Save Intermediate Results In Minimization Problem

I am running a minimization problem using the next code: import numpy as np from scipy.optimize import minimize Nfeval = 1 def objective_fnc(x): .... return y def callba

Solution 1:

I'd suggest something like this:

import numpy as np

class CallbackFunctor:
    def __init__(self, obj_fun):
        self.best_fun_vals = [np.inf]
        self.best_sols = []
        self.num_calls = 0
        self.obj_fun = obj_fun
    
    def __call__(self, x):
        fun_val = self.obj_fun(x)
        self.num_calls += 1
        if fun_val < self.best_fun_vals[-1]:
            self.best_sols.append(x)
            self.best_fun_vals.append(fun_val)
   
    def save_sols(self, filename):
        sols = np.array([sol for sol in self.best_sols])
        np.savetxt(filename, sols)

Here, you don't need ugly global variables and the callback saves each now found solution, i.e. a solution with a lower objective function value than the last found solution. Example usage:

cb = CallbackFunctor(objective_fun)
res = minimize(objective_fun, x0=x0, callback=cb)

print(cb.best_sols)       # contains all your collected solutions
print(cb.best_fun_vals)   # contains the corresponding objective function values
cb.save_sols("dummy.txt") # writes all solutions to a file 'dummy.txt'

---

However, in case you really want to write all solutions to a file for each new found solution, you can modify the callback such that it calls save_sol after each new solution:

import numpy as np

class CallbackFunctor:
    def __init__(self, obj_fun, filename):
        self.best_fun_vals = [np.inf]
        self.best_sols = []
        self.num_calls = 0
        self.obj_fun = obj_fun
        self.filename = filename
    
    def __call__(self, x):
        fun_val = self.obj_fun(x)
        self.num_calls += 1
        if fun_val < self.best_fun_vals[-1]:
            self.best_sols.append(x)
            self.best_fun_vals.append(fun_val)
            self.save_sols(self.filename)
   
    def save_sols(self, filename):
        sols = np.array([sol for sol in self.best_sols])
        np.savetxt(filename, sols)

cb = CallbackFunctor(objective_fun, "dummy.txt")
res = minimize(objective_fun, x0=x0, callback=cb)