# Solvers¶

The solvers module in SymPy implements methods for solving equations.

## Algebraic equations¶

Use solve() to solve algebraic equations. We suppose all equations are equaled to 0, so solving x**2 == 1 translates into the following code:

>>> from sympy.solvers import solve
>>> from sympy import Symbol
>>> x = Symbol('x')
>>> solve(x**2 - 1, x)
[-1, 1]


The first argument for solve() is an equation (equaled to zero) and the second argument is the symbol that we want to solve the equation for.

sympy.solvers.solvers.solve(f, *symbols, **flags)[source]

Algebraically solves equations and systems of equations.

Currently supported are:
• univariate polynomial,
• transcendental
• piecewise combinations of the above
• systems of linear and polynomial equations
• sytems containing relational expressions.

Input is formed as:

• f
• a single Expr or Poly that must be zero,
• an Equality
• a Relational expression or boolean
• iterable of one or more of the above
• symbols (Symbol, Function or Derivative) specified as
• none given (all free symbols will be used)
• single symbol
• denested list of symbols e.g. solve(f, x, y)
• ordered iterable of symbols e.g. solve(f, [x, y])
• flags
‘dict’=True (default is False)

return list (perhaps empty) of solution mappings

‘set’=True (default is False)

return list of symbols and set of tuple(s) of solution(s)

‘exclude=[] (default)’

don’t try to solve for any of the free symbols in exclude; if expressions are given, the free symbols in them will be extracted automatically.

‘check=True (default)’

If False, don’t do any testing of solutions. This can be useful if one wants to include solutions that make any denominator zero.

‘numerical=True (default)’

do a fast numerical check if f has only one symbol.

‘minimal=True (default is False)’

a very fast, minimal testing.

‘warning=True (default is False)’

print a warning if checksol() could not conclude.

‘simplify=True (default)’

simplify all but cubic and quartic solutions before returning them and (if check is not False) use the general simplify function on the solutions and the expression obtained when they are substituted into the function which should be zero

‘force=True (default is False)’

make positive all symbols without assumptions regarding sign.

‘rational=True (default)’

recast Floats as Rational; if this option is not used, the system containing floats may fail to solve because of issues with polys. If rational=None, Floats will be recast as rationals but the answer will be recast as Floats. If the flag is False then nothing will be done to the Floats.

‘manual=True (default is False)’

do not use the polys/matrix method to solve a system of equations, solve them one at a time as you might “manually”.

‘implicit=True (default is False)’

allows solve to return a solution for a pattern in terms of other functions that contain that pattern; this is only needed if the pattern is inside of some invertible function like cos, exp, ....

Notes

assumptions aren’t checked when $$solve()$$ input involves relationals or bools.

When the solutions are checked, those that make any denominator zero are automatically excluded. If you do not want to exclude such solutions then use the check=False option:

>>> from sympy import sin, limit
>>> solve(sin(x)/x)
[]


If check=False then a solution to the numerator being zero is found: x = 0. In this case, this is a spurious solution since sin(x)/x has the well known limit (without dicontinuity) of 1 at x = 0:

>>> solve(sin(x)/x, check=False)
[0]


In the following case, however, the limit exists and is equal to the the value of x = 0 that is excluded when check=True:

>>> eq = x**2*(1/x - z**2/x)
>>> solve(eq, x)
[]
>>> solve(eq, x, check=False)
[0]
>>> limit(eq, x, 0, '-')
0
>>> limit(eq, x, 0, '+')
0


Examples

The output varies according to the input and can be seen by example:

>>> from sympy import solve, Poly, Eq, Function, exp
>>> from sympy.abc import x, y, z, a, b
>>> f = Function('f')

• boolean or univariate Relational

>>> solve(x < 3)
And(im(x) == 0, re(x) < 3)

• to always get a list of solution mappings, use flag dict=True

>>> solve(x - 3, dict=True)
[{x: 3}]
>>> solve([x - 3, y - 1], dict=True)
[{x: 3, y: 1}]

• to get a list of symbols and set of solution(s) use flag set=True

>>> solve([x**2 - 3, y - 1], set=True)
([x, y], set([(-sqrt(3), 1), (sqrt(3), 1)]))

• single expression and single symbol that is in the expression

>>> solve(x - y, x)
[y]
>>> solve(x - 3, x)
[3]
>>> solve(Eq(x, 3), x)
[3]
>>> solve(Poly(x - 3), x)
[3]
>>> set(solve(x**2 - y**2, x))
set([-y, y])
>>> set(solve(x**4 - 1, x))
set([-1, 1, -I, I])

• single expression with no symbol that is in the expression

>>> solve(3, x)
[]
>>> solve(x - 3, y)
[]

• single expression with no symbol given

In this case, all free symbols will be selected as potential symbols to solve for. If the equation is univariate then a list of solutionsis returned; otherwise – as is the case when symbols are given as an iterable of length > 1 – a list of mappings will be returned.

>>> solve(x - 3)
[3]
>>> solve(x**2 - y**2)
[{x: -y}, {x: y}]
>>> solve(z**2*x**2 - z**2*y**2)
[{x: -y}, {x: y}]
>>> solve(z**2*x - z**2*y**2)
[{x: y**2}]

• when a Function or Derivative is given as a symbol, it is isolated algebraically and an implicit solution may be obtained; to obtain the solution for a function within a derivative, use dsolve.

>>> solve(f(x) - x, f(x))
[x]
>>> solve(f(x).diff(x) - f(x) - x, f(x).diff(x))
[x + f(x)]
>>> solve(f(x).diff(x) - f(x) - x, f(x))
[-x + Derivative(f(x), x)]
>>> set(solve(x + exp(x)**2, exp(x)))
set([-sqrt(-x), sqrt(-x)])

• To solve for a symbol implicitly, use ‘implicit=True’:

>>> solve(x + exp(x), x)
[-LambertW(1)]
>>> solve(x + exp(x), x, implicit=True)
[-exp(x)]

• single expression and more than 1 symbol

• when there is a linear solution

>>> solve(x - y**2, x, y)
[{x: y**2}]
>>> solve(x**2 - y, x, y)
[{y: x**2}]

• when undetermined coefficients are identified

• that are linear

>>> solve((a + b)*x - b + 2, a, b)
{a: -2, b: 2}

• that are nonlinear

>>> set(solve((a + b)*x - b**2 + 2, a, b))
set([(-sqrt(2), sqrt(2)), (sqrt(2), -sqrt(2))])

• if there is no linear solution then the first successful attempt for a nonlinear solution will be returned

>>> solve(x**2 - y**2, x, y)
[{x: -y}, {x: y}]
>>> solve(x**2 - y**2/exp(x), x, y)
[{x: 2*LambertW(y/2)}]
>>> solve(x**2 - y**2/exp(x), y, x)
[{y: -x*exp(x/2)}, {y: x*exp(x/2)}]

• iterable of one or more of the above

• involving relationals or bools

>>> solve([x < 3, x - 2])
And(im(x) == 0, re(x) == 2)
>>> solve([x > 3, x - 2])
False

• when the system is linear

• with a solution

>>> solve([x - 3], x)
{x: 3}
>>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y)
{x: -3, y: 1}
>>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y, z)
{x: -3, y: 1}
>>> solve((x + 5*y - 2, -3*x + 6*y - z), z, x, y)
{x: -5*y + 2, z: 21*y - 6}

• without a solution

>>> solve([x + 3, x - 3])
[]

• when the system is not linear

>>> set(solve([x**2 + y -2, y**2 - 4], x, y))
set([(-2, -2), (0, 2), (2, -2)])

• if no symbols are given, all free symbols will be selected and a list of mappings returned

>>> solve([x - 2, x**2 + y])
[{x: 2, y: -4}]
>>> solve([x - 2, x**2 + f(x)], set([f(x), x]))
[{x: 2, f(x): -4}]

• if any equation doesn’t depend on the symbol(s) given it will be eliminated from the equation set and an answer may be given implicitly in terms of variables that were not of interest

>>> solve([x - y, y - 3], x)
{x: y}

sympy.solvers.solvers.solve_linear(lhs, rhs=0, symbols=[], exclude=[])[source]

Return a tuple containing derived from f = lhs - rhs that is either:

(numerator, denominator) of f

If this comes back as (0, 1) it means that f is independent of the symbols in symbols, e.g:

y*cos(x)**2 + y*sin(x)**2 - y = y*(0) = 0
cos(x)**2 + sin(x)**2 = 1


If it comes back as (0, 0) there is no solution to the equation amongst the symbols given.

If the numerator is not zero then the function is guaranteed to be dependent on a symbol in symbols.

or

(symbol, solution) where symbol appears linearly in the numerator of f, is in symbols (if given) and is not in exclude (if given).

No simplification is done to f other than and mul=True expansion, so the solution will correspond strictly to a unique solution.

Examples

>>> from sympy.solvers.solvers import solve_linear
>>> from sympy.abc import x, y, z


These are linear in x and 1/x:

>>> solve_linear(x + y**2)
(x, -y**2)
>>> solve_linear(1/x - y**2)
(x, y**(-2))


When not linear in x or y then the numerator and denominator are returned.

>>> solve_linear(x**2/y**2 - 3)
(x**2 - 3*y**2, y**2)


If the numerator is a symbol then (0, 0) is returned if the solution for that symbol would have set any denominator to 0:

>>> solve_linear(1/(1/x - 2))
(0, 0)
>>> 1/(1/x) # to SymPy, this looks like x ...
x
>>> solve_linear(1/(1/x)) # so a solution is given
(x, 0)


If x is allowed to cancel, then this appears linear, but this sort of cancellation is not done so the solution will always satisfy the original expression without causing a division by zero error.

>>> solve_linear(x**2*(1/x - z**2/x))
(x**2*(-z**2 + 1), x)


You can give a list of what you prefer for x candidates:

>>> solve_linear(x + y + z, symbols=[y])
(y, -x - z)


You can also indicate what variables you don’t want to consider:

>>> solve_linear(x + y + z, exclude=[x, z])
(y, -x - z)


If only x was excluded then a solution for y or z might be obtained.

sympy.solvers.solvers.solve_linear_system(system, *symbols, **flags)[source]

Solve system of N linear equations with M variables, which means both Cramer and over defined systems are supported. The possible number of solutions is zero, one or infinite. Respectively, this procedure will return None or dictionary with solutions. In the case of over-defined systems all arbitrary parameters are skipped. This may cause situation in which an empty dictionary is returned. In this case it means all symbols can be assigned arbitrary values.

Input to this functions is a Nx(M+1) matrix, which means it has to be in augmented form. If you prefer to enter N equations and M unknowns then use $$solve(Neqs, *Msymbols)$$ instead. Note: a local copy of the matrix is made by this routine so the matrix that is passed will not be modified.

The algorithm used here is fraction-free Gaussian elimination, which results, after elimination, in an upper-triangular matrix. Then solutions are found using back-substitution. This approach is more efficient and compact than the Gauss-Jordan method.

>>> from sympy import Matrix, solve_linear_system
>>> from sympy.abc import x, y


Solve the following system:

   x + 4 y ==  2
-2 x +   y == 14
>>> system = Matrix(( (1, 4, 2), (-2, 1, 14)))
>>> solve_linear_system(system, x, y)
{x: -6, y: 2}

sympy.solvers.solvers.solve_linear_system_LU(matrix, syms)[source]

Solves the augmented matrix system using LUsolve and returns a dictionary in which solutions are keyed to the symbols of syms as ordered.

The matrix must be invertible.

sympy.matrices.LUsolve

Examples

>>> from sympy import Matrix
>>> from sympy.abc import x, y, z
>>> from sympy.solvers.solvers import solve_linear_system_LU

>>> solve_linear_system_LU(Matrix([
... [1, 2, 0, 1],
... [3, 2, 2, 1],
... [2, 0, 0, 1]]), [x, y, z])
{x: 1/2, y: 1/4, z: -1/2}

sympy.solvers.solvers.solve_undetermined_coeffs(equ, coeffs, sym, **flags)[source]

Solve equation of a type p(x; a_1, ..., a_k) == q(x) where both p, q are univariate polynomials and f depends on k parameters. The result of this functions is a dictionary with symbolic values of those parameters with respect to coefficients in q.

This functions accepts both Equations class instances and ordinary SymPy expressions. Specification of parameters and variable is obligatory for efficiency and simplicity reason.

>>> from sympy import Eq
>>> from sympy.abc import a, b, c, x
>>> from sympy.solvers import solve_undetermined_coeffs

>>> solve_undetermined_coeffs(Eq(2*a*x + a+b, x), [a, b], x)
{a: 1/2, b: -1/2}

>>> solve_undetermined_coeffs(Eq(a*c*x + a+b, x), [a, b], x)
{a: 1/c, b: -1/c}

sympy.solvers.solvers.tsolve(eq, sym)[source]
sympy.solvers.solvers.nsolve(*args, **kwargs)[source]

Solve a nonlinear equation system numerically:

nsolve(f, [args,] x0, modules=['mpmath'], **kwargs)

f is a vector function of symbolic expressions representing the system. args are the variables. If there is only one variable, this argument can be omitted. x0 is a starting vector close to a solution.

Use the modules keyword to specify which modules should be used to evaluate the function and the Jacobian matrix. Make sure to use a module that supports matrices. For more information on the syntax, please see the docstring of lambdify.

Overdetermined systems are supported.

>>> from sympy import Symbol, nsolve
>>> import sympy
>>> sympy.mpmath.mp.dps = 15
>>> x1 = Symbol('x1')
>>> x2 = Symbol('x2')
>>> f1 = 3 * x1**2 - 2 * x2**2 - 1
>>> f2 = x1**2 - 2 * x1 + x2**2 + 2 * x2 - 8
>>> print(nsolve((f1, f2), (x1, x2), (-1, 1)))
[-1.19287309935246]
[ 1.27844411169911]


For one-dimensional functions the syntax is simplified:

>>> from sympy import sin, nsolve
>>> from sympy.abc import x
>>> nsolve(sin(x), x, 2)
3.14159265358979
>>> nsolve(sin(x), 2)
3.14159265358979


mpmath.findroot is used, you can find there more extensive documentation, especially concerning keyword parameters and available solvers.

sympy.solvers.solvers.check_assumptions(expr, **assumptions)[source]

Checks whether expression $$expr$$ satisfies all assumptions.

$$assumptions$$ is a dict of assumptions: {‘assumption’: True|False, ...}.

Examples

>>> from sympy import Symbol, pi, I, exp
>>> from sympy.solvers.solvers import check_assumptions

>>> check_assumptions(-5, integer=True)
True
>>> check_assumptions(pi, real=True, integer=False)
True
>>> check_assumptions(pi, real=True, negative=True)
False
>>> check_assumptions(exp(I*pi/7), real=False)
True

>>> x = Symbol('x', real=True, positive=True)
>>> check_assumptions(2*x + 1, real=True, positive=True)
True
>>> check_assumptions(-2*x - 5, real=True, positive=True)
False


$$None$$ is returned if check_assumptions() could not conclude.

>>> check_assumptions(2*x - 1, real=True, positive=True)
>>> z = Symbol('z')
>>> check_assumptions(z, real=True)

sympy.solvers.solvers.checksol(f, symbol, sol=None, **flags)[source]

Checks whether sol is a solution of equation f == 0.

Input can be either a single symbol and corresponding value or a dictionary of symbols and values. f can be a single equation or an iterable of equations. A solution must satisfy all equations in f to be considered valid; if a solution does not satisfy any equation, False is returned; if one or more checks are inconclusive (and none are False) then None is returned.

Examples

>>> from sympy import symbols
>>> from sympy.solvers import checksol
>>> x, y = symbols('x,y')
>>> checksol(x**4-1, x, 1)
True
>>> checksol(x**4-1, x, 0)
False
>>> checksol(x**2 + y**2 - 5**2, {x:3, y: 4})
True


None is returned if checksol() could not conclude.

flags:
‘numerical=True (default)’
do a fast numerical check if f has only one symbol.
‘minimal=True (default is False)’
a very fast, minimal testing.
‘warn=True (default is False)’
print a warning if checksol() could not conclude.
‘simplify=True (default)’
simplify solution before substituting into function and simplify the function before trying specific simplifications
‘force=True (default is False)’
make positive all symbols without assumptions regarding sign.

See ODE.

## Partial Differential Equations (PDEs)¶

sympy.solvers.pde.pde_separate(eq, fun, sep, strategy='mul')[source]

Separate variables in partial differential equation either by additive or multiplicative separation approach. It tries to rewrite an equation so that one of the specified variables occurs on a different side of the equation than the others.

Parameters: eq – Partial differential equation fun – Original function F(x, y, z) sep – List of separated functions [X(x), u(y, z)] strategy – Separation strategy. You can choose between additive separation (‘add’) and multiplicative separation (‘mul’) which is default.

Examples

>>> from sympy import E, Eq, Function, pde_separate, Derivative as D
>>> from sympy.abc import x, t
>>> u, X, T = list(map(Function, 'uXT'))

>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='add')
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]

>>> eq = Eq(D(u(x, t), x, 2), D(u(x, t), t, 2))
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='mul')
[Derivative(X(x), x, x)/X(x), Derivative(T(t), t, t)/T(t)]


Helper function for searching additive separable solutions.

Consider an equation of two independent variables x, y and a dependent variable w, we look for the product of two functions depending on different arguments:

$$w(x, y, z) = X(x) + y(y, z)$$

Examples

>>> from sympy import E, Eq, Function, pde_separate_add, Derivative as D
>>> from sympy.abc import x, t
>>> u, X, T = list(map(Function, 'uXT'))

>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
>>> pde_separate_add(eq, u(x, t), [X(x), T(t)])
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]

sympy.solvers.pde.pde_separate_mul(eq, fun, sep)[source]

Helper function for searching multiplicative separable solutions.

Consider an equation of two independent variables x, y and a dependent variable w, we look for the product of two functions depending on different arguments:

$$w(x, y, z) = X(x)*u(y, z)$$

Examples

>>> from sympy import Function, Eq, pde_separate_mul, Derivative as D
>>> from sympy.abc import x, y
>>> u, X, Y = list(map(Function, 'uXY'))

>>> eq = Eq(D(u(x, y), x, 2), D(u(x, y), y, 2))
>>> pde_separate_mul(eq, u(x, y), [X(x), Y(y)])
[Derivative(X(x), x, x)/X(x), Derivative(Y(y), y, y)/Y(y)]


## Recurrence Equtions¶

sympy.solvers.recurr.rsolve(f, y, init=None)[source]

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))
2**n*C0 + C1*n!

>>> rsolve(f, y(n), { y(0):0, y(1):3 })
3*2**n - 3*n!

sympy.solvers.recurr.rsolve_poly(coeffs, f, n, **hints)[source]

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 polynomial solutions over field K of characteristic zero.

The algorithm performs two basic steps:

1. Compute degree N of the general polynomial solution.
2. Find all polynomials of degree N or less of Ly = f.

There are two methods for computing the polynomial solutions. If the degree bound is relatively small, i.e. it’s smaller than or equal to the order of the recurrence, then naive method of undetermined coefficients is being used. This gives system of algebraic equations with N+1 unknowns.

In the other case, the algorithm performs transformation of the initial equation to an equivalent one, for which the system of algebraic equations has only ‘r’ indeterminates. This method is quite sophisticated (in comparison with the naive one) and was invented together by Abramov, Bronstein and Petkovsek.

It is possible to generalize the algorithm implemented here to the case of linear q-difference and differential equations.

Lets say that we would like to compute m-th Bernoulli polynomial up to a constant. For this we can use b(n+1) - b(n) == m*n**(m-1) recurrence, which has solution b(n) = B_m + C. For example:

>>> from sympy import Symbol, rsolve_poly
>>> n = Symbol('n', integer=True)

>>> rsolve_poly([-1, 1], 4*n**3, n)
C0 + n**4 - 2*n**3 + n**2


For more information on implemented algorithms refer to:

[1] S. A. Abramov, M. Bronstein and M. Petkovsek, On polynomial
solutions of linear operator equations, in: T. Levelt, ed., Proc. ISSAC ‘95, ACM Press, New York, 1995, 290-296.
[2] M. Petkovsek, Hypergeometric solutions of linear recurrences
with polynomial coefficients, J. Symbolic Computation, 14 (1992), 243-264.

[3] M. Petkovsek, H. S. Wilf, D. Zeilberger, A = B, 1996.

sympy.solvers.recurr.rsolve_ratio(coeffs, f, n, **hints)[source]

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

Examples

>>> from sympy.abc import x
>>> from sympy.solvers.recurr import rsolve_ratio
>>> rsolve_ratio([ - 2*x**3 + x**2 + 2*x - 1, 2*x**3 + x**2 - 6*x,
... - 2*x**3 - 11*x**2 - 18*x - 9, 2*x**3 + 13*x**2 + 22*x + 8], 0, x)
C2*(2*x - 3)/(2*(x**2 - 1))

sympy.solvers.recurr.rsolve_hyper(coeffs, f, n, **hints)[source]

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.

Examples

>>> from sympy.solvers import rsolve_hyper
>>> from sympy.abc import x

>>> rsolve_hyper([-1, -1, 1], 0, x)
C0*(1/2 + sqrt(5)/2)**x + C1*(-sqrt(5)/2 + 1/2)**x

>>> rsolve_hyper([-1, 1], 1+x, x)
C0 + x*(x + 1)/2


## Systems of Polynomial Equations¶

sympy.solvers.polysys.solve_poly_system(seq, *gens, **args)[source]

Solve a system of polynomial equations.

Examples

>>> from sympy import solve_poly_system
>>> from sympy.abc import x, y

>>> solve_poly_system([x*y - 2*y, 2*y**2 - x**2], x, y)
[(0, 0), (2, -sqrt(2)), (2, sqrt(2))]

sympy.solvers.polysys.solve_triangulated(polys, *gens, **args)[source]

Solve a polynomial system using Gianni-Kalkbrenner algorithm.

The algorithm proceeds by computing one Groebner basis in the ground domain and then by iteratively computing polynomial factorizations in appropriately constructed algebraic extensions of the ground domain.

References

1. Patrizia Gianni, Teo Mora, Algebraic Solution of System of Polynomial Equations using Groebner Bases, AAECC-5 on Applied Algebra, Algebraic Algorithms and Error-Correcting Codes, LNCS 356 247–257, 1989

Examples

>>> from sympy.solvers.polysys import solve_triangulated
>>> from sympy.abc import x, y, z

>>> F = [x**2 + y + z - 1, x + y**2 + z - 1, x + y + z**2 - 1]

>>> solve_triangulated(F, x, y, z)
[(0, 0, 1), (0, 1, 0), (1, 0, 0)]