DKenefake / OptimalPoly

Generates MinMax polynomial approximations of functions. Stable implementation of the Remez Algorithm using multi-precision arithmetic. Also does C/C++ code generation!

OptimalPoly

Generates MinMax polynomial approximations of functions. Stable implementation of the Remez Algorithm using multi-precision arithmetic. Also does C/C++ code generation!

Example of use

This is all that is needed to create a 4th order optimal polynomial approximation of $e^x$ on [0,1].

function = lambda x: mp.exp(x)
poly_coeffs, max_error = remez(f, 4, 0, 1)

We can inspect how well we approximated it visually with the following code snippet.

import matplotlib.pyplot as plt
x = numpy.linspace(0, 1, 50)

y_approx = numpy.polyval(poly_coeffs[::-1], x)
y_exact = numpy.array([function(x_i) for x_i in x])
plt.plot(x, y_exact)
plt.plot(x, y_approx, 'x')
plt.title(r'$f(x)$ v. $P^*_{4}(x)$')

And we can also look at the distinctive equioscillation of the optimal polynomial result with the following.

import matplotlib.pyplot as plt
x = numpy.linspace(0, 1, 500)

y_approx = numpy.polyval(poly_coeffs[::-1], x)
y_exact = numpy.array([function(x_i) for x_i in x])
plt.plot(x, y_exact - y_approx)
plt.title('$f(x) - P^*_{4}(x)$')

To generate the C code, you need to specify the data type of either float or double. This uses horner's method to speed up the polynomial evaluation, and depending on the compiler flags used, this should compile to 4 fmad operations.

c_code_gen(data_type = 'float', name = 'exp_approx', poly_coeffs = poly_coeffs, comments = f'x in [0, 1]')

float exp_approx (float x){
	// x in [0, 1] 

	const float a_0 = 1.0000271624188655f;
	const float a_1 = 0.9986854006378604f;
	const float a_2 = 0.510139460205787f;
	const float a_3 = 0.13969814854689722f;
	const float a_4 = 0.0697044942307694f;
 	return a_0+x*(a_1 +x*(a_2 +x*(a_3 +x*a_4)));
}

Dependencies

• numpy
• mpmath