Using several initial Guesses in Optimization function
Multi tool use
A project I am currently working on requires an optimization with regards to a function.
In order to make the optimization process more reliable in terms of finding the global minimum, I would like to use several initial guesses for each variable that I can specify beforehand.
In the following I randomized the lists of initial guesses (ipp_term, ipp_ci, i_risky_w) for demonstrating the lists of initial guesses.
Theses lists I would like to input as seeds (line 13) then.
upper_term = max(term)
upper_ci = max(ci)
ipp_term = np.random.uniform(0,upper_term,30)
ipp_ci = np.random.uniform(0,upper_ci,30)
i_risky_w = np.random.uniform(0,1,30)
bds = [(0,1), (0,upper_term), (0,upper_ci)]
scipy.optimize.differential_evolution(
optInsInv_opt, bounds = bds, strategy='best1bin', maxiter=None,
popsize=1, tol=0.00001, mutation=(0.5, 1), recombination=0.7,
**seed=[i_risky_w, ipp_term, ipp_ci, i_risky_w],**
callback=None, disp=True, polish=True, init='latinhypercube')
From the documentation I understand that there are two methods for initializing the populated parameters, but from my understanding they are also randomly generated!?
Does anyone know how to alter the scipy.optimize.differential_evolution package so that I can use my own lists of initial guesses or can tell me another library with similiar algorithm that can do that?
Thanks for your help!
optimization scipy initialization differential-evolution
asked Dec 28 '18 at 3:07
Max
256
add a comment |
A project I am currently working on requires an optimization with regards to a function.
In order to make the optimization process more reliable in terms of finding the global minimum, I would like to use several initial guesses for each variable that I can specify beforehand.
In the following I randomized the lists of initial guesses (ipp_term, ipp_ci, i_risky_w) for demonstrating the lists of initial guesses.
Theses lists I would like to input as seeds (line 13) then.
upper_term = max(term)
upper_ci = max(ci)
ipp_term = np.random.uniform(0,upper_term,30)
ipp_ci = np.random.uniform(0,upper_ci,30)
i_risky_w = np.random.uniform(0,1,30)
bds = [(0,1), (0,upper_term), (0,upper_ci)]
scipy.optimize.differential_evolution(
optInsInv_opt, bounds = bds, strategy='best1bin', maxiter=None,
popsize=1, tol=0.00001, mutation=(0.5, 1), recombination=0.7,
**seed=[i_risky_w, ipp_term, ipp_ci, i_risky_w],**
callback=None, disp=True, polish=True, init='latinhypercube')
From the documentation I understand that there are two methods for initializing the populated parameters, but from my understanding they are also randomly generated!?
Does anyone know how to alter the scipy.optimize.differential_evolution package so that I can use my own lists of initial guesses or can tell me another library with similiar algorithm that can do that?
Thanks for your help!
optimization scipy initialization differential-evolution
asked Dec 28 '18 at 3:07
Max
256
add a comment |
A project I am currently working on requires an optimization with regards to a function.
In order to make the optimization process more reliable in terms of finding the global minimum, I would like to use several initial guesses for each variable that I can specify beforehand.
In the following I randomized the lists of initial guesses (ipp_term, ipp_ci, i_risky_w) for demonstrating the lists of initial guesses.
Theses lists I would like to input as seeds (line 13) then.
upper_term = max(term)
upper_ci = max(ci)
ipp_term = np.random.uniform(0,upper_term,30)
ipp_ci = np.random.uniform(0,upper_ci,30)
i_risky_w = np.random.uniform(0,1,30)
bds = [(0,1), (0,upper_term), (0,upper_ci)]
scipy.optimize.differential_evolution(
optInsInv_opt, bounds = bds, strategy='best1bin', maxiter=None,
popsize=1, tol=0.00001, mutation=(0.5, 1), recombination=0.7,
**seed=[i_risky_w, ipp_term, ipp_ci, i_risky_w],**
callback=None, disp=True, polish=True, init='latinhypercube')
From the documentation I understand that there are two methods for initializing the populated parameters, but from my understanding they are also randomly generated!?
Does anyone know how to alter the scipy.optimize.differential_evolution package so that I can use my own lists of initial guesses or can tell me another library with similiar algorithm that can do that?
Thanks for your help!
optimization scipy initialization differential-evolution
asked Dec 28 '18 at 3:07
Max
256
A project I am currently working on requires an optimization with regards to a function.
In order to make the optimization process more reliable in terms of finding the global minimum, I would like to use several initial guesses for each variable that I can specify beforehand.
In the following I randomized the lists of initial guesses (ipp_term, ipp_ci, i_risky_w) for demonstrating the lists of initial guesses.
Theses lists I would like to input as seeds (line 13) then.
upper_term = max(term)
upper_ci = max(ci)
ipp_term = np.random.uniform(0,upper_term,30)
ipp_ci = np.random.uniform(0,upper_ci,30)
i_risky_w = np.random.uniform(0,1,30)
bds = [(0,1), (0,upper_term), (0,upper_ci)]
scipy.optimize.differential_evolution(
optInsInv_opt, bounds = bds, strategy='best1bin', maxiter=None,
popsize=1, tol=0.00001, mutation=(0.5, 1), recombination=0.7,
**seed=[i_risky_w, ipp_term, ipp_ci, i_risky_w],**
callback=None, disp=True, polish=True, init='latinhypercube')
From the documentation I understand that there are two methods for initializing the populated parameters, but from my understanding they are also randomly generated!?
Does anyone know how to alter the scipy.optimize.differential_evolution package so that I can use my own lists of initial guesses or can tell me another library with similiar algorithm that can do that?
Thanks for your help!
optimization scipy initialization differential-evolution
optimization scipy initialization differential-evolution
asked Dec 28 '18 at 3:07
Max
256
asked Dec 28 '18 at 3:07
Max
256
asked Dec 28 '18 at 3:07
Max
256
asked Dec 28 '18 at 3:07
Max
256
asked Dec 28 '18 at 3:07
Max
256
256
add a comment |
add a comment |
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