def svp_call(self, kappa, block_size, radius, pruning, nr_hints=0, tracer=dummy_tracer):
"""Call SVP oracle
:param kappa: current index
:param params: BKZ parameters
:param block_size: block size
:param tracer: object for maintaining statistics
:returns: Coordinates of SVP solution or ``None`` if none was found.
.. note::
``block_size`` may be smaller than ``params.block_size`` for the last blocks.
"""
solutions = []
try:
enum_obj = Enumeration(self.M, nr_hints+1, EvaluatorStrategy.OPPORTUNISTIC_N_SOLUTIONS)
if pruning is None:
with tracer.context("enumeration", enum_obj=enum_obj, probability=1., full=block_size==self.params.bkz_param.block_size):
solutions = enum_obj.enumerate(kappa, kappa + block_size, radius, 0)
else:
with tracer.context("enumeration", enum_obj=enum_obj, probability=pruning.expectation, full=block_size==self.params.bkz_param.block_size):
solutions = enum_obj.enumerate(kappa, kappa + block_size, radius, 0, pruning=pruning.coefficients)
return solutions
except EnumerationError:
return []
def enum_trial(bkz, preproc_cost, radius):
n = bkz.A.nrows
r = [bkz.M.get_r(i, i) for i in range(0, n)]
gh = gaussian_heuristic(r)
PRUNE_START = time()
NPS = 2**24
pruning = prune(radius, NPS * preproc_cost, [r], 10,
metric="solutions", float_type="dd",
flags=Pruning.GRADIENT|Pruning.NELDER_MEAD)
PRUNE_TIME = time() - PRUNE_START
ENUM_START = time()
enum_obj = Enumeration(bkz.M, sub_solutions=True)
success = False
try:
enum_obj.enumerate(0, n, radius, 0, pruning=pruning.coefficients)
success = True
except EnumerationError:
pass
print ("# [Prune] time %.4f"%PRUNE_TIME)
ENUM_TIME = time() - ENUM_START
print ("# [Enum] (Expecting %.5f solutions)"%(pruning.expectation)),
print (", TIME = %.2f"%ENUM_TIME)
"""
print ("# subsolutions : r[i]/gh"),
for (a, b) in enum_obj.sub_solutions:
print ("%.3f"%abs(a/gh)),
print
"""
insert_sub_solutions(bkz, enum_obj.sub_solutions)
return success
def enum_trial(bkz_obj, preproc_cost, gh_factor=1.1):
n = bkz_obj.A.nrows
r = [bkz_obj.M.get_r(i, i) for i in range(0, n)]
gh = gaussian_heuristic(r)
radius = max(r[0] * .99, gh * gh_factor)
PRUNE_START = time()
pruning = prune(radius,
NPS * preproc_cost, [r],
10,
metric="solutions",
float_type="dd",
flags=Pruning.GRADIENT)
PRUNE_TIME = time() - PRUNE_START
print "Pruning time %.4f" % PRUNE_TIME
ENUM_START = time()
enum_obj = Enumeration(bkz_obj.M, sub_solutions=True)
try:
print "Enum ... (Expecting %.5f solutions)" % (pruning.expectation),
enum_obj.enumerate(0, n, radius, 0, pruning=pruning.coefficients)
except EnumerationError:
pass
ENUM_TIME = time() - ENUM_START
print " \t\t\t\t\t\t TIME = %.2f" % ENUM_TIME
zeros = 0
print "subsolutions : r[i]/gh",
for (a, b) in enum_obj.sub_solutions[:20]:
print "%.3f" % abs(a / gh),
insert_sub_solutions(bkz_obj, enum_obj.sub_solutions[:n / 4])
return
def test_callback_enum(d=40):
FPLLL.set_random_seed(0x1337)
A = LLL.reduction(IntegerMatrix.random(100, "qary", k=50, q=7681))
M = GSO.Mat(A)
M.update_gso()
# we are not imposing a constraint
enum_obj = Enumeration(M)
solutions = enum_obj.enumerate(0, d, 0.99*M.get_r(0, 0), 0)
max_dist, sol = solutions[0]
assert(A.multiply_left(sol)[0] != 2)
# now we do
def callback(new_sol_coord):
if A.multiply_left(new_sol_coord)[0] == 2:
return True
else:
return False
enum_obj = Enumeration(M, callbackf=callback)
solutions = enum_obj.enumerate(0, d, 0.99*M.get_r(0, 0), 0)
max_dist, sol = solutions[0]
assert(A.multiply_left(sol)[0] == 2)
def enum(self, k, b, radius, pruning, for_hints=False):
solutions = []
try:
if self.recycle:
enum_obj = Enumeration(self.M,
b / 2,
always_update_radius=True)
else:
enum_obj = Enumeration(self.M, 1, always_update_radius=True)
if pruning is None:
with self.tracer.context("enumeration",
enum_obj=enum_obj,
probability=1.):
enum_obj.enumerate(k, k + b, radius, 0, aux_sols=solutions)
else:
with self.tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.probability):
enum_obj.enumerate(k,
k + b,
radius,
0,
pruning=pruning.coefficients,
aux_sols=solutions)
return solutions[0][0], [sol for (sol, _) in solutions[1:]]
except EnumerationError:
return None, []
def test_enum_gram_coherence():
"""
Test if the enumeration algorithm is consistent with the Gram matrices
The vectors returned by the enumeration should be the same wether a
lattice is given by its basis or by its Gram matrix
"""
dimensions = ((3, 3), (10, 10), (20, 20), (25, 25))
for m, n in dimensions:
for int_type in int_types:
A = make_integer_matrix(m, n, int_type=int_type)
LLL.reduction(A)
G = tools.compute_gram(A)
for float_type in float_types:
M_A = GSO.Mat(copy(A), float_type=float_type, gram=False)
M_G = GSO.Mat(copy(G), float_type=float_type, gram=True)
M_A.update_gso()
M_G.update_gso()
enum_obj_a = Enumeration(M_A, nr_solutions=min(m, 5))
shortest_vectors_a = enum_obj_a.enumerate(
0, M_A.d, M_A.get_r(0, 0), 0)
enum_obj_g = Enumeration(M_G, nr_solutions=min(m, 5))
shortest_vectors_g = enum_obj_g.enumerate(
0, M_G.d, M_G.get_r(0, 0), 0)
for i in range(len(shortest_vectors_a)):
assert shortest_vectors_a[i] == shortest_vectors_g[i]
def test_enum_enum():
for int_type in int_types:
A = make_integer_matrix(20, 20, int_type=int_type)
LLL.reduction(A)
for float_type in float_types:
M = GSO.Mat(copy(A), float_type=float_type)
M.update_gso()
enum_obj = Enumeration(M)
enum_obj.enumerate(0, M.d, M.get_r(0, 0), 0)
def svp_reduction_mpi_trial_enum (self, bkz_sub, preproc_cost, radius, kappa, block_size):
verbose = 0
bkz_sub.M.update_gso()
r = [bkz_sub.M.get_r(i, i) for i in range(kappa, kappa+block_size)]
r_old = r[0]
gh = gaussian_heuristic(r)
PRUNE_START = time()
try:
pruning = prune(radius, NPS[block_size] * preproc_cost, [r], 10,
metric="solutions", float_type="mpfr",
flags=Pruning.GRADIENT|Pruning.NELDER_MEAD)
"""
pruning = prune(radius, NPS[block_size] * preproc_cost, [r], 0.0001,
metric="probability", float_type="mpfr",
flags=Pruning.GRADIENT|Pruning.NELDER_MEAD)
"""
except:
return False, -1, 0, 0, 0
PRUNE_TIME = time() - PRUNE_START
ENUM_START = time()
enum_obj = Enumeration(bkz_sub.M, sub_solutions=True)
success = False
length = -1
#print radius, pruning.coefficients
estimate_cost = sum(pruning.detailed_cost) / NPS[block_size]
try:
enum_obj.enumerate(kappa, kappa+block_size, radius, 0, pruning=pruning.coefficients)
length = enum_obj.sub_solutions[0][0]
success = True
except EnumerationError:
pass
ENUM_TIME = time() - ENUM_START
if (verbose):
print ("# [Prune] time %.4f"%PRUNE_TIME)
print ("# [Enum] (Expecting %.5f solutions)"%(pruning.expectation)),
print (", TIME = %.2f"%ENUM_TIME)
"""
for (a, b) in enum_obj.sub_solutions[:20]:
print "%.3f"%abs(a/gh),
print
"""
bkz_sub.M.update_gso()
#A_old = deepcopy(bkz_sub.A)
bkz_sub.insert_sub_solutions(kappa, block_size, enum_obj.sub_solutions[:1+block_size/4])
#print self.compare(A_old, bkz_sub.A)
bkz_sub.M.update_gso()
r_new = bkz_sub.M.get_r(kappa, kappa)
if (r_new < r_old):
success = True
length = r_new
return success, length, PRUNE_TIME, ENUM_TIME, estimate_cost
def svp_call(self, kappa, block_size, params, stats=None):
"""Call SVP oracle
:param kappa: current index
:param params: BKZ parameters
:param block_size: block size
:param stats: object for maintaining statistics
:returns: Coordinates of SVP solution or ``None`` if none was found.
.. note::
``block_size`` may be smaller than ``params.block_size`` for the last blocks.
"""
max_dist, expo = self.M.get_r_exp(kappa, kappa)
delta_max_dist = self.lll_obj.delta * max_dist
if params.flags & BKZ.GH_BND:
root_det = self.M.get_root_det(kappa, kappa+block_size)
max_dist, expo = gaussian_heuristic(max_dist, expo, block_size, root_det, params.gh_factor)
try:
E = Enumeration(self.M)
solution, max_dist = E.enumerate(kappa, kappa + block_size, max_dist, expo)
stats.current_tour["enum nodes"] += E.get_nodes()
except EnumerationError as msg:
if params.flags & BKZ.GH_BND:
return None
else:
raise EnumerationError(msg)
if max_dist >= delta_max_dist:
return None
else:
return solution
def svp_enum(bkz, params, goal):
n = bkz.M.d
r = [bkz.M.get_r(i, i) for i in range(0, n)]
gh = gaussian_heuristic(r)
rerandomize = False
while bkz.M.get_r(0, 0) > goal:
if rerandomize:
bkz.randomize_block(0, n)
bkz.svp_preprocessing(0, n, params)
strategy = params.strategies[n]
radius = goal
pruning = strategy.get_pruning(goal, gh)
try:
enum_obj = Enumeration(bkz.M)
max_dist, solution = enum_obj.enumerate(
0, n, radius, 0, pruning=pruning.coefficients)[0]
bkz.svp_postprocessing(0, n, solution, tracer=dummy_tracer)
rerandomize = False
except EnumerationError:
rerandomize = True
bkz.lll_obj()
return
def parallel_svp_reduction_worker(self, kappa, block_size, params,
rerandomize):
"""
One SVP reduction, typically called in a worker process after forking.
:param kappa: current index
:param block_size: block size
:param params: BKZ parameters
:param tracer: object for maintaining statistics
"""
# we create a new tracer object to report back our timings to the calling process
tracer = BKZTreeTracer(self,
verbosity=params.flags & BKZ.VERBOSE,
root_label="svp")
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
self.randomize_block(
kappa + 1,
kappa + block_size,
density=params.rerandomization_density,
tracer=tracer)
with tracer.context("reduction"):
self.svp_preprocessing(kappa, block_size, params, tracer)
radius, expo = self.M.get_r_exp(kappa, kappa)
radius *= self.lll_obj.delta
if params.flags & BKZ.GH_BND and block_size > 30:
root_det = self.M.get_root_det(kappa, kappa + block_size)
radius, expo = adjust_radius_to_gh_bound(radius, expo, block_size,
root_det,
params.gh_factor)
pruning = self.get_pruning(kappa, block_size, params, tracer)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == params.block_size):
max_dist, solution = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
expo,
pruning=pruning.coefficients)[0]
with tracer.context("postprocessing"):
# we translate our solution to the canonical basis because our basis is not
# necessarily the basis of the calling process at this point
solution = self.A.multiply_left(solution, start=kappa)
except EnumerationError:
solution, max_dist = None, None
return solution, max_dist, tracer.trace, pruning.expectation
def svp_reduction(self, kappa, block_size, param, tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
self.lll_obj.size_reduction(0, kappa+1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize = 1.0, False
while remaining_probability > 1. - param.min_success_probability:
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
self.randomize_block(kappa+1, kappa+block_size,
density=param.rerandomization_density, tracer=tracer)
with tracer.context("reduction"):
self.svp_preprocessing(kappa, block_size, param, tracer=tracer)
radius, expo = self.M.get_r_exp(kappa, kappa)
radius *= self.lll_obj.delta
if param.flags & BKZ.GH_BND and block_size > 30:
root_det = self.M.get_root_det(kappa, kappa + block_size)
radius, expo = gaussian_heuristic(radius, expo, block_size, root_det, param.gh_factor)
pruning = self.get_pruning(kappa, block_size, param, tracer)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.probability,
full=block_size==param.block_size):
solution, max_dist = enum_obj.enumerate(kappa, kappa + block_size, radius, expo,
pruning=pruning.coefficients)[0]
with tracer.context("postprocessing"):
self.svp_postprocessing(kappa, block_size, solution, tracer=tracer)
rerandomize = False
except EnumerationError:
rerandomize = True
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 enum(self, k, b, radius, pruning, for_hints=False):
solutions = []
try:
if self.recycle:
enum_obj = Enumeration(self.M, b / 2)
else:
enum_obj = Enumeration(self.M, 1)
if pruning is None:
with self.tracer.context("enumeration",
enum_obj=enum_obj,
probability=1.):
solutions = enum_obj.enumerate(k, k + b, radius, 0)
else:
with self.tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation):
solutions = enum_obj.enumerate(
k, k + b, radius, 0, pruning=pruning.coefficients)
return [sol for (sol, _) in solutions[0:]]
except EnumerationError:
return None, []
def test_svp():
for m, n in dimensions:
A = make_integer_matrix(m, n)
A = LLL.reduction(A)
M = GSO.Mat(A)
M.update_gso()
E = Enumeration(M)
_, v1 = E.enumerate(0, M.d, M.get_r(0, 0), 0)[0]
v1 = A.multiply_left(v1)
nv1 = sum([v_**2 for v_ in v1])
v0 = SVP.shortest_vector(A)
nv0 = sum([v_**2 for v_ in v0])
assert nv0 == nv1
def enumeration_cvp(self, lattice, gso, pruning, radius, target):
self.log("Start Enumeration(CVP).")
try:
E = Enumeration(gso)
enum = E.enumerate(0,
lattice.nrows,
radius,
0,
gso.from_canonical(target),
pruning=pruning.coefficients)
_, v1 = enum[0]
return self.vector_from_coeffs(v1, lattice)
except EnumerationError:
self.log("No solution.")
return None
def test_cvp():
for m, n in dimensions:
A = make_integer_matrix(m, n)
A = LLL.reduction(A)
M = GSO.Mat(A)
M.update_gso()
t = list(make_integer_matrix(n, n)[0])
v0 = CVP.closest_vector(A, t)
E = Enumeration(M)
v1, _ = E.enumerate(0, A.nrows, 2, 40, M.from_canonical(t))
v1 = IntegerMatrix.from_iterable(1, A.nrows, map(lambda x: int(round(x)), v1))
v1 = tuple((v1*A)[0])
assert v0 == v1
def svp_reduction(self, kappa, block_size):
"""Call the SVP oracle and insert found vector into basis.
:param kappa: row index
:param block_size: an integer > 2
"""
clean = True
self.lll_obj(0, kappa, kappa + block_size)
if self.lll_obj.nswaps > 0:
clean = False
max_dist, expo = self.m.get_r_exp(kappa, kappa)
delta_max_dist = self.lll_obj.delta * max_dist
solution, max_dist = Enum.enumerate(self.m, max_dist, expo, kappa, kappa + block_size, None)
if max_dist >= delta_max_dist:
return clean
nonzero_vectors = len([x for x in solution if x])
if nonzero_vectors == 1:
first_nonzero_vector = None
for i in range(block_size):
if abs(solution[i]) == 1:
first_nonzero_vector = i
break
self.m.move_row(kappa + first_nonzero_vector, kappa)
self.lll_obj.size_reduction(kappa, kappa + 1)
else:
d = self.m.d
self.m.create_row()
with self.m.row_ops(d, d+1):
for i in range(block_size):
self.m.row_addmul(d, kappa + i, solution[i])
self.m.move_row(d, kappa)
self.lll_obj(kappa, kappa, kappa + block_size + 1)
self.m.move_row(kappa + block_size, d)
self.m.remove_last_row()
return False
def parallel_svp_reduction_worker(self, kappa, block_size, params, rerandomize):
"""
One SVP reduction, typically called in a worker process after forking.
:param kappa: current index
:param block_size: block size
:param params: BKZ parameters
:param tracer: object for maintaining statistics
"""
# we create a new tracer object to report back our timings to the calling process
tracer = BKZTreeTracer(self, verbosity=params.flags & BKZ.VERBOSE, root_label="svp")
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
self.randomize_block(
kappa + 1, kappa + block_size, density=params.rerandomization_density, tracer=tracer
)
with tracer.context("reduction"):
self.svp_preprocessing(kappa, block_size, params, tracer)
radius, expo = self.M.get_r_exp(kappa, kappa)
radius *= self.lll_obj.delta
if params.flags & BKZ.GH_BND and block_size > 30:
root_det = self.M.get_root_det(kappa, kappa + block_size)
radius, expo = gaussian_heuristic(radius, expo, block_size, root_det, params.gh_factor)
pruning = self.get_pruning(kappa, block_size, params, tracer)
try:
enum_obj = Enumeration(self.M)
with tracer.context(
"enumeration", enum_obj=enum_obj, probability=pruning.probability, full=block_size == params.block_size
):
solution, max_dist = enum_obj.enumerate(
kappa, kappa + block_size, radius, expo, pruning=pruning.coefficients
)[0]
with tracer.context("postprocessing"):
# we translate our solution to the canonical basis because our basis is not
# necessarily the basis of the calling process at this point
solution = self.A.multiply_left(solution, start=kappa)
except EnumerationError:
solution = None
return solution, tracer.trace, pruning.probability
def test_cvp():
for m, n in dimensions:
A = make_integer_matrix(m, n)
A = LLL.reduction(A)
M = GSO.Mat(A)
M.update_gso()
t = list(make_integer_matrix(n, n)[0])
v0 = CVP.closest_vector(A, t)
E = Enumeration(M)
v1, _ = E.enumerate(0, A.nrows, 2, 40, M.from_canonical(t))[0]
v1 = IntegerMatrix.from_iterable(1, A.nrows,
map(lambda x: int(round(x)), v1))
v1 = tuple((v1 * A)[0])
assert v0 == v1
def svp_call(self, kappa, block_size, params, tracer=None):
"""Call SVP oracle
:param kappa: current index
:param params: BKZ parameters
:param block_size: block size
:param tracer: object for maintaining statistics
:returns: Coordinates of SVP solution or ``None`` if none was found.
.. note::
``block_size`` may be smaller than ``params.block_size`` for the last blocks.
"""
max_dist, expo = self.M.get_r_exp(kappa, kappa)
delta_max_dist = self.lll_obj.delta * max_dist
if params.flags & BKZ.GH_BND:
root_det = self.M.get_root_det(kappa, kappa + block_size)
max_dist, expo = adjust_radius_to_gh_bound(max_dist, expo,
block_size, root_det,
params.gh_factor)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=1.0):
solution, max_dist = enum_obj.enumerate(
kappa, kappa + block_size, max_dist, expo)[0]
except EnumerationError as msg:
if params.flags & BKZ.GH_BND:
return None
else:
raise EnumerationError(msg)
if max_dist >= delta_max_dist * (1 << expo):
return None
else:
return solution
def dsvp_reduction(self, kappa, block_size):
"""FIXME! briefly describe function
:param kappa:
:param block_size:
:returns:
:rtype:
"""
clean = True
self.lll_obj(0, kappa, kappa + block_size)
if self.lll_obj.nswaps > 0:
clean = False
max_dist, expo = self.m.get_r_exp(kappa + block_size - 1, kappa + block_size - 1)
max_dist = 1.0/max_dist
expo *= -1.0
delta_max_dist = self.lll_obj.delta * max_dist
solution, max_dist = Enum.enumerate(self.m, max_dist, expo, kappa, kappa + block_size, None, dual=True)
if max_dist >= delta_max_dist:
return clean
with self.m.row_ops(kappa, kappa+block_size):
pairs = list(enumerate(solution, start=kappa))
[self.m.negate_row(pair[0]) for pair in pairs if pair[1] < 0]
pairs = map(lambda x: (x[0], abs(x[1])), pairs)
# GCD should be tree based but for proof of concept implementation, this will do
row, x = reduce(self.euclid, pairs)
if x != 1:
raise RuntimeError("Euclid failed!")
self.m.move_row(row, kappa + block_size - 1)
self.lll_obj(kappa, kappa, kappa + block_size)
return False
def svp_call(self, kappa, block_size, params, tracer=None):
"""Call SVP oracle
:param kappa: current index
:param params: BKZ parameters
:param block_size: block size
:param tracer: object for maintaining statistics
:returns: Coordinates of SVP solution or ``None`` if none was found.
.. note::
``block_size`` may be smaller than ``params.block_size`` for the last blocks.
"""
max_dist, expo = self.M.get_r_exp(kappa, kappa)
delta_max_dist = self.lll_obj.delta * max_dist
if params.flags & BKZ.GH_BND:
root_det = self.M.get_root_det(kappa, kappa+block_size)
max_dist, expo = adjust_radius_to_gh_bound(max_dist, expo, block_size, root_det, params.gh_factor)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration", enum_obj=enum_obj, probability=1.0):
solution, max_dist = enum_obj.enumerate(kappa, kappa + block_size, max_dist, expo)[0]
except EnumerationError as msg:
if params.flags & BKZ.GH_BND:
return None
else:
raise EnumerationError(msg)
if max_dist >= delta_max_dist * (1<<expo):
return None
else:
return solution
def yolo_hsvp(n, A, gh_factor, core=0):
timer = Timer()
ybkz = YoloBKZ(A, tuners=tuners)
start_from = None
start_from_rec = None
first_len = ybkz.M.get_r(0, 0)
root_det = ybkz.M.get_root_det(0, n)
gh_radius, ge = gaussian_heuristic(first_len, 0, n, root_det, 1.)
gh_radius = abs(gh_radius * 2**ge)
radius = gh_factor * gh_radius
target_prob = (1. / gh_factor)**(n / 2)
trial = 0
count = 0
restarted = 0
ybkz.randomize(0, n, density=1)
while True:
timer.reset()
max_efficiency = 0.
for b in range(8, n / 2, 4):
ybkz.tour(b, target_prob=.50)
restarted += 1
for b in range(n / 2, n - 10, 2):
count += 1
ybkz.tour(b, target_prob=.10)
overhead = NODE_PER_SEC * timer.elapsed()
R = tuple([ybkz.M.get_r(i, i) for i in range(0, n)])
title = "c=%d r=%d b=%d t=%.1fs" % (core, restarted, b,
timer.elapsed())
print title
pruning = prune(radius,
overhead,
target_prob, [R],
descent_method="hybrid",
precision=53,
start_from=start_from)
start_from = pruning.coefficients
print "c=%d pruning approximated t=%.1fs" % (core,
timer.elapsed())
pruning = prune(radius,
overhead,
target_prob, [R],
descent_method="gradient",
precision=YOLO_PRUNER_PREC,
start_from=start_from)
title = "c=%d r=%d b=%d t=%.1fs p=%1.2e e=%.1fs" % (
core, restarted, b, timer.elapsed(),
pruning.probability / target_prob,
(target_prob * timer.elapsed()) / pruning.probability)
print title
plot_and_save([log(x / gh_radius) / log(2.) for x in R], title,
'%d/c%ds%d.png' % (n, core, count))
start_from = pruning.coefficients
try:
enum_obj = Enumeration(ybkz.M)
solution, _ = enum_obj.enumerate(0,
n,
radius,
0,
pruning=pruning.coefficients)
ybkz.insert(0, n, solution)
print
print list(A[0])
return
except EnumerationError:
print "c=%d Enum failed t=%.1fs" % (core, timer.elapsed())
pass
efficiency = (pruning.probability / timer.elapsed())
# RECYCLING
r_start = count % 10
recycling_radius = ybkz.M.get_r(r_start, r_start) * .99
pruning = prune(recycling_radius,
overhead,
target_prob, [R[r_start:]],
descent_method="hybrid",
precision=53)
title = "REC c=%d r=%d b=%d t=%.1fs p=%1.2e e=%.1fs" % (
core, restarted, b, timer.elapsed(),
pruning.probability / target_prob,
(target_prob * timer.elapsed()) / pruning.probability)
print title
try:
hints = []
enum_obj = Enumeration(ybkz.M, n / 2)
solution, _ = enum_obj.enumerate(r_start,
n,
recycling_radius,
r_start,
pruning=pruning.coefficients,
aux_sols=hints)
hints = [sol for (sol, _) in hints[1:]]
ybkz.insert(r_start, n, solution, hints=hints)
print "c=%d Recycled %d t=%.1fs" % (core, len(hints) + 1,
timer.elapsed())
break
except EnumerationError:
pass
start_from_rec = pruning.coefficients
# END OF RECYCLING
if 2 * efficiency < max_efficiency:
ybkz.randomize(0, n, density=1)
ybkz.lll_obj(0, 0, n)
break
max_efficiency = max(efficiency, max_efficiency)
timer.reset()
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()
def approx_svp_time(seed, params, return_queue=None, progressive=False):
"""Run Approx-SVP_{1.05} reduction on ``A`` using ``params``.
:param seed: random seed for matrix creation
:param params: BKZ preprocessing parameters, preprocessing block size is ignored
:param return_queue: if not ``None``, the result is put on this queue.
:param progressive: run Progressive-BKZ
"""
from chal import load_svp_challenge
from fpylll.algorithms.bkz import BKZReduction as BKZBase
FPLLL.set_random_seed(seed)
A = load_svp_challenge(params.block_size, seed=seed)
M = GSO.Mat(A)
M.update_gso()
gh = gaussian_heuristic(M.r())
target_norm = 1.05**2 * gh
nodes_per_second = 2.0 * 10**9 / 100.0
self = BKZ2(M)
tracer = BKZTreeTracer(self, start_clocks=True)
rerandomize = False
preproc_cost = None
with tracer.context(("tour", 0)):
while M.get_r(0, 0) > target_norm:
with tracer.context("preprocessing"):
if rerandomize:
self.randomize_block(
1,
params.block_size,
density=params.rerandomization_density,
tracer=tracer)
with tracer.context("reduction"):
BKZBase.svp_preprocessing(self, 0, params.block_size,
params, tracer) # LLL
preproc = round(0.9878 * params.block_size -
24.12) # curve fitted to chal.py output
prepar = params.__class__(block_size=preproc,
strategies=params.strategies,
flags=BKZ.GH_BND)
self.tour(prepar, 0, params.block_size, tracer=tracer)
if preproc_cost is None:
preproc_cost = float(
tracer.trace.find("preprocessing")["walltime"])
preproc_cost *= nodes_per_second
with tracer.context("pruner"):
step_target = M.get_r(0,
0) * 0.99 if progressive else target_norm
pruner = Pruning.Pruner(step_target,
preproc_cost, [M.r()],
target=1,
metric=Pruning.EXPECTED_SOLUTIONS)
coefficients = pruner.optimize_coefficients([1.] * M.d)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
full=True):
max_dist, solution = enum_obj.enumerate(
0,
params.block_size,
target_norm,
0,
pruning=coefficients)[0]
with tracer.context("postprocessing"):
self.svp_postprocessing(0,
params.block_size,
solution,
tracer=tracer)
rerandomize = False
except EnumerationError:
rerandomize = True
self.M.update_gso()
logger.debug("r_0: %7.2f, target: %7.2f, preproc: %3d" %
(log(M.get_r(0, 0), 2), log(target_norm, 2), preproc))
tracer.exit()
tracer.trace.data["|A_0|"] = A[0].norm()
tracer.trace.data["preprocessing_block_size"] = preproc
if return_queue:
return_queue.put(tracer.trace)
else:
return tracer.trace
def svp_reduction_single_nosub(self,
kappa,
block_size,
params,
tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
if (block_size == 80):
start_time = clock()
#if block_size < 30:
# return BKZBase.svp_reduction(self, kappa, block_size, params, tracer=tracer)
self.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize, trials = 1.0, False, 0
while remaining_probability > 1. - params.min_success_probability:
preproc_start = clock()
with tracer.context("preprocessing"):
if False: # ((trials%5)==4):
self.randomize_block(kappa + 1,
kappa + block_size,
density=1,
tracer=tracer)
self.svp_preprocessing(kappa,
block_size,
params,
tracer=tracer,
trials=trials)
preproc_cost = clock() - preproc_start
with tracer.context("pruner"):
target = 1 - ((1. - params.min_success_probability) /
remaining_probability)
target = min(target, .5)
# target = params.min_success_probability
radius, pruning = self.get_pruning(kappa, block_size, params,
target * 1.01, preproc_cost,
tracer)
if (block_size == 90):
print " single target is ", target, ", expectation ", pruning.expectation
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == params.block_size):
max_dist, solution = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
0,
pruning=pruning.coefficients)[0]
with tracer.context("postprocessing"):
rerandomize = True
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer)
except EnumerationError:
rerandomize = False
remaining_probability *= (1 - pruning.expectation)
trials += 1
self.lll_obj.size_reduction(0, kappa + 1)
new_first, new_first_expo = self.M.get_r_exp(kappa, kappa)
if (block_size == 90):
print " time bs 90 is ", clock(
) - start_time, ", trials = ", trials, kappa
clean = old_first <= new_first * 2**(new_first_expo - old_first_expo)
return clean
def svp_reduction_single(self,
kappa,
block_size,
params,
tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
verbose = 0
if (verbose):
start_time = time()
self.M.update_gso()
r = [self.M.get_r(i, i) for i in range(kappa, kappa + block_size)]
#gh_length = gaussian_heuristic(r) * params.gh_factor
gh_length = gaussian_heuristic(r) * 1.1
kappa_length = self.M.get_r(kappa, kappa)
goal = min(kappa_length, gh_length)
self.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability = 1.0
rerandomize = False
trials = 0
sub_solutions = block_size > SUBSOL_BLOCKSIZE
while remaining_probability > 1. - params.min_success_probability:
# 1. preprocessing
preproc_start = time()
with tracer.context("preprocessing"):
self.M.update_gso()
self.svp_preprocessing(kappa,
block_size,
params,
trials,
tracer=tracer)
preproc_cost = time() - preproc_start
with tracer.context("pruner"):
target = 1 - ((1. - params.min_success_probability) /
remaining_probability)
radius, pruning = self.get_pruning(kappa, block_size, params,
target * 1.01, preproc_cost,
tracer)
# 2. enum
enum_obj = Enumeration(self.M, sub_solutions=sub_solutions)
try:
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == params.block_size):
max_dist, solution = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
0,
pruning=pruning.coefficients)[0]
# 3. post processing
with tracer.context("postprocessing"):
preproc_start = time(
) # Include post_processing time as the part of the next pre_processing
if not sub_solutions:
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer)
if sub_solutions:
self.insert_sub_solutions(
kappa, block_size,
enum_obj.sub_solutions[:1 + block_size / 4])
self.M.update_gso()
except EnumerationError:
preproc_start = time()
remaining_probability *= (1 - pruning.expectation)
trials += 1
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)
if (verbose):
if (rank == 0):
kappa_length = self.M.get_r(kappa, kappa)
print "# [rank %d] kappa %d, bs %d, r %d (gh %d), time %s, trials %s " % \
(rank, kappa, block_size, kappa_length, goal, time()-start_time, trials)
print "gh_factor: ", params.gh_factor
return clean
def svp_reduction(self,
kappa,
block_size,
param,
tracer=dummy_tracer,
top_level=False):
if top_level:
# do a full LLL up to kappa + block_size
with tracer.context("lll"):
self.lll_obj(0, kappa, kappa + block_size, 0)
remaining_probability, rerandomize = 1.0, False
while remaining_probability > 1. - param.min_success_probability:
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
# make a copy of the local block to restore in case rerandomisation decreases quality
self.copy_block(kappa, block_size)
self.randomize_block(
kappa + 1,
kappa + block_size,
density=param.rerandomization_density,
tracer=tracer)
with tracer.context("reduction"):
self.svp_preprocessing(kappa,
block_size,
param,
tracer=tracer)
radius, expo = self.M.get_r_exp(kappa, kappa)
radius *= self.lll_obj.delta
if param.flags & BKZ.GH_BND and block_size > 30:
root_det = self.M.get_root_det(kappa, kappa + block_size)
radius, expo = adjust_radius_to_gh_bound(
radius, expo, block_size, root_det, param.gh_factor)
pruning = self.get_pruning(kappa, block_size, param, tracer)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == param.block_size):
solution, max_dist = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
expo,
pruning=pruning.coefficients)[0]
with tracer.context("postprocessing"):
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer,
top_level=top_level)
if rerandomize:
self.delete_copy_block(kappa,
block_size,
restore=False)
rerandomize = False
except EnumerationError:
with tracer.context("postprocessing"):
if rerandomize:
# restore block, TODO don't do this unconditionally
self.delete_copy_block(kappa, block_size, restore=True)
rerandomize = True
remaining_probability *= (1 - pruning.expectation)
def 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.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize = 1.0, False
preproc_timer = LocalTimer()
enum_timer = LocalTimer()
pruner_timer = LocalTimer()
preproc_block_size = 0
while remaining_probability > 1. - param.min_success_probability:
with LocalTimerContext(preproc_timer):
with stats.context("preproc"):
if rerandomize:
self.randomize_block(
kappa + 1,
kappa + block_size,
density=param.rerandomization_density,
stats=stats)
preproc_block_size = self.svp_preprocessing(
kappa, block_size, param, stats)
self.lll_obj(kappa, kappa, kappa + block_size)
target_probability = 1.01 * (
(param.min_success_probability - 1) / remaining_probability +
1.)
with LocalTimerContext(pruner_timer):
radius, pruning = self.decide_enumeration(
kappa,
block_size,
param,
stats=stats,
preproc_time=preproc_timer.val,
target_probability=target_probability)
try:
enum_obj = Enumeration(self.M)
with LocalTimerContext(enum_timer):
with stats.context("svp", E=enum_obj):
solution, max_dist = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
0,
pruning=pruning.coefficients)
self.svp_postprocessing(kappa, block_size, solution, stats)
rerandomize = False
except EnumerationError:
rerandomize = True
if block_size >= AUTO_MIN_BLOCK_SIZE:
probability_per_second = pruning.probability / (
preproc_timer.val + enum_timer.val + pruner_timer.val)
self.preproc_decider.feedback(block_size, preproc_block_size,
probability_per_second)
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 svp_reduction(self, kappa, block_size, params, tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
self.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize = 1.0, False
# NOTE: In the tail we might have less than (1+c)β space
end = min(ceil(kappa + (1 + self.c) * block_size), self.M.d)
while remaining_probability > 1.0 - params.min_success_probability:
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
self.randomize_block(
kappa + 1,
kappa + block_size,
density=params.rerandomization_density,
tracer=tracer,
)
with tracer.context("reduction"):
self.svp_preprocessing(kappa,
end,
block_size,
params,
tracer=tracer)
with tracer.context("pruner"):
radius, exp, pruning = self.get_pruning(
kappa, block_size, params, tracer)
try:
enum_obj = Enumeration(self.M)
with tracer.context(
"enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=True,
): # HACK: we wan to record all enum costs.
max_dist, solution = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
exp,
pruning=pruning.coefficients,
)[0]
with tracer.context("postprocessing"):
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer)
rerandomize = False
except EnumerationError:
rerandomize = True
remaining_probability *= 1 - pruning.expectation
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 __call__(cls,
M,
predicate,
squared_target_norm,
invalidate_cache=lambda: None,
target_prob=None,
preproc_offset=20,
ph=0,
threads=1,
**kwds):
preproc_time = None
ntests = 0
if target_prob is None:
target_prob = cls.DEFAULT_TARGET_PROB
bkz_res = usvp_pred_bkz_enum_solve(M,
predicate,
block_size=min(
STRATEGIES_MAX_DIM, M.d),
invalidate_cache=invalidate_cache,
threads=threads)
if bkz_res.success: # this might be enough
return bkz_res
FPLLL.set_threads(threads)
M.update_gso()
bkz = BKZ2(M)
tracer = BKZTreeTracer(bkz, root_label="enum_pred", start_clocks=True)
remaining_probability, rerandomize, found, solution = (1.0, False,
False, None)
while remaining_probability > 1.0 - target_prob:
invalidate_cache()
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
bkz.randomize_block(0, M.d, tracer=tracer, density=3)
with tracer.context("reduction"):
with tracer.context("lll"):
bkz.lll_obj()
for _ in range(4):
bkz.tour(
BKZ.EasyParam(min(max(M.d - preproc_offset, 2),
STRATEGIES_MAX_DIM),
flags=BKZ.GH_BND),
tracer=tracer,
)
if preproc_time is None:
preproc_time = float(
tracer.trace.child("preprocessing")["cputime"])
with tracer.context("check"):
for v in M.B:
ntests += 1
if predicate(v, standard_basis=True):
found = True
solution = tuple([int(v_) for v_ in v])
break
if found:
break
with tracer.context("pruner"):
preproc_cost = threads * preproc_time * 2 * 10**9 / 100 # 100 cycles per node
with SuppressStream():
r = []
for i in range(M.d):
r_, exp = M.get_r_exp(i, i)
r.append(r_ * 2**(exp - ph))
(cost, prob), coeffs = cls.pruning_coefficients(
squared_target_norm / 2**ph,
r,
preproc_cost,
target_prob=target_prob)
def callbackf(v):
nonlocal ntests
ntests += 1
return predicate(v, standard_basis=False)
enum_obj = Enumeration(M, callbackf=callbackf)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=prob,
full=True):
try:
solutions = enum_obj.enumerate(0,
M.d,
squared_target_norm / 2**ph,
ph,
pruning=coeffs)
_, v = solutions[0]
found = True
solution = tuple([int(v_) for v_ in M.B.multiply_left(v)])
break
except EnumerationError:
pass
rerandomize = True
remaining_probability *= 1 - prob
tracer.exit()
FPLLL.set_threads(1)
b0, b0e = bkz.M.get_r_exp(0, 0)
return USVPPredSolverResults(
success=found,
solution=solution,
ntests=ntests + bkz_res.ntests,
b0=b0**(0.5) * 2**(b0e / 2.0),
cputime=tracer.trace.data["cputime"] + bkz_res.cputime,
walltime=tracer.trace.data["walltime"] + bkz_res.walltime,
data=tracer.trace,
)
def svp_reduction(self, kappa, block_size, param, tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
self.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize = 1.0, False
while remaining_probability > 1. - param.min_success_probability:
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
self.randomize_block(
kappa + 1,
kappa + block_size,
density=param.rerandomization_density,
tracer=tracer)
with tracer.context("reduction"):
self.svp_preprocessing(kappa,
block_size,
param,
tracer=tracer)
radius, expo = self.M.get_r_exp(kappa, kappa)
radius *= self.lll_obj.delta
if param.flags & BKZ.GH_BND and block_size > 30:
root_det = self.M.get_root_det(kappa, kappa + block_size)
radius, expo = adjust_radius_to_gh_bound(
radius, expo, block_size, root_det, param.gh_factor)
pruning = self.get_pruning(kappa, block_size, param, tracer)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == param.block_size):
solution, max_dist = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
expo,
pruning=pruning.coefficients)[0]
with tracer.context("postprocessing"):
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer)
rerandomize = False
except EnumerationError:
rerandomize = True
remaining_probability *= (1 - pruning.expectation)
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 svp_reduction(self, kappa, block_size, params, tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
self.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize = 1.0, False
while remaining_probability > 1. - params.min_success_probability:
with tracer.context("preprocessing"):
if rerandomize:
with tracer.context("randomization"):
self.randomize_block(
kappa + 1,
kappa + block_size,
density=params.rerandomization_density,
tracer=tracer)
with tracer.context("reduction"):
self.svp_preprocessing(kappa,
block_size,
params,
tracer=tracer)
with tracer.context("pruner"):
radius, pruning = self.get_pruning(kappa, block_size, params,
tracer)
try:
enum_obj = Enumeration(self.M)
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == params.block_size):
max_dist, solution = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
0,
pruning=pruning.coefficients)[0]
with tracer.context("postprocessing"):
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer)
rerandomize = False
except EnumerationError:
rerandomize = True
remaining_probability *= (1 - pruning.expectation)
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 svp_reduction(self, kappa, block_size, params, tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
self.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability, rerandomize, trials = 1.0, False, 0
sub_solutions = block_size > SUBSOL_BLOCKSIZE
preproc_start = time()
while remaining_probability > 1. - params.min_success_probability:
with tracer.context("preprocessing"):
self.svp_preprocessing(kappa,
block_size,
params,
trials,
tracer=tracer)
preproc_cost = time() - preproc_start
with tracer.context("pruner"):
target = 1 - ((1. - params.min_success_probability) /
remaining_probability)
radius, pruning = self.get_pruning(kappa, block_size, params,
target * 1.01, preproc_cost,
tracer)
enum_obj = Enumeration(self.M, sub_solutions=sub_solutions)
try:
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == params.block_size):
max_dist, solution = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
0,
pruning=pruning.coefficients)[0]
with tracer.context("postprocessing"):
preproc_start = time(
) # Include post_processing time as the part of the next pre_processing
if not sub_solutions:
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer)
if sub_solutions:
self.insert_sub_solutions(
kappa, block_size,
enum_obj.sub_solutions[:1 + block_size / 4])
except EnumerationError:
preproc_start = time()
remaining_probability *= (1 - pruning.expectation)
trials += 1
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 svp_reduction_single(self,
kappa,
block_size,
params,
tracer=dummy_tracer):
"""
:param kappa:
:param block_size:
:param params:
:param tracer:
"""
print self.lll_obj.delta
verbose = 0
if (params.flags & BKZ.DUMP_GSO):
verbose = 1
if (verbose):
start_time = time()
self.M.update_gso()
r = [self.M.get_r(i, i) for i in range(kappa, kappa + block_size)]
gh_length = gaussian_heuristic(r)
kappa_length = self.M.get_r(kappa, kappa)
goal = min(kappa_length, gh_length)
self.lll_obj.size_reduction(0, kappa + 1)
old_first, old_first_expo = self.M.get_r_exp(kappa, kappa)
remaining_probability = 1.0
rerandomize = False
trials = 0
sub_solutions = block_size > SUBSOL_BLOCKSIZE
# copy old lattice
if (params.flags & BKZ.DUMP_GSO):
A_backup = self.copy_to_IntegerMatrix_long(self.A)
v_old = A_backup[kappa]
r_old = [
log(self.M.get_r(i, i))
for i in range(kappa, kappa + block_size)
]
# main loop
while remaining_probability > 1. - params.min_success_probability:
# 1. preprocessing
preproc_start = time()
with tracer.context("preprocessing"):
#self.M.update_gso()
self.svp_preprocessing(kappa,
block_size,
params,
trials,
tracer=tracer)
preproc_cost = time() - preproc_start
with tracer.context("pruner"):
target = 1 - ((1. - params.min_success_probability) /
remaining_probability)
radius, pruning = self.get_pruning(kappa, block_size, params,
target * 1.01, preproc_cost,
tracer)
# 2. enum
enum_obj = Enumeration(self.M, sub_solutions=sub_solutions)
try:
with tracer.context("enumeration",
enum_obj=enum_obj,
probability=pruning.expectation,
full=block_size == params.block_size):
max_dist, solution = enum_obj.enumerate(
kappa,
kappa + block_size,
radius,
0,
pruning=pruning.coefficients)[0]
# 3. post processing
with tracer.context("postprocessing"):
preproc_start = time(
) # Include post_processing time as the part of the next pre_processing
if not sub_solutions:
self.svp_postprocessing(kappa,
block_size,
solution,
tracer=tracer)
if sub_solutions:
self.insert_sub_solutions(
kappa, block_size,
enum_obj.sub_solutions[:1 + block_size / 4])
self.M.update_gso()
except EnumerationError:
preproc_start = time()
remaining_probability *= (1 - pruning.expectation)
trials += 1
# recover basis
if (params.flags & BKZ.DUMP_GSO):
r_new = [
self.M.get_r(i, i) for i in range(kappa, kappa + block_size)
]
current = self.copy_to_vector_long(self.A[kappa])
# update
self.copy_from_IntegerMatrix_long(A_backup)
self.M = GSO.Mat(self.A, float_type=TYPE)
self.M.update_gso()
self.lll_obj = LLL.Reduction(self.M, flags=LLL.DEFAULT)
self.insert_in_IntegerMatrix(self.A, current, kappa, block_size)
# update again for safe
self.M = GSO.Mat(self.A, float_type=TYPE)
self.M.update_gso()
self.M = GSO.Mat(self.A, float_type=TYPE)
self.M = GSO.Mat(self.A, float_type=TYPE)
self.M.update_gso()
self.lll_obj = LLL.Reduction(self.M, flags=LLL.DEFAULT)
if (not self.check_compare(A_backup, self.A, kappa, block_size)):
print "# error exit"
sys.exit(1)
self.M.update_gso()
new_first, new_first_expo = self.M.get_r_exp(kappa, kappa)
clean = old_first <= new_first * 2**(new_first_expo - old_first_expo)
if (params.flags & BKZ.DUMP_GSO):
global stat_update_gh
r_new = self.M.get_r(kappa, kappa)
r_newlog = [
log(self.M.get_r(i, i))
for i in range(kappa, kappa + block_size)
]
stat_update_gh[stat_tours - 1].append(
float(sqrt(r_new / gh_length)))
if (verbose):
if (rank == 0):
kappa_length = r_new
print "# [rank %d] kappa %d, bs %d, r %d (gh %d), time %s, trials %s " % \
(rank, kappa, block_size, kappa_length, goal, time()-start_time, trials)
det_n = float(sum(r_old) / block_size)
normalized_old = [(r_old[i] - det_n)
for i in range(0, block_size)]
normalized_new = [(r_newlog[i] - det_n)
for i in range(0, block_size)]
global stat_old_norm
global stat_new_norm
if (block_size == params.block_size):
for i in range(block_size):
stat_old_norm[i] = stat_old_norm[i] + normalized_old[i]
stat_new_norm[i] = stat_new_norm[i] + normalized_new[i]
return clean