A central facet of Sage is that it supports computation with objects in many different computer algebra systems “under one roof” using a common interface and clean programming language.

The console and interact methods of an interface do very different things. For example, using GAP as an example:

  1. gap.console(): This opens the GAP console - it transfers control to GAP. Here Sage is serving as nothing more than a convenient program launcher, similar to the Linux bash shell.
  2. gap.interact(): This is a convenient way to interact with a running GAP instance that may be “full of” Sage objects. You can import Sage objects into this GAP session (even from the interactive interface), etc.


PARI is a compact, very mature, highly optimized C program whose primary focus is number theory. There are two very distinct interfaces that you can use in Sage:

  • gp - the “G o P ARI” interpreter, and
  • pari - the PARI C library.

For example, the following are two ways of doing the same thing. They look identical, but the output is actually different, and what happens behind the scenes is drastically different.

sage: gp('znprimroot(10007)')
Mod(5, 10007)
sage: pari('znprimroot(10007)')
Mod(5, 10007)

In the first case, a separate copy of the GP interpreter is started as a server, and the string 'znprimroot(10007)' is sent to it, evaluated by GP, and the result is assigned to a variable in GP (which takes up space in the child GP processes memory that won’t be freed). Then the value of that variable is displayed. In the second case, no separate program is started, and the string 'znprimroot(10007)' is evaluated by a certain PARI C library function. The result is stored in a piece of memory on the Python heap, which is freed when the variable is no longer referenced. The objects have different types:

sage: type(gp('znprimroot(10007)'))
<class ''>
sage: type(pari('znprimroot(10007)'))
<type 'sage.libs.cypari2.gen.Gen'>

So which should you use? It depends on what you’re doing. The GP interface can do absolutely anything you could do in the usual GP/PARI command line program, since it is running that program. In particular, you can load complicated PARI programs and run them. In contrast, the PARI interface (via the C library) is much more restrictive. First, not all member functions have been implemented. Second, a lot of code, e.g., involving numerical integration, won’t work via the PARI interface. That said, the PARI interface can be significantly faster and more robust than the GP one.

(If the GP interface runs out of memory evaluating a given input line, it will silently and automatically double the stack size and retry that input line. Thus your computation won’t crash if you didn’t correctly anticipate the amount of memory that would be needed. This is a nice trick the usual GP interpreter doesn’t seem to provide. Regarding the PARI C library interface, it immediately copies each created object off of the PARI stack, hence the stack never grows. However, each object must not exceed 100MB in size, or the stack will overflow when the object is being created. This extra copying does impose a slight performance penalty.)

In summary, Sage uses the PARI C library to provide functionality similar to that provided by the GP/PARI interpreter, except with different sophisticated memory management and the Python programming language.

First we create a PARI list from a Python list.

sage: v = pari([1,2,3,4,5])
sage: v
[1, 2, 3, 4, 5]
sage: type(v)
<type 'sage.libs.cypari2.gen.Gen'>

Every PARI object is of type Gen. The PARI type of the underlying object can be obtained using the type member function.

sage: v.type()

In PARI, to create an elliptic curve we enter ellinit([1,2,3,4,5]). Sage is similar, except that ellinit is a method that can be called on any PARI object, e.g., our t_VEC \(v\).

sage: e = v.ellinit()
sage: e.type()
sage: pari(e)[:13]
[1, 2, 3, 4, 5, 9, 11, 29, 35, -183, -3429, -10351, 6128487/10351]

Now that we have an elliptic curve object, we can compute some things about it.

sage: e.elltors()
[1, [], []]
sage: e.ellglobalred()
[10351, [1, -1, 0, -1], 1, [11, 1; 941, 1], [[1, 5, 0, 1], [1, 5, 0, 1]]]
sage: f = e.ellchangecurve([1,-1,0,-1])
sage: f[:5]
[1, -1, 0, 4, 3]


Sage comes with GAP for computational discrete mathematics, especially group theory.

Here’s an example of GAP’s IdGroup function, which uses the optional small groups database that has to be installed separately, as explained below.

sage: G = gap('Group((1,2,3)(4,5), (3,4))')
sage: G
Group( [ (1,2,3)(4,5), (3,4) ] )
sage: G.Center()
Group( () )
sage: G.IdGroup()    # optional - database_gap
[ 120, 34 ]
sage: G.Order()

We can do the same computation in Sage without explicitly invoking the GAP interface as follows:

sage: G = PermutationGroup([[(1,2,3),(4,5)],[(3,4)]])
Subgroup of (Permutation Group with generators [(3,4), (1,2,3)(4,5)]) generated by [()]
sage: G.group_id()     # optional - database_gap
[120, 34]
sage: n = G.order(); n

For some GAP functionality, you should install two optional Sage packages. This can be done with the command:

sage -i gap_packages database_gap


Singular provides a massive and mature library for Gröbner bases, multivariate polynomial gcds, bases of Riemann-Roch spaces of a plane curve, and factorizations, among other things. We illustrate multivariate polynomial factorization using the Sage interface to Singular (do not type the ....:):

sage: R1 = singular.ring(0, '(x,y)', 'dp')
sage: R1
polynomial ring, over a field, global ordering
//   characteristic : 0
//   number of vars : 2
//        block   1 : ordering dp
//                  : names    x y
//        block   2 : ordering C
sage: f = singular('9*y^8 - 9*x^2*y^7 - 18*x^3*y^6 - 18*x^5*y^6 +'
....:     '9*x^6*y^4 + 18*x^7*y^5 + 36*x^8*y^4 + 9*x^10*y^4 - 18*x^11*y^2 -'
....:     '9*x^12*y^3 - 18*x^13*y^2 + 9*x^16')

Now that we have defined \(f\), we print it and factor.

sage: f
sage: f.parent()
sage: F = f.factorize(); F
sage: F[1][2]

As with the GAP example in GAP, we can compute the above factorization without explicitly using the Singular interface (however, behind the scenes Sage uses the Singular interface for the actual computation). Do not type the ....::

sage: x, y = QQ['x, y'].gens()
sage: f = (9*y^8 - 9*x^2*y^7 - 18*x^3*y^6 - 18*x^5*y^6 + 9*x^6*y^4
....:     + 18*x^7*y^5 + 36*x^8*y^4 + 9*x^10*y^4 - 18*x^11*y^2 - 9*x^12*y^3
....:     - 18*x^13*y^2 + 9*x^16)
sage: factor(f)
(9) * (-x^5 + y^2)^2 * (x^6 - 2*x^3*y^2 - x^2*y^3 + y^4)


Maxima is included with Sage, as well as a Lisp implementation. The gnuplot package (which Maxima uses by default for plotting) is distributed as a Sage optional package. Among other things, Maxima does symbolic manipulation. Maxima can integrate and differentiate functions symbolically, solve 1st order ODEs, most linear 2nd order ODEs, and has implemented the Laplace transform method for linear ODEs of any degree. Maxima also knows about a wide range of special functions, has plotting capabilities via gnuplot, and has methods to solve and manipulate matrices (such as row reduction, eigenvalues and eigenvectors), and polynomial equations.

We illustrate the Sage/Maxima interface by constructing the matrix whose \(i,j\) entry is \(i/j\), for \(i,j=1,\ldots,4\).

sage: f = maxima.eval('ij_entry[i,j] := i/j')
sage: A = maxima('genmatrix(ij_entry,4,4)'); A
sage: A.determinant()
sage: A.echelon()
sage: A.eigenvalues()
sage: A.eigenvectors()

Here’s another example:

sage: A = maxima("matrix ([1, 0, 0], [1, -1, 0], [1, 3, -2])")
sage: eigA = A.eigenvectors()
sage: V = VectorSpace(QQ,3)
sage: eigA
sage: v1 = V(sage_eval(repr(eigA[1][0][0]))); lambda1 = eigA[0][0][0]
sage: v2 = V(sage_eval(repr(eigA[1][1][0]))); lambda2 = eigA[0][0][1]
sage: v3 = V(sage_eval(repr(eigA[1][2][0]))); lambda3 = eigA[0][0][2]

sage: M = MatrixSpace(QQ,3,3)
sage: AA = M([[1,0,0],[1, - 1,0],[1,3, - 2]])
sage: b1 = v1.base_ring()
sage: AA*v1 == b1(lambda1)*v1
sage: b2 = v2.base_ring()
sage: AA*v2 == b2(lambda2)*v2
sage: b3 = v3.base_ring()
sage: AA*v3 == b3(lambda3)*v3

Finally, we give an example of using Sage to plot using openmath. Many of these were modified from the Maxima reference manual.

A 2D plot of several functions (do not type the ....:):

sage: maxima.plot2d('[cos(7*x),cos(23*x)^4,sin(13*x)^3]','[x,0,1]',  # not tested
....:     '[plot_format,openmath]')

A “live” 3D plot which you can move with your mouse (do not type the ....:):

sage: maxima.plot3d ("2^(-u^2 + v^2)", "[u, -3, 3]", "[v, -2, 2]",  # not tested
....:     '[plot_format, openmath]')
sage: maxima.plot3d("atan(-x^2 + y^3/4)", "[x, -4, 4]", "[y, -4, 4]",  # not tested
....:     "[grid, 50, 50]",'[plot_format, openmath]')

The next plot is the famous Möbius strip (do not type the ....:):

sage: maxima.plot3d("[cos(x)*(3 + y*cos(x/2)), sin(x)*(3 + y*cos(x/2)), y*sin(x/2)]",  # not tested
....:     "[x, -4, 4]", "[y, -4, 4]", '[plot_format, openmath]')

The next plot is the famous Klein bottle (do not type the ....:):

sage: maxima("expr_1: 5*cos(x)*(cos(x/2)*cos(y) + sin(x/2)*sin(2*y)+ 3.0) - 10.0")
sage: maxima("expr_2: -5*sin(x)*(cos(x/2)*cos(y) + sin(x/2)*sin(2*y)+ 3.0)")
sage: maxima("expr_3: 5*(-sin(x/2)*cos(y) + cos(x/2)*sin(2*y))")
sage: maxima.plot3d ("[expr_1, expr_2, expr_3]", "[x, -%pi, %pi]",  # not tested
....:     "[y, -%pi, %pi]", "['grid, 40, 40]", '[plot_format, openmath]')