The problem at hand is optimization of multivariate function with nonlinear constraints.
There is a differential equation (in its oversimplified form)
dy/dx = y(x)*t(x) + g(x)
I need to minimize the solution of the DE y(x), but by varying the t(x).
Since it is physics under the hood, there are constraints on t(x). I successfully implemented all of them except one:
0 < t(x) < 1 for any x in range [a,b]
For certainty, the t(x) is a general polynomial:
t(x) = a0 + a1*x + a2*x**2 + a3*x**3 + a4*x**4 + a5*x**5
The x is fixed numpy.ndarray of floats and the optimization goes for coefficients a. I use scipy.optimize with trust-constr.
What I have tried so far:
Root finding at each step and determining the minimal/maximal value of the function using optimize.root and checking for sign changes. Return 0.5 if constraints are satisfied and numpy.inf or -1 or whatever not in [0;1] range if constraints are not satisfied. The optimizer stops soon and the function is not minimized properly.
Since x is fixed-length and known, I tried to define a constraint for each point, so I got N constraints where N = len(x). This works (at least look like) but takes forever for not-so large N. Also, since x is discrete and non-uniform, I can't be sure that there are no violated constraints for any x in [a,b].
EDIT #1: the minimal reproducible example
import scipy.optimize as optimize
from scipy.optimize import Bounds
import numpy as np
# some function y(x)
x = np.linspace(-np.pi,np.pi,100)
y = np.sin(x)
# polynomial t(z)
def t(a,z):
v = 0.0;
for ii in range(len(a)):
v += a[ii]*z**ii
return v
# let's minimize the sum
def targetFn(a):
return np.sum(y*t(a,x))
# polynomial order
polyord = 3
# simple bounds to have reliable results,
# otherwise the solution will grow toward +-infinity
bnd = 10.0
bounds = Bounds([-bnd for i in range(polyord+1)],
[bnd for i in range(polyord+1)])
res = optimize.minimize(targetFn, [1.0 for i in range(polyord+1)],
bounds = bounds)
if np.max(t(res.x,x))>200:
print('max constraint violated!')
if np.min(t(res.x,x))<-100:
print('min constraint violated!')
In the reproducible example given above, let the constraints to be that the value of the polynomial t(a,x) is in range [-100;200] for the given x.
So the question is: how does one properly define a constraint to tell the optimizer that the function's values must be constrained for the given range of arguments?
Related
I'm currently doing my master's project in accelerating circuit simulators and I am learning some basics for circuit simulations. My "circuit simulator" code in Python can currently run OP analysis for linear resistive circuits which uses the MNA matrix and Ax=b matrix solvers which solve for the x.
I am trying to add Newton-Raphson to the mix for non-linear resistive circuits OP analysis. This is where I am a bit stuck. It seems to use the Jacobian matrix (the conductance matrix) and normal nodal analysis equations for the Ax=b, x to be solved. However, I can't find a way of making the code generic and not hard-coding the nodal analysis equations. The only way that I could think of is by expanding the Jacobian matrix (which needs integration for each element inside the matrix). I have tried my method of expanding the Jacobian matrix while multiplying with the variables needed to be solved but it's not accurate as it is not the direct integration of the elements. Is there another way of doing this or I should just try integrating every element in the Jacobian matrix with respect to the variables of the x array?
The code below is only for the Newton-Raphson method test for the non-linear circuit, not added with the generic code yet
import numpy as np
import time as tm
import matplotlib.pyplot as plt
def F(x):
return np.array(
[(x[0]-x[1])/1000 + x[2],
(x[1]-x[0])/1000 + 0.001*(x[1])**3,
x[0] - 1])
def J(x):
return np.array(
[[1/1000, -1/1000, 1],
[-1/1000, (1+(x[1])**2)/1000, 0], # Need to add 3 as coefficient of x[1] to simulate the hard-coded function
[1, 0, 0]])
def Newton_system(x_guess, eps):
"""
Solve nonlinear system F=0 by Newton's method.
J is the Jacobian of F. Both F and J must be functions of x.
At input, x holds the start value. The iteration continues
until ||F|| < eps.
"""
RHS = np.array([0,0,1])
x_old = x_guess
F_x = np.matmul(J(x_old),x_old)-RHS # "Generic" expanded Jacobian input
error = 9e9
#F_value = F(x_old) # Hard-coded input
F_value = F_x # "Generic" expanded Jacobian input
iteration_counter = 0
while error > eps and iteration_counter < 5000:
delta = np.linalg.solve(J(x_old), F_value)
x_new = x_old - delta
#F_value = F(x_new) # Hard-coded input
F_value = np.matmul(J(x_new),x_new)-RHS # "Generic" expanded Jacobian input
error = np.max(np.abs(x_new - x_old))
x_old = x_new
iteration_counter += 1
print(x_new,iteration_counter)
# Here, either a solution is found, or too many iterations
if error > eps:
iteration_counter = -1
return x_new, iteration_counter
expected = np.array([1, 0.7, -0.003])
st = tm.time()
ans, n = Newton_system(x_guess = np.array([1, 0.6823, -0.003]), eps=1e-9)
et = tm.time()
diff = et - st
print("The amount of time taken is", diff)
Please help :(
I need to find the k in the range [1, 10], which is the least positive integer such that binomial(k, 2)≥ m, where m≥3 - integer. The binomial() function is the binominal coefficient.
My attempt is:
After some algebraic steps, I have found the minization task: min k(k-1) - 2m ≥ 0, s.t. m≥3. I have defined the objective function and gradient. In the objective function I fixed the m=3 and my problem is how to define integer domain for the variable m.
from scipy.optimize import line_search
# objective function
def objective(k):
m = 3
return k*(k-1)-2*m
# gradient for the objective function
def gradient(k):
return 2.0 * k - 1
# define range
r_min, r_max = 1, 11
# prepare inputs
inputs = arange(r_min, r_max, 1)
# compute targets
targets = [objective(k) for k in inputs]
# define the starting point
point = 1.0
# define the direction to move
direction = 1.0
# print the initial conditions
print('start=%.1f, direction=%.1f' % (point, direction))
# perform the line search
result = line_search(objective, gradient, point, direction)
print(result)
I have see the
LineSearchWarning: The line search algorithm did not converge
Question. How to define the objective function in Python?
You are look to minimise k such that k(k-1)-2m≥0, with additional constraints on k on which we'll come back later. You can explicitly solve this inequation, by solving the corresponding equation first, that is, finding the roots of P:=X²-X-2m. The quadratic formulas give the roots (1±√(1+4m²))/2. Since P(x)→∞ as x→±∞, you know that the x that satisfy your inequation are the ones above the greatest root, and below the lowest root. Since you are only interested in positive solutions, and since 1-√(1+m²)<0, the set of wanted solutions is [(1+√(1+m²))/2,∞). Among these solutions, the smallest integer is the ceil of (1+√(1+m²))/2 which is strictly greater than 1. Let k=ceil((1+sqrt(1+m**2))/2) be that integer. If k≤10, then your problem has a solution, which is k. Otherwise, your problem has no solutions. In Python, you get the following:
import math
def solve(m):
k = math.ceil((1+math.sqrt(1+m**2))/2)
return k if k <= 10 else None
I'm facing a problem while trying to implement the coupled differential equation below (also known as single-mode coupling equation) in Python 3.8.3. As for the solver, I am using Scipy's function scipy.integrate.solve_bvp, whose documentation can be read here. I want to solve the equations in the complex domain, for different values of the propagation axis (z) and different values of beta (beta_analysis).
The problem is that it is extremely slow (not manageable) compared with an equivalent implementation in Matlab using the functions bvp4c, bvpinit and bvpset. Evaluating the first few iterations of both executions, they return the same result, except for the resulting mesh which is a lot greater in the case of Scipy. The mesh sometimes even saturates to the maximum value.
The equation to be solved is shown here below, along with the boundary conditions function.
import h5py
import numpy as np
from scipy import integrate
def coupling_equation(z_mesh, a):
ka_z = k # Global
z_a = z # Global
a_p = np.empty_like(a).astype(complex)
for idx, z_i in enumerate(z_mesh):
beta_zf_i = np.interp(z_i, z_a, beta_zf) # Get beta at the desired point of the mesh
ka_z_i = np.interp(z_i, z_a, ka_z) # Get ka at the desired point of the mesh
coupling_matrix = np.empty((2, 2), complex)
coupling_matrix[0] = [-1j * beta_zf_i, ka_z_i]
coupling_matrix[1] = [ka_z_i, 1j * beta_zf_i]
a_p[:, idx] = np.matmul(coupling_matrix, a[:, idx]) # Solve the coupling matrix
return a_p
def boundary_conditions(a_a, a_b):
return np.hstack(((a_a[0]-1), a_b[1]))
Moreover, I couldn't find a way to pass k, z and beta_zf as arguments of the function coupling_equation, given that the fun argument of the solve_bpv function must be a callable with the parameters (x, y). My approach is to define some global variables, but I would appreciate any help on this too if there is a better solution.
The analysis function which I am trying to code is:
def analysis(k, z, beta_analysis, max_mesh):
s11_analysis = np.empty_like(beta_analysis, dtype=complex)
s21_analysis = np.empty_like(beta_analysis, dtype=complex)
initial_mesh = np.linspace(z[0], z[-1], 10) # Initial mesh of 10 samples along L
mesh = initial_mesh
# a_init must be complex in order to solve the problem in a complex domain
a_init = np.vstack((np.ones(np.size(initial_mesh)).astype(complex),
np.zeros(np.size(initial_mesh)).astype(complex)))
for idx, beta in enumerate(beta_analysis):
print(f"Iteration {idx}: beta_analysis = {beta}")
global beta_zf
beta_zf = beta * np.ones(len(z)) # Global variable so as to use it in coupling_equation(x, y)
a = integrate.solve_bvp(fun=coupling_equation,
bc=boundary_conditions,
x=mesh,
y=a_init,
max_nodes=max_mesh,
verbose=1)
# mesh = a.x # Mesh for the next iteration
# a_init = a.y # Initial guess for the next iteration, corresponding to the current solution
s11_analysis[idx] = a.y[1][0]
s21_analysis[idx] = a.y[0][-1]
return s11_analysis, s21_analysis
I suspect that the problem has something to do with the initial guess that is being passed to the different iterations (see commented lines inside the loop in the analysis function). I try to set the solution of an iteration as the initial guess for the following (which must reduce the time needed for the solver), but it is even slower, which I don't understand. Maybe I missed something, because it is my first time trying to solve differential equations.
The parameters used for the execution are the following:
f2 = h5py.File(r'path/to/file', 'r')
k = np.array(f2['k']).squeeze()
z = np.array(f2['z']).squeeze()
f2.close()
analysis_points = 501
max_mesh = 1e6
beta_0 = 3e2;
beta_low = 0; # Lower value of the frequency for the analysis
beta_up = beta_0; # Upper value of the frequency for the analysis
beta_analysis = np.linspace(beta_low, beta_up, analysis_points);
s11_analysis, s21_analysis = analysis(k, z, beta_analysis, max_mesh)
Any ideas on how to improve the performance of these functions? Thank you all in advance, and sorry if the question is not well-formulated, I accept any suggestions about this.
Edit: Added some information about performance and sizing of the problem.
In practice, I can't find a relation that determines de number of times coupling_equation is called. It must be a matter of the internal operation of the solver. I checked the number of callings in one iteration by printing a line, and it happened in 133 ocasions (this was one of the fastests). This must be multiplied by the number of iterations of beta. For the analyzed one, the solver returned this:
Solved in 11 iterations, number of nodes 529.
Maximum relative residual: 9.99e-04
Maximum boundary residual: 0.00e+00
The shapes of a and z_mesh are correlated, since z_mesh is a vector whose length corresponds with the size of the mesh, recalculated by the solver each time it calls coupling_equation. Given that a contains the amplitudes of the progressive and regressive waves at each point of z_mesh, the shape of a is (2, len(z_mesh)).
In terms of computation times, I only managed to achieve 19 iterations in about 2 hours with Python. In this case, the initial iterations were faster, but they start to take more time as their mesh grows, until the point that the mesh saturates to the maximum allowed value. I think this is because of the value of the input coupling coefficients in that point, because it also happens when no loop in beta_analysisis executed (just the solve_bvp function for the intermediate value of beta). Instead, Matlab managed to return a solution for the entire problem in just 6 minutes, aproximately. If I pass the result of the last iteration as initial_guess (commented lines in the analysis function, the mesh overflows even faster and it is impossible to get more than a couple iterations.
Based on semi-random inputs, we can see that max_mesh is sometimes reached. This means that coupling_equation can be called with a quite big z_mesh and a arrays. The problem is that coupling_equation contains a slow pure-Python loop iterating on each column of the arrays. You can speed the computation up a lot using Numpy vectorization. Here is an implementation:
def coupling_equation_fast(z_mesh, a):
ka_z = k # Global
z_a = z # Global
a_p = np.empty(a.shape, dtype=np.complex128)
beta_zf_i = np.interp(z_mesh, z_a, beta_zf) # Get beta at the desired point of the mesh
ka_z_i = np.interp(z_mesh, z_a, ka_z) # Get ka at the desired point of the mesh
# Fast manual matrix multiplication
a_p[0] = (-1j * beta_zf_i) * a[0] + ka_z_i * a[1]
a_p[1] = ka_z_i * a[0] + (1j * beta_zf_i) * a[1]
return a_p
This code provides a similar output with semi-random inputs compared to the original implementation but is roughly 20 times faster on my machine.
Furthermore, I do not know if max_mesh happens to be big with your inputs too and even if this is normal/intended. It may make sense to decrease the value of max_mesh in order to reduce the execution time even more.
I am evaluating a set of ODEs with time varying coefficients
def deriv(y, t, N, coefficients):
S, I, R = y
dSdt = coefficients['beta'](t) * S * I / N * -1
dIdt = coefficients['beta'](t) * S * I / N - coefficients['gamma']* I
dRdt = coefficients['gamma'] * I
return dSdt, dIdt, dRdt
In particular, I have 'beta' values in a pre-calculated array, of size equal to int(max(t)).
coefficients = {'beta' : beta_f,'gamma':0.1}
def beta_f(t):
return mybetas.iloc[int(t)]
# Initial conditions vector
y0 = (S0, I0, R0)
# Integrate the SIR equations over the time grid, t.
ret = odeint(deriv,y0,t,args=(N,coefficients))
When I run odeint, it is evaluate also for value beyond max(t), raising an index out of bound error in beta_f.
How to limit the evaluation span for odeint?
Since len(mybetas) == int(max(t)), you can get an out-of-bounds error even for values of t which are not beyond max(t).
For example, mybetas.iloc[int(max(t))] will give you the out-of-bounds error, even though int(max(t)) <= max(t) for positive values of t.
But to your point, odeint does indeed check some values outside of the domain of integration. I had to deal with a problem similar to yours just a few weeks ago, and the following two discussions on stackoverflow were really helpful:
integrate.ode sets t0 values outside of my data range
Solve ODEs with discontinuous input/forcing data
The second link explains why it might be computationally faster to solve the ODE with odeint over each individual integer time step one after the other in a for loop, instead of letting odeint deal with the discontinuities in your derivative caused by jumps in the values of your betas.
Otherwise, if this is appropriate for your study case, you can interpolate your betas, and let the function beta_f return interpolated values of beta. Of course, you will have to extend the interpolation domain slightly beyond your integration domain, since odeint might want to evaluate the derivative for some t larger than max(t).
I am using Python 2.7. I am wondering why the optimize function of SciPy doesn't converge to the right function when the target is a high frequency sinus wave.
import numpy as np
from scipy import optimize
test_func = lambda x: 5*np.sin(15*x+3)+1
t = linspace(0,25,100000)
y_t = test_func(t)
plot(t,y_t)
fitfunc = lambda p, x: p[0]*np.sin(p[1]*x+p[2])+p[3]
errfunc = lambda p, x, y: fitfunc(p, x) - y
p0 = [max(y_t),10,2,0]
p1, success = optimize.leastsq(errfunc, p0, args=(t,y_t))
plot(t,fitfunc(p1,t))
One can clearly see that the end solution diverges from the target clearly. Am I doing something wrong ? Is the error function ill adapted here ?
Thanks for any input
Your problem is that there are a large number of local minima in your residuals function as the phase and frequency shift with respect to their true values; without really good initial guesses for the phase and frequency you will converge into one instead of falling into the much deeper, global minimum:
If you don't have any more information about the phase and frequency, you can either estimate them from a FFT of the data or rewrite your formula as
Asin(bx + phi) + d = Acos(phi)sin(bx) + Asin(phi)cos(bx) + d
which has only one nonlinear parameter (b): you can use a grid-search for b and much faster and more reliable linear least-squares fitting for the rest (a1 = Acos(phi), a2 = Asin(phi) and d).
Here's a plot of the rms residual as the frequency, b varies, showing the various minima: