#!/usr/bin/python3
# -*- coding: utf-8 -*-
# Dependencies:
# (pip install bintrees)
# (pip install Pillow), if --animate is used
import sys
from dataclasses import dataclass
from typing import Optional
from collections import deque
from math import log
# From the Halo paper:
# Let A = [0, 2^{λ/2 + 1} + 2^{λ/2} - 1]. It is straightforward to verify that a, b ∈ A
# at the end of Algorithm 3 for any input \mathbf{r}.
# {In fact a, b ∈ [2^{λ/2} + 1, 2^{λ/2 + 1} + 2^{λ/2} - 1], but it is convenient to
# define A to start at 0.}
#
# Next we need to show that the mapping (a ⦂ A, b ⦂ A) ↦ (a ζ_q + b) mod q is injective.
# This will depend on the specific values of ζ_q and q, and can be cast as a sumset problem.
#
# We use the notation v·A + A for { (av + b) mod q : a, b ∈ A }, and A - A for -1·A + A.
# {We take sumsets using this notation to implicitly be subsets of F_q.}
# The question is then whether |ζ_q·A + A| = |A|^2.
#
# For intuition, note that if av + b = a'v + b' (mod q), with a ≠ a', we would have
# v = (b' - b)/(a - a') (mod q). Thus the number of v ∈ F_q for which |v·A + A| < |A|^2
# is at most (|A - A| - 1)^2. {We thank Robert Israel for this observation. [HI2019]}
# Since in our case (|A - A| - 1)^2 ≈ 9·2^130 is small compared to q ≈ 2^254, we would
# heuristically expect that |ζ_q·A + A| = |A|^2 unless there is some reason why ζ_q does
# not "behave like a random element of F_q".
#
# Of course ζ_q is *not* a random element of F_q, and so the above argument can only be
# used for intuition. Even when (|A - A| - 1)^2 is small compared to q, there are clearly
# values of ζ_q and q for which it would not hold. To prove that it holds in the needed
# cases for the Tweedledum and Tweedledee curves used in our implementation, we take a
# different tack.
#
# Define a distance metric δ_q on F_q so that δ_q(x, y) is the minimum distance between
# x and y around the ring of integers modulo q in either direction, i.e.
#
# δ_q(x, y) = min(z, q - z) where z = (x - y) mod q
#
# Now let D_{q,ζ_q}(m) be the minimum δ_q-distance between any two elements of ζ_q·[0, m],
# i.e.
#
# D_{q,ζ_q}(m) = min{ δ_q(a ζ_q, a' ζ_q ) : a, a' ∈ [0, m] }
#
# An algorithm to compute D_{q,ζ_q}(m) is implemented by checksumsets.py in [Hopw2019]
# [i.e. this file]; it works by iteratively finding each m at which D_{q,ζ_q}(m)
# decreases. [...]
#
# Now if D_{q,ζ_q}(2^{λ/2 + 1} + 2^{λ/2} - 1) ≥ 2^{λ/2 + 1} + 2^{λ/2}, then copies of
# A will "fit within the gaps" in ζ_q·A. That is, ζ_q·A + A will have |A|^2 elements,
# because all of the sets { ζ_q·{a} + A : a ∈ A } will be disjoint.
#
# The algorithm is based on the observation that the problem of deciding when
# D_{q,ζ_q}(m) next decreases is self-similar to deciding when it first decreases.
# It computes the exact min-distance at each decrease (not just a lower bound),
# which facilitates detecting any bugs in the algorithm. Also, we check correctness
# of the partial results up to a given bound on m, against a naive algorithm.
BRUTEFORCE_THRESHOLD = 100000
DEBUG = False
@dataclass
class State:
u: int
m: int
n: int
d: int
def D(q, zeta, mm, animator=None):
if DEBUG: print("(q, zeta, mm) =", (q, zeta, mm))
Dcheck = [] if BRUTEFORCE_THRESHOLD == 0 else bruteforce_D(q, zeta, min(mm, BRUTEFORCE_THRESHOLD))
# (u + am) : a ∈ Nat is the current arithmetic progression
# n is the previous min-distance
# d is the current min-distance
cur = State(u=0, m=1, n=q, d=zeta)
old = None
while True:
# Consider values of x where D_{q,ζ_q}(x) decreases, i.e. where
# D_{q,ζ_q}(x) < D_{q,ζ_q})(x-1).
#
# We keep track of an arithmetic progression (u + am) such that the next
# value at which D_{q,ζ_q}(x) decreases will be for x in this progression,
# at the point at which xζ gets close to (but not equal to) 0.
#
# TODO: explain why the target is always 0.
#
# If we set s = floor(n/d), then D_{q,ζ_q}(x) can decrease at a = s
# and potentially also at a = s+1.
assert (cur.m*zeta) % q in (cur.d, q - cur.d)
s = cur.n // cur.d
x0 = cur.u + s*cur.m
d0 = cur.n % cur.d
if DEBUG: print("\n(x0, d0, cur, s) =", (x0, d0, cur, s))
assert dist(0, x0*zeta, q) == d0
if x0-1 < len(Dcheck): assert Dcheck[x0-1] == cur.d
if x0 > mm:
if animator is not None:
if DEBUG: print("(q, zeta, old, cur, s) =", (q, zeta, old, cur, s))
animator.render(q, zeta, old, cur, None, s)
return cur.d
if x0 < len(Dcheck): assert Dcheck[x0] == d0
x1 = cur.u + (s+1)*cur.m
d1 = (s+1)*cur.d - cur.n
if d1 < d0:
if DEBUG: print("(x1, d1, cur, s+1) =", (x1, d1, cur, s+1))
assert dist(0, x1*zeta, q) == d1
if x1-1 < len(Dcheck): assert Dcheck[x1-1] == d0
if x1 > mm:
if animator is not None:
if DEBUG: print("(q, zeta, old, cur, s+1) =", (q, zeta, old, cur, s+1))
animator.render(q, zeta, old, cur, None, s+1)
return d0
if x1 < len(Dcheck): assert Dcheck[x1] == d1
# This is the case where the smaller new distance is past zero.
# The next iteration should consider the region of size d0 starting at x = x0
# (i.e. just before we went past zero) and increasing by x1, i.e. dividing
# that region by intervals of d1.
new = State(u=x0, m=x1, n=d0, d=d1)
else:
# This is the case where the smaller new distance is short of zero.
# The next iteration should check the region of size cur.d - d0 starting at x = x1
# (i.e. the wraparound past zero) and increasing by x0, i.e. dividing that
# region by intervals of d0.
new = State(u=x1, m=x0, n=cur.d - d0, d=d0)
assert dist(0, new.u*zeta, q) in (new.n, q - new.n)
#if dist(0, new.u*zeta, q) != new.n: print("hmm")
if animator is not None:
animator.render(q, zeta, old, cur, new, s)
(old, cur) = (cur, new)
def bruteforce_D(q, zeta, mm):
# Can't use sortedcontainers because its data structures are backed by
# lists-of-lists, not trees. We must have O(log n) insert, prev, and succ.
from bintrees import RBTree as sortedset
resD = deque([zeta])
lastd = zeta
S = sortedset()
S.insert(0, None)
S.insert(q, None)
for x in range(1, mm+1):
v = (x*zeta) % q
S.insert(v, None)
vp = S.prev_key(v)
vs = S.succ_key(v)
d = min(v-vp, vs-v)
resD.append(d)
#if DEBUG and d < lastd: print((x, d))
lastd = d
return list(resD)
def dist(x, y, q):
z = (x-y+q) % q
return min(z, q-z)
def signed_mod(x, q):
r = x % q
return r if r <= q//2 else r-q
class Animator:
fontfile = '/usr/share/texlive/texmf-dist/fonts/truetype/google/roboto/Roboto-Regular.ttf'
frame_duration = 20 # ms
pause_frames = 35
zoom_frames = 45
width = 800 # pixels
height = 400 # pixels
oversample = 3
line_halfwidth = 1 # subpixels
ground_colour = '#ffffff' # white
scale_colour = '#0000a0' # blue
midline_colour = '#c00000' # red
old_colour = '#a0a0a0' # grey
cur_colour = '#000000' # black
new_colour = '#008000' # green
final_colour = '#c00000' # red
def __init__(self, name):
# We don't want to depend on PIL unless an Animator is instantiated.
from PIL import Image, ImageDraw, ImageColor, ImageFont
self.Image = Image
self.ImageDraw = ImageDraw
self.ImageColor = ImageColor
self.font = ImageFont.truetype(self.fontfile, 20*self.oversample, index=0, encoding='unic')
self.font_super = ImageFont.truetype(self.fontfile, 12*self.oversample, index=0, encoding='unic')
self.images = deque()
self.name = name
def render(self, q, zeta, old, cur, new, s):
sys.stdout.write(':')
sys.stdout.flush()
n = min(cur.n, q//2)
for aa in range(1, s+1):
self.render_zoomed(q, zeta, old, cur, None, aa, n)
if new is None:
self.render_zoomed(q, zeta, old, cur, new, s, n, final=True)
return
self.render_zoomed( q, zeta, old, cur, new, s+1, n, frames=self.pause_frames)
step = (1.0*n/new.n - 1.0)/self.zoom_frames
for zoom in range(1, self.zoom_frames):
n_scale = int(n/(1.0 + zoom*step))
self.render_zoomed(q, zeta, old, cur, new, s+1, n_scale)
self.render_zoomed( q, zeta, old, cur, new, s+1, new.n, frames=self.pause_frames)
def render_zoomed(self, q, zeta, old, cur, new, aa, n_scale, frames=1, final=False):
px = self.oversample
lx = self.line_halfwidth
(w, h) = (self.width * px, self.height * px)
scale = (w/2)/n_scale
xmid = w//2
ymid = (40*px + h)//2
image = self.Image.new('RGB', (w, h), color=self.ground_colour)
image.convert('P')
draw = self.ImageDraw.Draw(image)
bits = int(log(n_scale, 2))
for tick in range(bits-3, bits+1):
xoff = int(scale*(1<= limit else '<'), limit)
if animator is not None:
animator.save()
assert Dq >= limit
def main():
args = sys.argv[1:]
if "--help" in args:
print("Usage: checksumsets.py [--animate]")
return
halflambda = 64
limit = 3<