209 lines
7.0 KiB
Python
209 lines
7.0 KiB
Python
#
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# Copyright (c) 2008-2016 Stefan Krah. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
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# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
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# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
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# SUCH DAMAGE.
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#
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######################################################################
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# This file lists and checks some of the constants and limits used #
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# in libmpdec's Number Theoretic Transform. At the end of the file #
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# there is an example function for the plain DFT transform. #
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######################################################################
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#
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# Number theoretic transforms are done in subfields of F(p). P[i]
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# are the primes, D[i] = P[i] - 1 are highly composite and w[i]
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# are the respective primitive roots of F(p).
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#
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# The strategy is to convolute two coefficients modulo all three
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# primes, then use the Chinese Remainder Theorem on the three
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# result arrays to recover the result in the usual base RADIX
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# form.
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#
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# ======================================================================
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# Primitive roots
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# ======================================================================
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#
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# Verify primitive roots:
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#
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# For a prime field, r is a primitive root if and only if for all prime
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# factors f of p-1, r**((p-1)/f) =/= 1 (mod p).
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#
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def prod(F, E):
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"""Check that the factorization of P-1 is correct. F is the list of
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factors of P-1, E lists the number of occurrences of each factor."""
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x = 1
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for y, z in zip(F, E):
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x *= y**z
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return x
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def is_primitive_root(r, p, factors, exponents):
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"""Check if r is a primitive root of F(p)."""
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if p != prod(factors, exponents) + 1:
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return False
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for f in factors:
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q, control = divmod(p-1, f)
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if control != 0:
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return False
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if pow(r, q, p) == 1:
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return False
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return True
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# =================================================================
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# Constants and limits for the 64-bit version
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# =================================================================
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RADIX = 10**19
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# Primes P1, P2 and P3:
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P = [2**64-2**32+1, 2**64-2**34+1, 2**64-2**40+1]
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# P-1, highly composite. The transform length d is variable and
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# must divide D = P-1. Since all D are divisible by 3 * 2**32,
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# transform lengths can be 2**n or 3 * 2**n (where n <= 32).
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D = [2**32 * 3 * (5 * 17 * 257 * 65537),
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2**34 * 3**2 * (7 * 11 * 31 * 151 * 331),
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2**40 * 3**2 * (5 * 7 * 13 * 17 * 241)]
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# Prime factors of P-1 and their exponents:
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F = [(2,3,5,17,257,65537), (2,3,7,11,31,151,331), (2,3,5,7,13,17,241)]
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E = [(32,1,1,1,1,1), (34,2,1,1,1,1,1), (40,2,1,1,1,1,1)]
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# Maximum transform length for 2**n. Above that only 3 * 2**31
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# or 3 * 2**32 are possible.
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MPD_MAXTRANSFORM_2N = 2**32
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# Limits in the terminology of Pollard's paper:
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m2 = (MPD_MAXTRANSFORM_2N * 3) // 2 # Maximum length of the smaller array.
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M1 = M2 = RADIX-1 # Maximum value per single word.
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L = m2 * M1 * M2
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P[0] * P[1] * P[2] > 2 * L
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# Primitive roots of F(P1), F(P2) and F(P3):
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w = [7, 10, 19]
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# The primitive roots are correct:
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for i in range(3):
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if not is_primitive_root(w[i], P[i], F[i], E[i]):
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print("FAIL")
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# =================================================================
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# Constants and limits for the 32-bit version
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# =================================================================
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RADIX = 10**9
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# Primes P1, P2 and P3:
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P = [2113929217, 2013265921, 1811939329]
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# P-1, highly composite. All D = P-1 are divisible by 3 * 2**25,
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# allowing for transform lengths up to 3 * 2**25 words.
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D = [2**25 * 3**2 * 7,
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2**27 * 3 * 5,
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2**26 * 3**3]
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# Prime factors of P-1 and their exponents:
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F = [(2,3,7), (2,3,5), (2,3)]
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E = [(25,2,1), (27,1,1), (26,3)]
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# Maximum transform length for 2**n. Above that only 3 * 2**24 or
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# 3 * 2**25 are possible.
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MPD_MAXTRANSFORM_2N = 2**25
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# Limits in the terminology of Pollard's paper:
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m2 = (MPD_MAXTRANSFORM_2N * 3) // 2 # Maximum length of the smaller array.
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M1 = M2 = RADIX-1 # Maximum value per single word.
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L = m2 * M1 * M2
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P[0] * P[1] * P[2] > 2 * L
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# Primitive roots of F(P1), F(P2) and F(P3):
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w = [5, 31, 13]
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# The primitive roots are correct:
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for i in range(3):
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if not is_primitive_root(w[i], P[i], F[i], E[i]):
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print("FAIL")
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# ======================================================================
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# Example transform using a single prime
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# ======================================================================
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def ntt(lst, dir):
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"""Perform a transform on the elements of lst. len(lst) must
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be 2**n or 3 * 2**n, where n <= 25. This is the slow DFT."""
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p = 2113929217 # prime
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d = len(lst) # transform length
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d_prime = pow(d, (p-2), p) # inverse of d
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xi = (p-1)//d
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w = 5 # primitive root of F(p)
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r = pow(w, xi, p) # primitive root of the subfield
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r_prime = pow(w, (p-1-xi), p) # inverse of r
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if dir == 1: # forward transform
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a = lst # input array
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A = [0] * d # transformed values
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for i in range(d):
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s = 0
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for j in range(d):
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s += a[j] * pow(r, i*j, p)
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A[i] = s % p
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return A
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elif dir == -1: # backward transform
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A = lst # input array
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a = [0] * d # transformed values
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for j in range(d):
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s = 0
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for i in range(d):
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s += A[i] * pow(r_prime, i*j, p)
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a[j] = (d_prime * s) % p
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return a
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def ntt_convolute(a, b):
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"""convolute arrays a and b."""
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assert(len(a) == len(b))
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x = ntt(a, 1)
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y = ntt(b, 1)
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for i in range(len(a)):
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y[i] = y[i] * x[i]
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r = ntt(y, -1)
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return r
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# Example: Two arrays representing 21 and 81 in little-endian:
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a = [1, 2, 0, 0]
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b = [1, 8, 0, 0]
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assert(ntt_convolute(a, b) == [1, 10, 16, 0])
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assert(21 * 81 == (1*10**0 + 10*10**1 + 16*10**2 + 0*10**3))
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