def main():
# sequence = "DdDddUUdDD"
# lower_bound = 10 ** 6
sequence = "UDDDUdddDDUDDddDdDddDDUDDdUUDd"
lower_bound = 10**15
p = x
for ch in reversed(sequence):
if ch == "D":
p = 3 * p
elif ch == "U":
p = (3 * p - 2) / 4
elif ch == "d":
p = (3 * p + 1) / 2
else:
raise NotImplementedError
p = Poly(p)
print(p)
a, b = p.all_coeffs()
n_a, d_a = sympy.fraction(a)
n_b, d_b = sympy.fraction(b)
assert d_a == d_b
d = d_a
r = (-n_b) * pow(n_a, -1, d) % d
assert (r * n_a + n_b) % d == 0
n_a %= d
n_b %= d
minimal = int(a * r + b)
step = int(a * d)
offset = math.ceil((lower_bound + 1 - minimal) / step)
print(f"{minimal} + {step} * {offset} = {minimal + step * offset}")
def _sqrt_symbolic_denest(a, b, r):
"""Given an expression, sqrt(a + b*sqrt(b)), return the denested
expression or None.
Algorithm:
If r = ra + rb*sqrt(rr), try replacing sqrt(rr) in ``a`` with
(y**2 - ra)/rb, and if the result is a quadratic, ca*y**2 + cb*y + cc, and
(cb + b)**2 - 4*ca*cc is 0, then sqrt(a + b*sqrt(r)) can be rewritten as
sqrt(ca*(sqrt(r) + (cb + b)/(2*ca))**2).
Examples
========
>>> from sympy.simplify.sqrtdenest import _sqrt_symbolic_denest, sqrtdenest
>>> from sympy import sqrt, Symbol
>>> from sympy.abc import x
>>> a, b, r = 16 - 2*sqrt(29), 2, -10*sqrt(29) + 55
>>> _sqrt_symbolic_denest(a, b, r)
sqrt(11 - 2*sqrt(29)) + sqrt(5)
If the expression is numeric, it will be simplified:
>>> w = sqrt(sqrt(sqrt(3) + 1) + 1) + 1 + sqrt(2)
>>> sqrtdenest(sqrt((w**2).expand()))
1 + sqrt(2) + sqrt(1 + sqrt(1 + sqrt(3)))
Otherwise, it will only be simplified if assumptions allow:
>>> w = w.subs(sqrt(3), sqrt(x + 3))
>>> sqrtdenest(sqrt((w**2).expand()))
sqrt((sqrt(sqrt(sqrt(x + 3) + 1) + 1) + 1 + sqrt(2))**2)
Notice that the argument of the sqrt is a square. If x is made positive
then the sqrt of the square is resolved:
>>> _.subs(x, Symbol('x', positive=True))
sqrt(sqrt(sqrt(x + 3) + 1) + 1) + 1 + sqrt(2)
"""
a, b, r = map(sympify, (a, b, r))
rval = _sqrt_match(r)
if not rval:
return None
ra, rb, rr = rval
if rb:
y = Dummy('y', positive=True)
try:
newa = Poly(a.subs(sqrt(rr), (y**2 - ra) / rb), y)
except PolynomialError:
return None
if newa.degree() == 2:
ca, cb, cc = newa.all_coeffs()
cb += b
if _mexpand(cb**2 - 4 * ca * cc).equals(0):
z = sqrt(ca * (sqrt(r) + cb / (2 * ca))**2)
if z.is_number:
z = _mexpand(Mul._from_args(z.as_content_primitive()))
return z
def _sqrt_symbolic_denest(a, b, r):
"""Given an expression, sqrt(a + b*sqrt(b)), return the denested
expression or None.
Algorithm:
If r = ra + rb*sqrt(rr), try replacing sqrt(rr) in ``a`` with
(y**2 - ra)/rb, and if the result is a quadratic, ca*y**2 + cb*y + cc, and
(cb + b)**2 - 4*ca*cc is 0, then sqrt(a + b*sqrt(r)) can be rewritten as
sqrt(ca*(sqrt(r) + (cb + b)/(2*ca))**2).
Examples
========
>>> from sympy.simplify.sqrtdenest import _sqrt_symbolic_denest, sqrtdenest
>>> from sympy import sqrt, Symbol
>>> from sympy.abc import x
>>> a, b, r = 16 - 2*sqrt(29), 2, -10*sqrt(29) + 55
>>> _sqrt_symbolic_denest(a, b, r)
sqrt(-2*sqrt(29) + 11) + sqrt(5)
If the expression is numeric, it will be simplified:
>>> w = sqrt(sqrt(sqrt(3) + 1) + 1) + 1 + sqrt(2)
>>> sqrtdenest(sqrt((w**2).expand()))
1 + sqrt(2) + sqrt(1 + sqrt(1 + sqrt(3)))
Otherwise, it will only be simplified if assumptions allow:
>>> w = w.subs(sqrt(3), sqrt(x + 3))
>>> sqrtdenest(sqrt((w**2).expand()))
sqrt((sqrt(sqrt(sqrt(x + 3) + 1) + 1) + 1 + sqrt(2))**2)
Notice that the argument of the sqrt is a square. If x is made positive
then the sqrt of the square is resolved:
>>> _.subs(x, Symbol('x', positive=True))
sqrt(sqrt(sqrt(x + 3) + 1) + 1) + 1 + sqrt(2)
"""
a, b, r = map(sympify, (a, b, r))
rval = _sqrt_match(r)
if not rval:
return None
ra, rb, rr = rval
if rb:
y = Dummy('y', positive=True)
try:
newa = Poly(a.subs(sqrt(rr), (y**2 - ra)/rb), y)
except PolynomialError:
return None
if newa.degree() == 2:
ca, cb, cc = newa.all_coeffs()
cb += b
if _mexpand(cb**2 - 4*ca*cc).equals(0):
z = sqrt(ca*(sqrt(r) + cb/(2*ca))**2)
if z.is_number:
z = _mexpand(Mul._from_args(z.as_content_primitive()))
return z
def _valid_expr(expr):
"""
return coefficients of expr if it is a univariate polynomial
with integer coefficients else raise a ValueError.
"""
if not expr.is_polynomial():
raise ValueError("The expression should be a polynomial")
polynomial = Poly(expr)
if not polynomial.is_univariate:
raise ValueError("The expression should be univariate")
if not polynomial.domain == ZZ:
raise ValueError("The expression should should have integer coefficients")
return polynomial.all_coeffs()
def _solve_inequality(ie, s):
""" A hacky replacement for solve, since the latter only works for
univariate inequalities. """
from sympy import Poly
if not ie.rel_op in ('>', '>=', '<', '<='):
raise NotImplementedError
expr = ie.lhs - ie.rhs
p = Poly(expr, s)
if p.degree() != 1:
raise NotImplementedError('%s' % ie)
a, b = p.all_coeffs()
if a.is_positive:
return ie.func(s, -b/a)
elif a.is_negative:
return ie.func(-b/a, s)
else:
raise NotImplementedError
def _solve_inequality(ie, s):
""" A hacky replacement for solve, since the latter only works for
univariate inequalities. """
from sympy import Poly
if not ie.rel_op in ('>', '>=', '<', '<='):
raise NotImplementedError
expr = ie.lhs - ie.rhs
p = Poly(expr, s)
if p.degree() != 1:
raise NotImplementedError('%s' % ie)
a, b = p.all_coeffs()
if a.is_positive:
return ie.func(s, -b / a)
elif a.is_negative:
return ie.func(-b / a, s)
else:
raise NotImplementedError
def _solve_inequality(ie, s):
""" A hacky replacement for solve, since the latter only works for
univariate inequalities. """
if not ie.rel_op in (">", ">=", "<", "<="):
raise NotImplementedError
expr = ie.lhs - ie.rhs
try:
p = Poly(expr, s)
except PolynomialError:
raise NotImplementedError
if p.degree() != 1:
raise NotImplementedError("%s" % ie)
a, b = p.all_coeffs()
if a.is_positive:
return ie.func(s, -b / a)
elif a.is_negative:
return ie.func(-b / a, s)
else:
raise NotImplementedError
def _solve_inequality(ie, s):
""" A hacky replacement for solve, since the latter only works for
univariate inequalities. """
expr = ie.lhs - ie.rhs
try:
p = Poly(expr, s)
if p.degree() != 1:
raise NotImplementedError
except (PolynomialError, NotImplementedError):
try:
return reduce_rational_inequalities([[ie]], s)
except PolynomialError:
return solve_univariate_inequality(ie, s)
a, b = p.all_coeffs()
if a.is_positive or ie.rel_op in ('!=', '=='):
return ie.func(s, -b/a)
elif a.is_negative:
return ie.reversed.func(s, -b/a)
else:
raise NotImplementedError
def _solve_inequality(ie, s, assume=True):
""" A hacky replacement for solve, since the latter only works for
univariate inequalities. """
if not ie.rel_op in ('>', '>=', '<', '<='):
raise NotImplementedError
expr = ie.lhs - ie.rhs
try:
p = Poly(expr, s)
if p.degree() != 1:
raise NotImplementedError
except (PolynomialError, NotImplementedError):
try:
n, d = expr.as_numer_denom()
return reduce_rational_inequalities([[ie]], s, assume=assume)
except PolynomialError:
return solve_univariate_inequality(ie, s, assume=assume)
a, b = p.all_coeffs()
if a.is_positive:
return ie.func(s, -b/a)
elif a.is_negative:
return ie.func(-b/a, s)
else:
raise NotImplementedError
def _solve_inequality(ie, s, assume=True):
""" A hacky replacement for solve, since the latter only works for
univariate inequalities. """
if not ie.rel_op in ('>', '>=', '<', '<='):
raise NotImplementedError
expr = ie.lhs - ie.rhs
try:
p = Poly(expr, s)
if p.degree() != 1:
raise NotImplementedError
except (PolynomialError, NotImplementedError):
try:
n, d = expr.as_numer_denom()
return reduce_rational_inequalities([[ie]], s, assume=assume)
except PolynomialError:
return solve_univariate_inequality(ie, s, assume=assume)
a, b = p.all_coeffs()
if a.is_positive:
return ie.func(s, -b / a)
elif a.is_negative:
return ie.func(-b / a, s)
else:
raise NotImplementedError
def _get_const_characteristic_eq_sols(r, func, order):
r"""
Returns the roots of characteristic equation of constant coefficient
linear ODE and list of collectterms which is later on used by simplification
to use collect on solution.
The parameter `r` is a dict of order:coeff terms, where order is the order of the
derivative on each term, and coeff is the coefficient of that derivative.
"""
x = func.args[0]
# First, set up characteristic equation.
chareq, symbol = S.Zero, Dummy('x')
for i in r.keys():
if type(i) == str or i < 0:
pass
else:
chareq += r[i] * symbol**i
chareq = Poly(chareq, symbol)
# Can't just call roots because it doesn't return rootof for unsolveable
# polynomials.
chareqroots = roots(chareq, multiple=True)
if len(chareqroots) != order:
chareqroots = [rootof(chareq, k) for k in range(chareq.degree())]
chareq_is_complex = not all(i.is_real for i in chareq.all_coeffs())
# Create a dict root: multiplicity or charroots
charroots = defaultdict(int)
for root in chareqroots:
charroots[root] += 1
# We need to keep track of terms so we can run collect() at the end.
# This is necessary for constantsimp to work properly.
collectterms = []
gensols = []
conjugate_roots = [] # used to prevent double-use of conjugate roots
# Loop over roots in theorder provided by roots/rootof...
for root in chareqroots:
# but don't repoeat multiple roots.
if root not in charroots:
continue
multiplicity = charroots.pop(root)
for i in range(multiplicity):
if chareq_is_complex:
gensols.append(x**i * exp(root * x))
collectterms = [(i, root, 0)] + collectterms
continue
reroot = re(root)
imroot = im(root)
if imroot.has(atan2) and reroot.has(atan2):
# Remove this condition when re and im stop returning
# circular atan2 usages.
gensols.append(x**i * exp(root * x))
collectterms = [(i, root, 0)] + collectterms
else:
if root in conjugate_roots:
collectterms = [(i, reroot, imroot)] + collectterms
continue
if imroot == 0:
gensols.append(x**i * exp(reroot * x))
collectterms = [(i, reroot, 0)] + collectterms
continue
conjugate_roots.append(conjugate(root))
gensols.append(x**i * exp(reroot * x) * sin(abs(imroot) * x))
gensols.append(x**i * exp(reroot * x) * cos(imroot * x))
# This ordering is important
collectterms = [(i, reroot, imroot)] + collectterms
return gensols, collectterms
