CompEcon Toolbox:
DemApp05
Chebychev polynomial and spline approximantion of various functions
Randall Romero Aguilar, PhD

This demo is based on the original Matlab demo accompanying the Computational Economics and Finance 2001 textbook by Mario Miranda and Paul Fackler.

Last updated: 2020-Sep-08

## About¶

Demonstrates Chebychev polynomial, cubic spline, and linear spline approximation for the following functions \begin{align} y &= 1 + x + 2x^2 - 3x^3 \\ y &= \exp(-x) \\ y &= \frac{1}{1+25x^2} \\ y &= \sqrt{|x|} \end{align}

## Initial tasks¶

In [ ]:
if 'google.colab' in str(get_ipython()):
print("This notebook is running on Google Colab. Installing the compecon package.")
!pip install compecon

In [ ]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from compecon import BasisChebyshev, BasisSpline, nodeunif


### Functions to be approximated¶

In [ ]:
funcs = [lambda x: 1 + x + 2 * x ** 2 - 3 * x ** 3,
lambda x: np.exp(-x),
lambda x: 1 / ( 1 + 25 * x ** 2),
lambda x: np.sqrt(np.abs(x))]

fst = ['$y = 1 + x + 2x^2 - 3x^3$', '$y = \exp(-x)$',
'$y = 1/(1+25x^2)$', '$y = \sqrt{|x|}$']


Set degree of approximation and endpoints of approximation interval

In [ ]:
n = 7   # degree of approximation
a = -1  # left endpoint
b = 1   # right endpoint


Construct uniform grid for error ploting

In [ ]:
x = np.linspace(a, b, 2001)

In [ ]:
def subfig(f,  title):

# Construct interpolants
C = BasisChebyshev(n, a, b, f=f)
S = BasisSpline(n, a, b, f=f)
L = BasisSpline(n, a, b, k=1, f=f)

data = pd.DataFrame({
'actual': f(x),
'Chebyshev': C(x),
'Cubic Spline': S(x),
'Linear Spline': L(x)},
index = x)

fig1, axs = plt.subplots(2,2, figsize=[12,6], sharex=True, sharey=True)
fig1.suptitle(title)
data.plot(ax=axs, subplots=True)

errors = data[['Chebyshev', 'Cubic Spline']].subtract(data['actual'], axis=0)

fig2, ax = plt.subplots(figsize=[12,3])
fig2.suptitle("Approximation Error")
errors.plot(ax=ax)



## Polynomial¶

$y = 1 + x + 2x^2 - 3x^3$

In [ ]:
subfig(lambda x: 1 + x + 2*x**2 - 3*x**3, '$y = 1 + x + 2x^2 - 3x^3$')


## Exponential¶

$y = \exp(-x)$

In [ ]:
subfig(lambda x: np.exp(-x),'$y = \exp(-x)$')


## Rational¶

$y = 1/(1+25x^2)$

In [ ]:
subfig(lambda x: 1 / ( 1 + 25 * x ** 2),'$y = 1/(1+25x^2)$')


## Kinky¶

$y = \sqrt{|x|}$

In [ ]:
subfig(lambda x: np.sqrt(np.abs(x)), '$y = \sqrt{|x|}$')