Hi nlopt commonity,
I'm new at python and currently i want to solve a non linear optimization
problem.
I've done it using fmincon in MATLAB yet my other codes are written in
python and thus I also want to solve the problem using python to improve
connectivity between my codes.
I try to solve the optimization using SciPy module, but with no fruition.
And in that circumstance, I bump into nlopt. I largerly understand how the
nlopt works but running my code, i still bump with "nlopt invalid
argument".
I don't know where my code goes wrong, and decides to ask nlopt commonity I
hope you can help me to solve this problem. My python code that include
optimization problem is attached.
Thank you
--
=====================================================
Taufiq Suryo Nugroho, PhD
Research Assistant
Institut Teknologi Bandung
=====================================================
email : taufiq.su...@gmail.com <tausn_su...@yahoo.com>
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 8 18:37:47 2020
@author: Taufiq Nugroho
"""
import numpy as np
import nlopt
#import data
p_a_ij = np.load('p_a_ij.npy')
num_od = 4
num_path = 4
num_link = 10
ob_link_flow = np.array([84,70,95,61])
ob_index = np.arange (7,8)
tij_est = np.ones (num_od)
#create function to calculate objective function and
def Est_link_flow (tij_est,p_a_ij,num_path,num_od,num_link):
temp_tij_r = np.zeros ([num_od * num_path, num_link])
for nl in range (0,num_link):
pc = 0 #pc = path counter
for od in range (0,num_od):
for p in range (0,num_path):
temp_tij_r [pc+p,nl] = tij_est [od] * p_a_ij [pc+p,nl]
pc = pc + num_path
est_flow = np.sum (temp_tij_r,1)
return est_flow
def Objective_f (tij_est,grad):
if grad.size > 0:
[]
S_Tij = np.sum ([tij_est * np.log(tij_est) - tij_est],1)
S_Tij = float (S_Tij)
return S_Tij
def Constraints (tij_est,grad,ob_link_flow,est_flow_hand,ob_index):
if grad.size > 0:
[]
est_flow = est_flow_hand (tij_est)
est_flow_indexed = est_flow[ob_index]
ceq = ob_link_flow - est_flow_indexed
return ceq
est_link_flow_hand = lambda tij_est: Est_link_flow(tij_est,
p_a_ij,
num_path,
num_od,
num_link)
est_link_flow = est_link_flow_hand (tij_est)
#optimization process
tij_0 = np.array([1,1,1,1])
opt = nlopt.opt(nlopt.LN_COBYLA,4)
opt.set_min_objective(Objective_f)
opt.set_lower_bounds(np.zeros(len(tij_est)))
opt.add_equality_constraint(lambda tij_est,grad:
Constraints(tij_est,grad,ob_link_flow,est_link_flow_hand,ob_index))
opt.set_xtol_rel(1e-4)
xopt = opt.optimize(tij_est)
minf = opt.last_optimum_value()
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