total_active)
    measured = np.squeeze(measured)
    Tp = Tp[:,2]
    return (np.sum(np.power((np.divide(np.diff(measured), measured[1:])-np.divide(np.diff(Tp),Tp[1:])),2)))

def SIR_model(x, t, params, total_active):
    S0, _, R0 = x
    v, tau, I0 = params
    dSdt = - v * S0 * I0 / total_active(t)
    dIdt = v * S0 * I0 / total_active(t) - g * I0 - tau * I0
    dCdt = tau * I0
    return [dSdt, dIdt, dCdt]

def simulate(p, t, x0, total_active):
    T = np.zeros((len(t), 3))
    T[0, :] = x0
    for i in range(len(t) - 1):
        ts = [t[i], t[i + 1]]
        y = odeint(SIR_model, x0, ts, args=(p, total_active))
        x0 = y[-1]
        T[i + 1, :] = x0
    return T

def identify_model(data, initial_guess, t, N, total_active):
    # Set initial condition of the model
    x0 = []
    Q0 = data[0]
    x0.append(N - initial_guess['It0'] - Q0)
    x0.append(initial_guess['It0'])
    x0.append(Q0)
    It0 = initial_guess['It0']
    v = initial_guess['v']
    tau = initial_guess['tau']
    lim_sup = [v * 10, tau * 1.5, It0 * 1.3]
    lim_inf = [v * 0, tau * 0.9, It0 * 0.7]
    bounds = Bounds(lim_inf, lim_sup)
    options = {"maxiter": 1000,"ftol": 1e-08}
    return minimize(cost_function, np.asarray([initial_guess['v'], initial_guess['tau'],initial_guess['It0']]),args=(data, t, x0, total_active), bounds=bounds,options=options, tol=1e-08,method="SLSQP")['x']

data=[275.5,317.,457.33333333,646.,888.66666667,1236.66666667,1619.33333333,2077.33333333,2542.33333333]
times = [i for i in range(len(data))]
total_active_data=[59999725.,59999684.33333334,59999558.66666666,59999385.33333334,59999158.33333333,59998823.,59998474.66666666,59998053.33333333,59997652.66666666]
total_active = interp1d([i for i in range(len(total_active_data))], total_active_data, fill_value="extrapolate")
initial_guess = {"v": 0.97, "tau": 0.066, "It0": 100}
print(identify_model(data,initial_guess,times,60e6,total_active))

This snippet gives, as result, [0.97099097,0.099,130.]. The (I hope so) equivalent MATLAB code is:

function [pars] = Identify_Model(data,initial_guess,lim_inf,lim_sup,times,N,total_active)

scalefac=100;

%Initialize the initial guess for each parameter
v=initial_guess.v;
It0=initial_guess.It0;
tau=initial_guess.tau;
g=0.07;

lim_inf(3)=lim_inf(3)/scalefac;
lim_sup(3)=lim_sup(3)/scalefac;

%Identify the model parameters
options=optimset('MaxIter',100,'TolFun',1e-6,'TolX',1e-6);
parmin=fmincon(@(pars) error_SIR(data,[pars(1),pars(2),g,scalefac*pars(3)],times,N,total_active),[v,tau,It0],[],[],[],[],lim_inf,lim_sup,[],options);
pars=num2cell([parmin(1:2),scalefac*parmin(3)]);

pars=[pars(1),pars(2),g,pars(3)];

end



function [costo]=error_SIR(data,pars,tempi,N,totale_attivi)

%Assign the parameters
pars=num2cell(pars);
[v,tau,g,I0]=pars{:};
%Simulate the model
Q0=data(1,1);
S0=N-I0-Q0;

[~,y]=ode45(@(t,x) SIR(t,x,v,tau,g,totale_attivi),tempi,[S0;I0;Q0]);
if length(tempi)==2
    y=[y(1,:);y(end,:)];
end

%Identify on the normalized data (Data/mean data)
costo=sum(((diff(sum(data,1))./sum(data(:,2:end),1))-(diff(sum(y(:,3),2))./sum(y(2:end,3),2))').^2);
end

function y=SIR(t,x,v,tau,g,total_active)
    y=zeros(3,1);
    y(1)=-v*x(1)*x(2)/total_active(t);           % S
    y(2)=v*x(1)*x(2)/total_active(t)-(tau+g)*x(2);   % I
    y(3)=tau*x(2);      % C
end

total_active_data=[59999725.,59999684.333,59999558.666,59999385.333,59999158.33,59998823.,59998474.66,59998053.333,59997652.66666666]
total_active  = @(t) interp1(1:9,total_active_data,t); 
initial_guess.It0=100;
initial_guess.v=0.97;
initial_guess.g=0.07;  
initial_guess.tau=0.066;
g=initial_guess.g;
It0=initial_guess.It0;
v=initial_guess.v;
tau=initial_guess.tau;
N=60e6
%Define the constrints for the identification
lim_sup=[v*10, tau*1.5, It0*1.3];
lim_inf=[v*0, tau*0.9, It0*0.7];
data=[275.5,317.,457.33333333,646.,888.66666667,1236.66666667,1619.33333333,2077.33333333,2542.33333333]

times=1:9;
%identify the model parameters
pars=Identify_Model(data,initial_guess,lim_inf,lim_sup,times,N,total_active)

And the result is {[0.643004812025865]} {[0.0989999761533351]} {[0.07]} {[129.9999687237]} (don't consider the value 0.07, it's fixed). I thought that the problem could linked to the fact that I want to minimize a non-convex function, and maybe fmincon is more powerful than the scipy.optimize.minimize function?

来源：https://stackoverflow.com/questions/64790692/different-behaviour-between-matlab-fmincon-and-scipy-optimize-minimize