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rbkz.py
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rbkz.py
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# -*- coding: utf-8 -*-
from random import randint
from fpylll import BKZ, Enumeration, EnumerationError
from fpylll.algorithms.bkz import BKZReduction as BKZ1
from fpylll.algorithms.bkz2 import BKZReduction as BKZ2
from fpylll.algorithms.bkz_stats import DummyStats
from fpylll.util import gaussian_heuristic
from time import time
from fpylll.numpy import dump_r
from fpylll import prune
NODE_PER_SEC = 2.** 26
PREPROC_BLOCK_SIZE_INIT = 32
PREPROC_BLOCK_SIZE_INCR = 4
class BKZReduction(BKZ2):
def __init__(self, A, recycling_pool_max_size=1):
"""Create new BKZ object.
:param A: an integer matrix, a GSO object or an LLL object
"""
BKZ2.__init__(self, A)
self.recycling_pool_max_size = recycling_pool_max_size
def recycled_svp_preprocessing(self, kappa, block_size, param, stats, preproc_block_size):
prepar = param.__class__(block_size=preproc_block_size, strategies=param.strategies, flags=BKZ.GH_BND)
clean = BKZ2.tour(self, prepar, kappa, kappa + block_size)
return clean
def tour(self, params, min_row=0, max_row=-1, stats=None):
"""One BKZ loop over all indices.
:param params: BKZ parameters
:param min_row: start index ≥ 0
:param max_row: last index ≤ n
:returns: ``True`` if no change was made and ``False`` otherwise
"""
if max_row == -1:
max_row = self.A.nrows
clean = True
for kappa in range(min_row, max_row-2):
block_size = min(params.block_size, max_row - kappa)
if block_size > 58:
clean &= self.recycled_svp_reduction(kappa, block_size, params, stats)
else:
clean &= self.svp_reduction(kappa, block_size, params, stats)
if stats:
stats.log_clean_kappa(kappa, clean)
return clean
def multi_insert(self, V, kappa, block_size, stats):
d = self.M.d
s = d
l = len(V)
print l,
for w in V:
self.M.create_row()
with self.M.row_ops(s, s+1):
for i in range(block_size):
self.M.row_addmul(s, kappa + i, w[i])
s += 1
for i in reversed(range(l)):
self.M.move_row(s - 1, kappa)
with stats.context("lll"):
self.lll_obj(kappa, kappa, kappa + block_size + l)
for i in range(l):
self.M.move_row(kappa + block_size, s - 1)
for i in range(l):
self.M.remove_last_row()
self.M.update_gso()
return
def recycled_svp_reduction(self, kappa, block_size, param, stats):
"""
:param kappa:
:param block_size:
:param params:
:param stats:
"""
if stats is None:
stats = DummyStats(self)
self.M.update_gso()
self.lll_obj.size_reduction(0, kappa+1)
self.lll_obj(kappa, kappa, kappa + block_size)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize = 1.0, False
print " - ",
preproc_block_size = PREPROC_BLOCK_SIZE_INIT
while remaining_probability > 1. - param.min_success_probability:
preproc_block_size += PREPROC_BLOCK_SIZE_INCR
start_preproc = time()
with stats.context("preproc"):
rec_clean = self.recycled_svp_preprocessing(kappa, block_size, param, stats, preproc_block_size)
time_preproc = time() - start_preproc
radius, expo = self.M.get_r_exp(kappa, kappa)
if param.flags & BKZ.GH_BND:
root_det = self.M.get_root_det(kappa, kappa+block_size)
radius, expo = gaussian_heuristic(radius, expo, block_size, root_det, param.gh_factor)
overhead = NODE_PER_SEC * time_preproc
with stats.context("postproc"):
self.M.update_gso()
R = dump_r(self.M, kappa, block_size)
# print R
goal_proba = 1.01 * ((param.min_success_probability - 1)/remaining_probability + 1)
pruning = prune(radius * 2**expo, overhead, goal_proba, [R],
descent_method="gradient", precision=53)
print goal_proba, pruning.probability
try:
enum_obj = Enumeration(self.M, self.recycling_pool_max_size)
aux_sols = []
with stats.context("svp", E=enum_obj):
K = [x for x in pruning.coefficients]
radius *= 1.05
for i in range(5, preproc_block_size):
K[i] /= 1.05
solution, max_dist = enum_obj.enumerate(kappa, kappa + block_size, radius, expo,
pruning=K, aux_sols=aux_sols)
V = [v for (v, _) in aux_sols[:10]]
self.multi_insert(V, kappa, block_size, stats)
except EnumerationError:
print 0,
pass
remaining_probability *= (1 - pruning.probability)
self.lll_obj.size_reduction(0, kappa+1)
new_first, new_first_expo = self.M.get_r_exp(kappa, kappa)
clean = old_first <= new_first * 2**(new_first_expo - old_first_expo)
return clean
# def to_cannonical(A, v, kappa, block_size):
# v = kappa*[0] + [x for x in v] + (A.nrows - (kappa + block_size)) * [0]
# v = IntegerMatrix.from_iterable(1, A.nrows, map(lambda x: int(round(x)), v))
# v = tuple((v*A)[0])
# return v
# def multi_insert_from_cannonical(M, V, kappa, block_size):
# d = M.d
# s = d
# l = len(V)
# for v in V:
# w = M.babai(v)
# for i in range(kappa+block_size, d):
# assert w[i] == 0
# M.create_row()
# with self.M.row_ops(s, s+1):
# for i in range(kappa + block_size):
# self.M.row_addmul(s, i, w[i])
# s += 1
# for i in range(l).reversed():
# self.M.move_row(kappa, d+i)
# with stats.context("lll"):
# self.lll_obj(kappa, kappa, kappa + block_size + 1)
# for i in range(l):
# self.M.move_row(kappa + block_size + i, s)
# for i in range(l):
# self.M.remove_last_row()