def rsolve_hyper(coeffs, f, n, **hints):
"""Given linear recurrence operator L of order 'k' with polynomial
coefficients and inhomogeneous equation Ly = f we seek for all
hypergeometric solutions over field K of characteristic zero.
The inhomogeneous part can be either hypergeometric or a sum
of a fixed number of pairwise dissimilar hypergeometric terms.
The algorithm performs three basic steps:
(1) Group together similar hypergeometric terms in the
inhomogeneous part of Ly = f, and find particular
solution using Abramov's algorithm.
(2) Compute generating set of L and find basis in it,
so that all solutions are linearly independent.
(3) Form final solution with the number of arbitrary
constants equal to dimension of basis of L.
Term a(n) is hypergeometric if it is annihilated by first order
linear difference equations with polynomial coefficients or, in
simpler words, if consecutive term ratio is a rational function.
The output of this procedure is a linear combination of fixed
number of hypergeometric terms. However the underlying method
can generate larger class of solutions - D'Alembertian terms.
Note also that this method not only computes the kernel of the
inhomogeneous equation, but also reduces in to a basis so that
solutions generated by this procedure are linearly independent
For more information on the implemented algorithm refer to:
[1] M. Petkovsek, Hypergeometric solutions of linear recurrences
with polynomial coefficients, J. Symbolic Computation,
14 (1992), 243-264.
[2] M. Petkovsek, H. S. Wilf, D. Zeilberger, A = B, 1996.
"""
coeffs = map(sympify, coeffs)
f = sympify(f)
r, kernel = len(coeffs)-1, []
if not f.is_zero:
if f.is_Add:
similar = {}
for g in f.expand().args:
if not g.is_hypergeometric(n):
return None
for h in similar.iterkeys():
if hypersimilar(g, h, n):
similar[h] += g
break
else:
similar[g] = S.Zero
inhomogeneous = []
for g, h in similar.iteritems():
inhomogeneous.append(g+h)
elif f.is_hypergeometric(n):
inhomogeneous = [f]
else:
return None
for i, g in enumerate(inhomogeneous):
coeff, polys = S.One, coeffs[:]
denoms = [ S.One ] * (r+1)
s = hypersimp(g, n)
for j in xrange(1, r+1):
coeff *= s.subs(n, n+j-1)
p, q = coeff.as_numer_denom()
polys[j] *= p
denoms[j] = q
for j in xrange(0, r+1):
polys[j] *= Mul(*(denoms[:j] + denoms[j+1:]))
R = rsolve_poly(polys, Mul(*denoms), n)
if not (R is None or R is S.Zero):
inhomogeneous[i] *= R
else:
return None
result = Add(*inhomogeneous)
else:
result = S.Zero
Z = Symbol('Z', dummy=True)
p, q = coeffs[0], coeffs[r].subs(n, n-r+1)
p_factors = [ z for z in roots(p, n).iterkeys() ]
q_factors = [ z for z in roots(q, n).iterkeys() ]
factors = [ (S.One, S.One) ]
for p in p_factors:
for q in q_factors:
if p.is_integer and q.is_integer and p <= q:
continue
else:
factors += [(n-p, n-q)]
p = [ (n-p, S.One) for p in p_factors ]
q = [ (S.One, n-q) for q in q_factors ]
factors = p + factors + q
for A, B in factors:
polys, degrees = [], []
D = A*B.subs(n, n+r-1)
for i in xrange(0, r+1):
a = Mul(*[ A.subs(n, n+j) for j in xrange(0, i) ])
b = Mul(*[ B.subs(n, n+j) for j in xrange(i, r) ])
poly = exquo(coeffs[i]*a*b, D, n)
polys.append(poly.as_poly(n))
if not poly.is_zero:
degrees.append(polys[i].degree())
d, poly = max(degrees), S.Zero
for i in xrange(0, r+1):
coeff = polys[i].nth(d)
if coeff is not S.Zero:
poly += coeff * Z**i
for z in roots(poly, Z).iterkeys():
if not z.is_real or z.is_zero:
continue
C = rsolve_poly([ polys[i]*z**i for i in xrange(r+1) ], 0, n)
if C is not None and C is not S.Zero:
ratio = z * A * C.subs(n, n + 1) / B / C
K = product(simplify(ratio), (n, 0, n-1))
if casoratian(kernel+[K], n) != 0:
kernel.append(K)
symbols = [ Symbol('C'+str(i)) for i in xrange(len(kernel)) ]
for C, ker in zip(symbols, kernel):
result += C * ker
if hints.get('symbols', False):
return (result, symbols)
else:
return result
def rsolve(f, y, init=None):
"""Solve univariate recurrence with rational coefficients.
Given k-th order linear recurrence Ly = f, or equivalently:
a_{k}(n) y(n+k) + a_{k-1}(n) y(n+k-1) + ... + a_{0}(n) y(n) = f
where a_{i}(n), for i=0..k, are polynomials or rational functions
in n, and f is a hypergeometric function or a sum of a fixed number
of pairwise dissimilar hypergeometric terms in n, finds all solutions
or returns None, if none were found.
Initial conditions can be given as a dictionary in two forms:
[1] { n_0 : v_0, n_1 : v_1, ..., n_m : v_m }
[2] { y(n_0) : v_0, y(n_1) : v_1, ..., y(n_m) : v_m }
or as a list L of values:
L = [ v_0, v_1, ..., v_m ]
where L[i] = v_i, for i=0..m, maps to y(n_i).
As an example lets consider the following recurrence:
(n - 1) y(n + 2) - (n**2 + 3 n - 2) y(n + 1) + 2 n (n + 1) y(n) == 0
>>> from sympy import Function, rsolve
>>> from sympy.abc import n
>>> y = Function('y')
>>> f = (n-1)*y(n+2) - (n**2+3*n-2)*y(n+1) + 2*n*(n+1)*y(n)
>>> rsolve(f, y(n))
C0*gamma(1 + n) + C1*2**n
>>> rsolve(f, y(n), { y(0):0, y(1):3 })
-3*gamma(1 + n) + 3*2**n
"""
if isinstance(f, Equality):
f = f.lhs - f.rhs
if f.is_Add:
F = f.args
else:
F = [f]
k = Wild('k')
n = y.args[0]
h_part = {}
i_part = S.Zero
for g in F:
if g.is_Mul:
G = g.args
else:
G = [g]
coeff = S.One
kspec = None
for h in G:
if h.is_Function:
if h.func == y.func:
result = h.args[0].match(n + k)
if result is not None:
kspec = int(result[k])
else:
raise ValueError("'%s(%s+k)' expected, got '%s'" % (y.func, n, h))
else:
raise ValueError("'%s' expected, got '%s'" % (y.func, h.func))
else:
coeff *= h
if kspec is not None:
if kspec in h_part:
h_part[kspec] += coeff
else:
h_part[kspec] = coeff
else:
i_part += coeff
for k, coeff in h_part.iteritems():
h_part[k] = simplify(coeff)
common = S.One
for coeff in h_part.itervalues():
if coeff.is_rational_function(n):
if not coeff.is_polynomial(n):
common = lcm(common, coeff.as_numer_denom()[1], n)
else:
raise ValueError("Polynomial or rational function expected, got '%s'" % coeff)
i_numer, i_denom = i_part.as_numer_denom()
if i_denom.is_polynomial(n):
common = lcm(common, i_denom, n)
if common is not S.One:
for k, coeff in h_part.iteritems():
numer, denom = coeff.as_numer_denom()
h_part[k] = numer*exquo(common, denom, n)
i_part = i_numer*exquo(common, i_denom, n)
K_min = min(h_part.keys())
if K_min < 0:
K = abs(K_min)
H_part = {}
i_part = i_part.subs(n, n+K).expand()
common = common.subs(n, n+K).expand()
for k, coeff in h_part.iteritems():
H_part[k+K] = coeff.subs(n, n+K).expand()
else:
H_part = h_part
K_max = max(H_part.keys())
coeffs = []
for i in xrange(0, K_max+1):
if i in H_part:
coeffs.append(H_part[i])
else:
coeffs.append(S.Zero)
result = rsolve_hyper(coeffs, i_part, n, symbols=True)
if result is None:
return None
else:
solution, symbols = result
if symbols and init is not None:
equations = []
if type(init) is list:
for i in xrange(0, len(init)):
eq = solution.subs(n, i) - init[i]
equations.append(eq)
else:
for k, v in init.iteritems():
try:
i = int(k)
except TypeError:
if k.is_Function and k.func == y.func:
i = int(k.args[0])
else:
raise ValueError("Integer or term expected, got '%s'" % k)
eq = solution.subs(n, i) - v
equations.append(eq)
result = solve(equations, *symbols)
if result is None:
return None
else:
for k, v in result.iteritems():
solution = solution.subs(k, v)
return (solution.expand()) / common
def rsolve_ratio(coeffs, f, n, **hints):
"""Given linear recurrence operator L of order 'k' with polynomial
coefficients and inhomogeneous equation Ly = f, where 'f' is a
polynomial, we seek for all rational solutions over field K of
characteristic zero.
This procedure accepts only polynomials, however if you are
interested in solving recurrence with rational coefficients
then use rsolve() which will pre-process the given equation
and run this procedure with polynomial arguments.
The algorithm performs two basic steps:
(1) Compute polynomial v(n) which can be used as universal
denominator of any rational solution of equation Ly = f.
(2) Construct new linear difference equation by substitution
y(n) = u(n)/v(n) and solve it for u(n) finding all its
polynomial solutions. Return None if none were found.
Algorithm implemented here is a revised version of the original
Abramov's algorithm, developed in 1989. The new approach is much
simpler to implement and has better overall efficiency. This
method can be easily adapted to q-difference equations case.
Besides finding rational solutions alone, this functions is
an important part of Hyper algorithm were it is used to find
particular solution of inhomogeneous part of a recurrence.
For more information on the implemented algorithm refer to:
[1] S. A. Abramov, Rational solutions of linear difference
and q-difference equations with polynomial coefficients,
in: T. Levelt, ed., Proc. ISSAC '95, ACM Press, New York,
1995, 285-289
"""
f = sympify(f)
if not f.is_polynomial(n):
return None
coeffs = map(sympify, coeffs)
r = len(coeffs)-1
A, B = coeffs[r], coeffs[0]
A = A.subs(n, n-r).expand()
h = Symbol('h', dummy=True)
res = resultant(A, B.subs(n, n+h), n)
if not res.is_polynomial(h):
p, q = res.as_numer_denom()
res = exquo(p, q, h)
nni_roots = roots(res, h, filter='Z',
predicate=lambda r: r >= 0).keys()
if not nni_roots:
return rsolve_poly(coeffs, f, n, **hints)
else:
C, numers = S.One, [S.Zero]*(r+1)
for i in xrange(int(max(nni_roots)), -1, -1):
d = gcd(A, B.subs(n, n+i), n)
A = exquo(A, d, n)
B = exquo(B, d.subs(n, n-i), n)
C *= Mul(*[ d.subs(n, n-j) for j in xrange(0, i+1) ])
denoms = [ C.subs(n, n+i) for i in range(0, r+1) ]
for i in range(0, r+1):
g = gcd(coeffs[i], denoms[i], n)
numers[i] = exquo(coeffs[i], g, n)
denoms[i] = exquo(denoms[i], g, n)
for i in xrange(0, r+1):
numers[i] *= Mul(*(denoms[:i] + denoms[i+1:]))
result = rsolve_poly(numers, f * Mul(*denoms), n, **hints)
if result is not None:
if hints.get('symbols', False):
return (simplify(result[0] / C), result[1])
else:
return simplify(result / C)
else:
return None