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Check internal variables how does scipy.optimize.minimize work?

I usually check my variables with help pdbas my program runs. Now I want to debug mine scipy.optimize.minimizeby checking the variables when starting optimization.

result = scipy.optimize.minimize(fun, x0, method='dogleg')

I tried to use a parameter callback, but it does not give me access to internal optimization parameters, such as the Jacobian matrix, etc.

The returned one resulthas all the information I need, but it returns only at the end of the whole optimization, but I need (intermediate) results as the optimization proceeds.

So my question is: how do I return intermediate optimization parameters (e.g. Jacobian) when executed scipy.optimize.minimize?

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:

scipy , . , , . , . pdb.set_trace() . , , , :

scipy.optimize.__file__

. , .pyc. scipy , _minimize_trust_region.

"" :

, , scipy. , , , , - .

from scipy.optimize import minimize
import inspect

def fun(x):
    return (x - 42)**2

def jac(x):
    return 2*(x - 42)

def hess(x):
    return [[2]]

def vanilla_cb(x):
    print(x)

def callback_on_crack(x):
    print(inspect.currentframe().f_back.f_locals)
    print(x)

x0=99, 42 6 :

>>> minimize(fun,x0=99,method='dogleg',jac=jac,hess=hess,callback=vanilla_cb)
[ 98.]
[ 96.]
[ 92.]
[ 84.]
[ 68.]
[ 42.]

, dict!

>>> minimize(fun,99,method='dogleg',jac=jac,hess=hess,callback=callback_on_crack)
{'disp': False, 'unknown_options': {}, 'm': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9d10>, 'm_proposed': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9d10>, 'return_all': False, 'hess': <function function_wrapper at 0x1248938>, 'callback': <function callback_on_crack at 0x12487d0>, 'nhessp': [0], 'njac': [1], 'predicted_reduction': array([ 113.]), 'subproblem': <class 'scipy.optimize._trustregion_dogleg.DoglegSubproblem'>, 'maxiter': 200, 'warnflag': 0, 'gtol': 0.0001, 'args': (), 'initial_trust_radius': 1.0, 'hits_boundary': True, 'trust_radius': 2.0, 'predicted_value': array([ 3136.]), 'rho': array([ 1.]), 'x': array([ 98.]), 'nhess': [1], 'x0': array([ 99.]), 'hessp': None, 'k': 0, 'actual_reduction': array([ 113.]), 'jac': <function function_wrapper at 0x12488c0>, 'p': array([-1.]), 'eta': 0.15, 'fun': <function function_wrapper at 0x1248848>, 'nfun': [2], 'max_trust_radius': 1000.0, 'x_proposed': array([ 98.])}
[ 98.]
{'disp': False, 'unknown_options': {}, 'm': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9d90>, 'm_proposed': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9d90>, 'return_all': False, 'hess': <function function_wrapper at 0x1248938>, 'callback': <function callback_on_crack at 0x12487d0>, 'nhessp': [0], 'njac': [2], 'predicted_reduction': array([ 220.]), 'subproblem': <class 'scipy.optimize._trustregion_dogleg.DoglegSubproblem'>, 'maxiter': 200, 'warnflag': 0, 'gtol': 0.0001, 'args': (), 'initial_trust_radius': 1.0, 'hits_boundary': True, 'trust_radius': 4.0, 'predicted_value': array([ 2916.]), 'rho': array([ 1.]), 'x': array([ 96.]), 'nhess': [2], 'x0': array([ 99.]), 'hessp': None, 'k': 1, 'actual_reduction': array([ 220.]), 'jac': <function function_wrapper at 0x12488c0>, 'p': array([-2.]), 'eta': 0.15, 'fun': <function function_wrapper at 0x1248848>, 'nfun': [3], 'max_trust_radius': 1000.0, 'x_proposed': array([ 96.])}
[ 96.]
{'disp': False, 'unknown_options': {}, 'm': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9e50>, 'm_proposed': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9e50>, 'return_all': False, 'hess': <function function_wrapper at 0x1248938>, 'callback': <function callback_on_crack at 0x12487d0>, 'nhessp': [0], 'njac': [3], 'predicted_reduction': array([ 416.]), 'subproblem': <class 'scipy.optimize._trustregion_dogleg.DoglegSubproblem'>, 'maxiter': 200, 'warnflag': 0, 'gtol': 0.0001, 'args': (), 'initial_trust_radius': 1.0, 'hits_boundary': True, 'trust_radius': 8.0, 'predicted_value': array([ 2500.]), 'rho': array([ 1.]), 'x': array([ 92.]), 'nhess': [3], 'x0': array([ 99.]), 'hessp': None, 'k': 2, 'actual_reduction': array([ 416.]), 'jac': <function function_wrapper at 0x12488c0>, 'p': array([-4.]), 'eta': 0.15, 'fun': <function function_wrapper at 0x1248848>, 'nfun': [4], 'max_trust_radius': 1000.0, 'x_proposed': array([ 92.])}
[ 92.]
{'disp': False, 'unknown_options': {}, 'm': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9dd0>, 'm_proposed': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9dd0>, 'return_all': False, 'hess': <function function_wrapper at 0x1248938>, 'callback': <function callback_on_crack at 0x12487d0>, 'nhessp': [0], 'njac': [4], 'predicted_reduction': array([ 736.]), 'subproblem': <class 'scipy.optimize._trustregion_dogleg.DoglegSubproblem'>, 'maxiter': 200, 'warnflag': 0, 'gtol': 0.0001, 'args': (), 'initial_trust_radius': 1.0, 'hits_boundary': True, 'trust_radius': 16.0, 'predicted_value': array([ 1764.]), 'rho': array([ 1.]), 'x': array([ 84.]), 'nhess': [4], 'x0': array([ 99.]), 'hessp': None, 'k': 3, 'actual_reduction': array([ 736.]), 'jac': <function function_wrapper at 0x12488c0>, 'p': array([-8.]), 'eta': 0.15, 'fun': <function function_wrapper at 0x1248848>, 'nfun': [5], 'max_trust_radius': 1000.0, 'x_proposed': array([ 84.])}
[ 84.]
{'disp': False, 'unknown_options': {}, 'm': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9e10>, 'm_proposed': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9e10>, 'return_all': False, 'hess': <function function_wrapper at 0x1248938>, 'callback': <function callback_on_crack at 0x12487d0>, 'nhessp': [0], 'njac': [5], 'predicted_reduction': array([ 1088.]), 'subproblem': <class 'scipy.optimize._trustregion_dogleg.DoglegSubproblem'>, 'maxiter': 200, 'warnflag': 0, 'gtol': 0.0001, 'args': (), 'initial_trust_radius': 1.0, 'hits_boundary': True, 'trust_radius': 32.0, 'predicted_value': array([ 676.]), 'rho': array([ 1.]), 'x': array([ 68.]), 'nhess': [5], 'x0': array([ 99.]), 'hessp': None, 'k': 4, 'actual_reduction': array([ 1088.]), 'jac': <function function_wrapper at 0x12488c0>, 'p': array([-16.]), 'eta': 0.15, 'fun': <function function_wrapper at 0x1248848>, 'nfun': [6], 'max_trust_radius': 1000.0, 'x_proposed': array([ 68.])}
[ 68.]
{'disp': False, 'unknown_options': {}, 'm': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9d50>, 'm_proposed': <scipy.optimize._trustregion_dogleg.DoglegSubproblem object at 0xdc9d50>, 'return_all': False, 'hess': <function function_wrapper at 0x1248938>, 'callback': <function callback_on_crack at 0x12487d0>, 'nhessp': [0], 'njac': [6], 'predicted_reduction': array([ 676.]), 'subproblem': <class 'scipy.optimize._trustregion_dogleg.DoglegSubproblem'>, 'maxiter': 200, 'warnflag': 0, 'gtol': 0.0001, 'args': (), 'initial_trust_radius': 1.0, 'hits_boundary': False, 'trust_radius': 32.0, 'predicted_value': array([ 0.]), 'rho': array([ 1.]), 'x': array([ 42.]), 'nhess': [6], 'x0': array([ 99.]), 'hessp': None, 'k': 5, 'actual_reduction': array([ 676.]), 'jac': <function function_wrapper at 0x12488c0>, 'p': array([-26.]), 'eta': 0.15, 'fun': <function function_wrapper at 0x1248848>, 'nfun': [7], 'max_trust_radius': 1000.0, 'x_proposed': array([ 42.])}
[ 42.]
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Source: https://habr.com/ru/post/1671744/

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