Fast Polynomial Multiplication using radix2 fast Fourier Transform. Fast Polynomial Multiplication using radix2 fast Fourier Transform. Reference: https:en.wikipedia.orgwikiCooleyE28093TukeyFFTalgorithmTheradix2DITcase For polynomials of degree m and n the algorithms has complexity Onlogn  mlogm The main part of the algorithm is split in two parts: 1 DFT: We compute the discrete fourier transform DFT of A and B using a bottomup dynamic approach  2 multiply: Once we obtain the DFT of AB, we can similarly invert it to obtain AB The class FFT takes two polynomials A and B with complex coefficients as arguments; The two polynomials should be represented as a sequence of coefficients starting from the free term. Thus, for instance x  2x3 could be represented as 0,1,0,2 or 0,1,0,2. The constructor adds some zeros at the end so that the polynomials have the same length which is a power of 2 at least the length of their product. Example: Create two polynomials as sequences  A  0, 1, 0, 2   x2x3  B  2, 3, 4, 0   23x4x2 Create an FFT object with them  x  FFTA, B Print product  x.product   2x  3x2  8x3  4x4  6x5 00j, 20j, 30j, 80j, 60j, 80j str test  printx A  0x0  1x1  2x0  3x2 B  0x2  1x3  2x4 AB  0x00j  1x20j  2x30j  3x80j  4x60j  5x80j Input as list Remove leading zero coefficients Add 0 to make lengths equal a power of 2 A complex root used for the fourier transform The product Discrete fourier transform of A and B Corner case First half of next step Second half of next step Update multiply the DFTs of  A and B and find AB Corner Case Inverse DFT First half of next step Even positions Odd positions Update Unpack Remove leading 0's Overwrite str for print; Shows A, B and AB Unit tests
import mpmath  # for roots of unity
import numpy as np


class FFT:
    """
    Fast Polynomial Multiplication using radix-2 fast Fourier Transform.

    Reference:
    https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case

    For polynomials of degree m and n the algorithms has complexity
    O(n*logn + m*logm)

    The main part of the algorithm is split in two parts:
        1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a
        bottom-up dynamic approach -
        2) __multiply: Once we obtain the DFT of A*B, we can similarly
        invert it to obtain A*B

    The class FFT takes two polynomials A and B with complex coefficients as arguments;
    The two polynomials should be represented as a sequence of coefficients starting
    from the free term. Thus, for instance x + 2*x^3 could be represented as
    [0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the
    polynomials have the same length which is a power of 2 at least the length of
    their product.

    Example:

    Create two polynomials as sequences
    >>> A = [0, 1, 0, 2]  # x+2x^3
    >>> B = (2, 3, 4, 0)  # 2+3x+4x^2

    Create an FFT object with them
    >>> x = FFT(A, B)

    Print product
    >>> x.product  # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5
    [(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)]

    __str__ test
    >>> print(x)
    A = 0*x^0 + 1*x^1 + 2*x^0 + 3*x^2
    B = 0*x^2 + 1*x^3 + 2*x^4
    A*B = 0*x^(-0+0j) + 1*x^(2+0j) + 2*x^(3+0j) + 3*x^(8+0j) + 4*x^(6+0j) + 5*x^(8+0j)
    """

    def __init__(self, poly_a=None, poly_b=None):
        # Input as list
        self.polyA = list(poly_a or [0])[:]
        self.polyB = list(poly_b or [0])[:]

        # Remove leading zero coefficients
        while self.polyA[-1] == 0:
            self.polyA.pop()
        self.len_A = len(self.polyA)

        while self.polyB[-1] == 0:
            self.polyB.pop()
        self.len_B = len(self.polyB)

        # Add 0 to make lengths equal a power of 2
        self.c_max_length = int(
            2 ** np.ceil(np.log2(len(self.polyA) + len(self.polyB) - 1))
        )

        while len(self.polyA) < self.c_max_length:
            self.polyA.append(0)
        while len(self.polyB) < self.c_max_length:
            self.polyB.append(0)
        # A complex root used for the fourier transform
        self.root = complex(mpmath.root(x=1, n=self.c_max_length, k=1))

        # The product
        self.product = self.__multiply()

    # Discrete fourier transform of A and B
    def __dft(self, which):
        dft = [[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
        # Corner case
        if len(dft) <= 1:
            return dft[0]
        #
        next_ncol = self.c_max_length // 2
        while next_ncol > 0:
            new_dft = [[] for i in range(next_ncol)]
            root = self.root**next_ncol

            # First half of next step
            current_root = 1
            for j in range(self.c_max_length // (next_ncol * 2)):
                for i in range(next_ncol):
                    new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                current_root *= root
            # Second half of next step
            current_root = 1
            for j in range(self.c_max_length // (next_ncol * 2)):
                for i in range(next_ncol):
                    new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                current_root *= root
            # Update
            dft = new_dft
            next_ncol = next_ncol // 2
        return dft[0]

    # multiply the DFTs of  A and B and find A*B
    def __multiply(self):
        dft_a = self.__dft("A")
        dft_b = self.__dft("B")
        inverce_c = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
        del dft_a
        del dft_b

        # Corner Case
        if len(inverce_c[0]) <= 1:
            return inverce_c[0]
        # Inverse DFT
        next_ncol = 2
        while next_ncol <= self.c_max_length:
            new_inverse_c = [[] for i in range(next_ncol)]
            root = self.root ** (next_ncol // 2)
            current_root = 1
            # First half of next step
            for j in range(self.c_max_length // next_ncol):
                for i in range(next_ncol // 2):
                    # Even positions
                    new_inverse_c[i].append(
                        (
                            inverce_c[i][j]
                            + inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / 2
                    )
                    # Odd positions
                    new_inverse_c[i + next_ncol // 2].append(
                        (
                            inverce_c[i][j]
                            - inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / (2 * current_root)
                    )
                current_root *= root
            # Update
            inverce_c = new_inverse_c
            next_ncol *= 2
        # Unpack
        inverce_c = [round(x[0].real, 8) + round(x[0].imag, 8) * 1j for x in inverce_c]

        # Remove leading 0's
        while inverce_c[-1] == 0:
            inverce_c.pop()
        return inverce_c

    # Overwrite __str__ for print(); Shows A, B and A*B
    def __str__(self):
        a = "A = " + " + ".join(
            f"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A])
        )
        b = "B = " + " + ".join(
            f"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B])
        )
        c = "A*B = " + " + ".join(
            f"{coef}*x^{i}" for coef, i in enumerate(self.product)
        )

        return f"{a}\n{b}\n{c}"


# Unit tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()
