Sage 4.1 was released on July 09, 2009. For the official, comprehensive release note, please refer to sage-4.1.txt. The following points are some of the foci of this release:

Upgrade to the Python 2.6.x series

Support for building Singular with GCC 4.4

FreeBSD support for the following packages: FreeType, gd, libgcrypt, libgpg-error, Linbox, NTL, Readline, Tachyon

Combinatorics: irreducible matrix representations of symmetric groups; and Yang-Baxter Graphs

Cryptography: Mini Advanced Encryption Standard for educational purposes

Graph theory: a backend for graph theory using Cython (c_graph); and improve accuracy of graph eigenvalues

Linear algebra: a general package for finitely generated, not-necessarily free R-modules; and multiplicative order for matrices over finite fields

Miscellaneous: optimized Sudoku solver; a decorator for declaring abstract methods; support Unicode in LaTeX cells (notebook); and optimized integer division

Number theory: improved random element generation for number field orders and ideals; support Michael Stoll’s ratpoints package; and elliptic exponential

Numerical: computing numerical values of constants using mpmath

Update/upgrade 19 packages to latest upstream releases

The following people made their first contribution in Sage 4.1:

Golam Mortuza Hossain Peter Jeremy Peter Mora Stanislav Bulygin

In this release, we closed 91 tickets and added two new components to the list of standard packages. These new spkg’s are mpmath for multiprecision floating-point arithmetic, and Ratpoints for computing rational points on hyperelliptic curves. This brings the total number of standard packages to 95. We increased doctest coverage by 0.3%, bringing the overall weighted doctest coverage score to 77.8%. A total of 216 functions were added; the total number of functions is now at 22398. For detailed information on what changed in this release, refer to trac.

Here is a summary of main features in this release, categorized under various headings:

Algebraic Geometry

Construct an elliptic curve from a plane curve of genus one (Lloyd Kilford, John Cremona ) — New function EllipticCurve_from_plane_curve() in the module sage/schemes/elliptic_curves/constructor.py to allow the construction of an elliptic curve from a smooth plane cubic with a rational point. Currently, this function uses Magma and it will not work on machines that do not have Magma installed. Assuming you have Magma installed on your computer, we can use the function EllipticCurve_from_plane_curve() to first check that the Fermat cubic is isomorphic to the curve with Cremona label “27a1”: sage: x, y, z = PolynomialRing(QQ, 3, 'xyz').gens() # optional - magma sage: C = Curve(x^3 + y^3 + z^3) # optional - magma sage: P = C(1, -1, 0) # optional - magma sage: E = EllipticCurve_from_plane_curve(C, P) # optional - magma sage: E # optional - magma Elliptic Curve defined by y^2 + y = x^3 - 7 over Rational Field sage: E.label() # optional - magma '27a1' Here is a quartic example: sage: u, v, w = PolynomialRing(QQ, 3, 'uvw').gens() # optional - magma sage: C = Curve(u^4 + u^2*v^2 - w^4) # optional - magma sage: P = C(1, 0, 1) # optional - magma sage: E = EllipticCurve_from_plane_curve(C, P) # optional - magma sage: E # optional - magma Elliptic Curve defined by y^2 = x^3 + 4*x over Rational Field sage: E.label() # optional - magma '32a1'

Basic Arithmetic

Speed-up integer division (Robert Bradshaw ) — In some cases, integer division is now up to 31% faster than previously. The following timing statistics were obtained using the machine sage.math: # BEFORE sage: a = next_prime(2**31) sage: b = Integers(a)(100) sage: %timeit a % b; 1000000 loops, best of 3: 1.12 µs per loop sage: %timeit 101 // int(5); 1000000 loops, best of 3: 215 ns per loop sage: %timeit 100 // int(-3) 1000000 loops, best of 3: 214 ns per loop sage: a = ZZ.random_element(10**50) sage: b = ZZ.random_element(10**15) sage: %timeit a.quo_rem(b) 1000000 loops, best of 3: 454 ns per loop # AFTER sage: a = next_prime(2**31) sage: b = Integers(a)(100) sage: %timeit a % b; 1000000 loops, best of 3: 1.02 µs per loop sage: %timeit 101 // int(5); 1000000 loops, best of 3: 201 ns per loop sage: %timeit 100 // int(-3) 1000000 loops, best of 3: 194 ns per loop sage: a = ZZ.random_element(10**50) sage: b = ZZ.random_element(10**15) sage: %timeit a.quo_rem(b) 1000000 loops, best of 3: 313 ns per loop

Combinatorics

Irreducible matrix representations of symmetric groups (Franco Saliola) — Support for constructing irreducible representations of the symmetric group. This is based on Alain Lascoux’s article Young representations of the symmetric group. The following types of representations are supported: Specht representations — The matrices have integer entries: sage: chi = SymmetricGroupRepresentation([3, 2]); chi Specht representation of the symmetric group corresponding to [3, 2] sage: chi([5, 4, 3, 2, 1]) [ 1 -1 0 1 0] [ 0 0 -1 0 1] [ 0 0 0 -1 1] [ 0 1 -1 -1 1] [ 0 1 0 -1 1] Young’s seminormal representation — The matrices have rational entries: sage: snorm = SymmetricGroupRepresentation([2, 1], "seminormal"); snorm Seminormal representation of the symmetric group corresponding to [2, 1] sage: snorm([1, 3, 2]) [-1/2 3/2] [ 1/2 1/2] Young’s orthogonal representation (the matrices are orthogonal) — These matrices are defined over Sage’s SymbolicRing : sage: ortho = SymmetricGroupRepresentation([3, 2], "orthogonal"); ortho Orthogonal representation of the symmetric group corresponding to [3, 2] sage: ortho([1, 3, 2, 4, 5]) [ 1 0 0 0 0] [ 0 -1/2 1/2*sqrt(3) 0 0] [ 0 1/2*sqrt(3) 1/2 0 0] [ 0 0 0 -1/2 1/2*sqrt(3)] [ 0 0 0 1/2*sqrt(3) 1/2] You can also create the CombinatorialClass of all irreducible matrix representations of a given symmetric group. Then particular representations can be created by providing partitions. For example: sage: chi = SymmetricGroupRepresentations(5); chi Specht representations of the symmetric group of order 5! over Integer Ring sage: chi([5]) # the trivial representation Specht representation of the symmetric group corresponding to [5] sage: chi([5])([2, 1, 3, 4, 5]) [1] sage: chi([1, 1, 1, 1, 1]) # the sign representation Specht representation of the symmetric group corresponding to [1, 1, 1, 1, 1] sage: chi([1, 1, 1, 1, 1])([2, 1, 3, 4, 5]) [-1] sage: chi([3, 2]) Specht representation of the symmetric group corresponding to [3, 2] sage: chi([3, 2])([5, 4, 3, 2, 1]) [ 1 -1 0 1 0] [ 0 0 -1 0 1] [ 0 0 0 -1 1] [ 0 1 -1 -1 1] [ 0 1 0 -1 1] See the documentation of SymmetricGroupRepresentation and SymmetricGroupRepresentations for more information and examples.

Yang-Baxter graphs (Franco Saliola) — Besides being used for constructing the irreducible matrix representations of the symmetric group, Yang-Baxter graphs can also be used to construct the Cayley graph of a finite group. For example: sage: def left_multiplication_by(g): ....: return lambda h : h*g ....: sage: G = AlternatingGroup(4) sage: operators = [ left_multiplication_by(gen) for gen in G.gens() ] sage: Y = YangBaxterGraph(root=G.identity(), operators=operators); Y Yang-Baxter graph with root vertex () sage: Y.plot(edge_labels=False)

Yang-Baxter graphs can also be used to construct the permutahedron: sage: from sage.combinat.yang_baxter_graph import SwapIncreasingOperator sage: operators = [SwapIncreasingOperator(i) for i in range(3)] sage: Y = YangBaxterGraph(root=(1,2,3,4), operators=operators); Y Yang-Baxter graph with root vertex (1, 2, 3, 4) sage: Y.plot()

See the documentation of YangBaxterGraph for more information and examples.

Cryptography

Mini Advanced Encryption Standard for educational purposes (Minh Van Nguyen) — New module sage/crypto/block_cipher/miniaes.py to support the Mini Advanced Encryption Standard (Mini-AES) to allow students to explore the working of a block cipher. This is a simplified variant of the Advanced Encryption Standard (AES) to be used for cryptography education. Mini-AES is described in the paper: A. C.-W. Phan. Mini advanced encryption standard (mini-AES): a testbed for cryptanalysis students. Cryptologia, 26(4):283–306, 2002. We can encrypt a plaintext using Mini-AES as follows: sage: from sage.crypto.block_cipher.miniaes import MiniAES sage: maes = MiniAES() sage: K = FiniteField(16, "x") sage: MS = MatrixSpace(K, 2, 2) sage: P = MS([K("x^3 + x"), K("x^2 + 1"), K("x^2 + x"), K("x^3 + x^2")]); P [ x^3 + x x^2 + 1] [ x^2 + x x^3 + x^2] sage: key = MS([K("x^3 + x^2"), K("x^3 + x"), K("x^3 + x^2 + x"), K("x^2 + x + 1")]); key [ x^3 + x^2 x^3 + x] [x^3 + x^2 + x x^2 + x + 1] sage: C = maes.encrypt(P, key); C [ x x^2 + x] [x^3 + x^2 + x x^3 + x] Here is the decryption process: sage: plaintxt = maes.decrypt(C, key) sage: plaintxt == P True We can also work directly with binary strings: sage: from sage.crypto.block_cipher.miniaes import MiniAES sage: maes = MiniAES() sage: bin = BinaryStrings() sage: key = bin.encoding("KE"); key 0100101101000101 sage: P = bin.encoding("Encrypt this secret message!") sage: C = maes(P, key, algorithm="encrypt") sage: plaintxt = maes(C, key, algorithm="decrypt") sage: plaintxt == P True Or work with integers such that : sage: from sage.crypto.block_cipher.miniaes import MiniAES sage: maes = MiniAES() sage: P = [n for n in xrange(16)]; P [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] sage: key = [2, 3, 11, 0]; key [2, 3, 11, 0] sage: P = maes.integer_to_binary(P) sage: key = maes.integer_to_binary(key) sage: C = maes(P, key, algorithm="encrypt") sage: plaintxt = maes(C, key, algorithm="decrypt") sage: plaintxt == P True

to support the Mini Advanced Encryption Standard (Mini-AES) to allow students to explore the working of a block cipher. This is a simplified variant of the Advanced Encryption Standard (AES) to be used for cryptography education. Mini-AES is described in the paper:

Graph Theory

Fast compiled graphs c_graph (Robert Miller) — The Python package NetworkX version 0.36 is currently the default graph implementation in Sage. The goal of fast compiled graphs, or c_graph , is to be the default implementation of graph theory in Sage. The c_graph implementation is developed using Cython, which allows graph theoretic computations to run at the speed of C. The c_graph backend is implemented in the module sage/graphs/base/c_graph.pyx . This module is called by higher-level frontends in sage/graphs/ . Where support is provided for using c_graph , graph theoretic computations is usually more efficient than using NetworkX. For example, the following timing statistics were obtained using the machine sage.math: # NetworkX 0.36 sage: time G = Graph(1000000, implementation="networkx") CPU times: user 8.74 s, sys: 0.27 s, total: 9.01 s Wall time: 9.08 s # c_graph sage: time G = Graph(1000000, implementation="c_graph") CPU times: user 0.01 s, sys: 0.14 s, total: 0.15 s Wall time: 0.19 s Here, we see an efficiency gain of up to 47x using c_graph .

(Robert Miller) — The Python package NetworkX version 0.36 is currently the default graph implementation in Sage. The goal of fast compiled graphs, or , is to be the default implementation of graph theory in Sage. The c_graph implementation is developed using Cython, which allows graph theoretic computations to run at the speed of C. The backend is implemented in the module . This module is called by higher-level frontends in . Where support is provided for using , graph theoretic computations is usually more efficient than using NetworkX. For example, the following timing statistics were obtained using the machine sage.math: Improve accuracy of graph eigenvalues (Rob Beezer) — New routines to compute eigenvalues and eigenvectors of integer matrices more precisely than before. Rather than converting adjacency matrices of graphs to computations over the real or complex fields, adjacency matrices are retained as matrices over the integers, yielding more accurate and informative results for eigenvalues, eigenvectors, and eigenspaces. Here is a comparison involving the computation of graph spectrum: # BEFORE sage: g = graphs.CycleGraph(8); g Cycle graph: Graph on 8 vertices sage: g.spectrum() [-2.0, -1.41421356237, -1.41421356237, 4.02475820828e-18, 6.70487495185e-17, 1.41421356237, 1.41421356237, 2.0] # AFTER sage: g = graphs.CycleGraph(8); g Cycle graph: Graph on 8 vertices sage: g.spectrum() [2, 1.414213562373095?, 1.414213562373095?, 0, 0, -1.414213562373095?, -1.414213562373095?, -2] Integer eigenvalues are now exact, irrational eigenvalues are more precise than previously, making multiplicities easier to determine. Similar comments apply to eigenvectors: sage: g.eigenvectors() [(2, [ (1, 1, 1, 1, 1, 1, 1, 1) ], 1), (-2, [ (1, -1, 1, -1, 1, -1, 1, -1) ], 1), (0, [ (1, 0, -1, 0, 1, 0, -1, 0), (0, 1, 0, -1, 0, 1, 0, -1) ], 2), (-1.414213562373095?, [(1, 0, -1, 1.414213562373095?, -1, 0, 1, -1.414213562373095?), (0, 1, -1.414213562373095?, 1, 0, -1, 1.414213562373095?, -1)], 2), (1.414213562373095?, [(1, 0, -1, -1.414213562373095?, -1, 0, 1, 1.414213562373095?), (0, 1, 1.414213562373095?, 1, 0, -1, -1.414213562373095?, -1)], 2)] Eigenspaces are exact, in that they can be expressed as vector spaces over number fields. When the defining polynomial has several roots, the eigenspaces are not repeated. Previously, eigenspaces were “fractured” owing to slight computational differences in identical eigenvalues. In concert with eigenvectors() , this command illuminates the structure of a graph’s eigenspaces more than purely numerical results. sage: g.eigenspaces() [ (2, Vector space of degree 8 and dimension 1 over Rational Field User basis matrix: [1 1 1 1 1 1 1 1]), (-2, Vector space of degree 8 and dimension 1 over Rational Field User basis matrix: [ 1 -1 1 -1 1 -1 1 -1]), (0, Vector space of degree 8 and dimension 2 over Rational Field User basis matrix: [ 1 0 -1 0 1 0 -1 0] [ 0 1 0 -1 0 1 0 -1]), (a3, Vector space of degree 8 and dimension 2 over Number Field in a3 with defining polynomial x^2 - 2 User basis matrix: [ 1 0 -1 -a3 -1 0 1 a3] [ 0 1 a3 1 0 -1 -a3 -1]) ] Complex eigenvalues (of digraphs) previously were missing their imaginary parts. This issue has been fixed as part of the improvement in calculating graph eigenvalues.

Graphics

Plot histogram improvement (David Joyner) — Some improvements to the plot_histogram() function of the class IndexedSequence in sage/gsl/dft.py . The default colour of the histogram is blue: sage: J = range(3) sage: A = [ZZ(i^2)+1 for i in J] sage: s = IndexedSequence(A, J) sage: s.plot_histogram()

You can now change the colour of the histogram with the argument clr : sage: s.plot_histogram(clr=(1,0,0))

and even use the argument eps to change the width of the spacing between the bars: sage: s.plot_histogram(clr=(1,0,1), eps=0.3)



Linear Algebra

Multiplicative order for matrices over finite fields (Yann Laigle-Chapuy) — New method multiplicative_order() in the class Matrix of sage/matrix/matrix0.pyx for computing the multiplicative order of a matrix. Here are some examples on using the new method multiplicative_order() : sage: A = matrix(GF(59), 3, [10,56,39,53,56,33,58,24,55]) sage: A.multiplicative_order() 580 sage: (A^580).is_one() True sage: B = matrix(GF(10007^3, 'b'), 0) sage: B.multiplicative_order() 1 sage: E = MatrixSpace(GF(11^2, 'e'), 5).random_element() sage: (E^E.multiplicative_order()).is_one() True

in the class of for computing the multiplicative order of a matrix. Here are some examples on using the new method : A general package for finitely generated not-necessarily free R-modules (William Stein, David Loeffler ) — This consists of the following new Sage modules: sage/modules/fg_pid/fgp_element.py — Elements of finitely generated modules over a principal ideal domain. Here are some examples: sage: V = span([[1/2,1,1], [3/2,2,1], [0,0,1]], ZZ) sage: W = V.span([2*V.0+4*V.1, 9*V.0+12*V.1, 4*V.2]) sage: Q = V/W sage: x = Q(V.0-V.1); x (0, 3) sage: type(x) <class 'sage.modules.fg_pid.fgp_element.FGP_Element'> sage: x is Q(x) True sage: x.parent() is Q True sage: Q Finitely generated module V/W over Integer Ring with invariants (4, 12) sage: Q.0.additive_order() 4 sage: Q.1.additive_order() 12 sage: (Q.0+Q.1).additive_order() 12 sage/modules/fg_pid/fgp_module.py — Finitely generated modules over a principal ideal domain. Currently, only the principal ideal domain of integers is supported. Here are some examples: sage: V = span([[1/2,1,1], [3/2,2,1], [0,0,1]], ZZ) sage: W = V.span([2*V.0+4*V.1, 9*V.0+12*V.1, 4*V.2]) sage: import sage.modules.fg_pid.fgp_module sage: Q = sage.modules.fg_pid.fgp_module.FGP_Module(V, W) sage: type(Q) <class 'sage.modules.fg_pid.fgp_module.FGP_Module_class'> sage: Q is sage.modules.fg_pid.fgp_module.FGP_Module(V, W, check=False) True sage: X = ZZ**2 / span([[3,0],[0,2]], ZZ) sage: X.linear_combination_of_smith_form_gens([1]) (1) sage: Q Finitely generated module V/W over Integer Ring with invariants (4, 12) sage: Q.gens() ((1, 0), (0, 1)) sage: Q.coordinate_vector(-Q.0) (-1, 0) sage: Q.coordinate_vector(-Q.0, reduce=True) (3, 0) sage: Q.cardinality() 48 sage/modules/fg_pid/fgp_morphism.py — Morphisms between finitely generated modules over a principal ideal domain. Here are some examples: sage: V = span([[1/2,1,1],[3/2,2,1],[0,0,1]],ZZ) sage: W = V.span([2*V.0+4*V.1, 9*V.0+12*V.1, 4*V.2]) sage: Q = V/W; Q Finitely generated module V/W over Integer Ring with invariants (4, 12) sage: phi = Q.hom([Q.0+3*Q.1, -Q.1]); phi Morphism from module over Integer Ring with invariants (4, 12) to module with invariants (4, 12) that sends the generators to [(1, 3), (0, 11)] sage: phi(Q.0) == Q.0 + 3*Q.1 True sage: phi(Q.1) == -Q.1 True sage: Q.hom([0, Q.1]).kernel() Finitely generated module V/W over Integer Ring with invariants (4) sage: A = Q.hom([Q.0, 0]).kernel(); A Finitely generated module V/W over Integer Ring with invariants (12) sage: Q.1 in A True sage: phi = Q.hom([Q.0-3*Q.1, Q.0+Q.1]) sage: A = phi.kernel(); A Finitely generated module V/W over Integer Ring with invariants (4) sage: phi(A) Finitely generated module V/W over Integer Ring with invariants ()



Miscellaneous

An optimized Sudoku solver (Rob Beezer, Tom Boothby) — Support two algorithms for efficiently solving a Sudoku puzzle: a backtrack algorithm and the DLX algorithm. Generally, the DLX algorithm is very fast and very consistent. The backtrack algorithm is very variable in its performance, on some occasions markedly faster than DLX but usually slower by a similar factor, with the potential to be orders of magnitude slower. The following code compares the performance between the Sudoku solver in Sage 4.0.2 and that in this release. We also compare the performance between the backtrack algorithm and the DLX algorithm. All timing statistics were obtained using the machine sage.math: # BEFORE sage: A = matrix(ZZ,9,[5,0,0, 0,8,0, 0,4,9, 0,0,0, 5,0,0, 0,3,0, 0,6,7, \ ....: 3,0,0, 0,0,1, 1,5,0, 0,0,0, 0,0,0, 0,0,0, 2,0,8, 0,0,0, \ ....: 0,0,0, 0,0,0, 0,1,8, \ ....: 7,0,0, 0,0,4, 1,5,0, 0,3,0, 0,0,2, 0,0,0, 4,9,0, 0,5,0, 0,0,3]) sage: %timeit sudoku(A); 10 loops, best of 3: 43.5 ms per loop sage: from sage.games.sudoku import solve_recursive sage: B = matrix(ZZ, 9, 9, [ [0,0,0,0,1,0,9,0,0], [8,0,0,4,0,0,0,0,0], \ ....: [2,0,0,0,0,0,0,0,0], [0,7,0,0,3,0,0,0,0], [0,0,0,0,0,0,2,0,4], \ ....: [0,0,0,0,0,0,0,5,8], [0,6,0,0,0,0,1,3,0], [7,0,0,2,0,0,0,0,0], \ ....: [0,0,0,8,0,0,0,0,0] ]) sage: %timeit solve_recursive(B, 8, 5); 1000 loops, best of 3: 325 µs per loop # AFTER sage: h = Sudoku('8..6..9.5.............2.31...7318.6.24.....73...........279.1..5...8..36..3......') sage: %timeit h.solve(algorithm='backtrack').next(); 1000 loops, best of 3: 1.12 ms per loop sage: %timeit h.solve(algorithm='dlx').next(); 1000 loops, best of 3: 1.58 ms per loop sage: # These are the first 10 puzzles in a list of "Top 95" puzzles. sage: top =['4.....8.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......',\ ....: '52...6.........7.13...........4..8..6......5...........418.........3..2...87.....',\ ....: '6.....8.3.4.7.................5.4.7.3..2.....1.6.......2.....5.....8.6......1....',\ ....: '48.3............71.2.......7.5....6....2..8.............1.76...3.....4......5....',\ ....: '....14....3....2...7..........9...3.6.1.............8.2.....1.4....5.6.....7.8...',\ ....: '......52..8.4......3...9...5.1...6..2..7........3.....6...1..........7.4.......3.',\ ....: '6.2.5.........3.4..........43...8....1....2........7..5..27...........81...6.....',\ ....: '.524.........7.1..............8.2...3.....6...9.5.....1.6.3...........897........',\ ....: '6.2.5.........4.3..........43...8....1....2........7..5..27...........81...6.....',\ ....: '.923.........8.1...........1.7.4...........658.........6.5.2...4.....7.....9.....'] sage: p = [Sudoku(top[i]) for i in xrange(10)] sage: for i in xrange(10): ....: %timeit p[i].solve(algorithm='dlx').next(); ....: %timeit p[i].solve(algorithm='backtrack').next(); ....: 100 loops, best of 3: 2.26 ms per loop 10 loops, best of 3: 223 ms per loop 100 loops, best of 3: 2.6 ms per loop 10 loops, best of 3: 21.3 ms per loop 100 loops, best of 3: 2.38 ms per loop 10 loops, best of 3: 83.5 ms per loop 1000 loops, best of 3: 1.76 ms per loop 10 loops, best of 3: 43.5 ms per loop 1000 loops, best of 3: 1.86 ms per loop 10 loops, best of 3: 316 ms per loop 1000 loops, best of 3: 1.65 ms per loop 10 loops, best of 3: 145 ms per loop 100 loops, best of 3: 1.84 ms per loop 10 loops, best of 3: 547 ms per loop 1000 loops, best of 3: 1.77 ms per loop 10 loops, best of 3: 255 ms per loop 100 loops, best of 3: 2.08 ms per loop 10 loops, best of 3: 445 ms per loop 1000 loops, best of 3: 1.67 ms per loop 10 loops, best of 3: 266 ms per loop

A decorator for declaring abstract methods (Nicolas Thiéry) — Support a decorator that can be used to declare a method that should be implemented by derived classes. This declaration should typically include documentation for the specification for this method. The purpose of the decorator is to enforce a consistent and visual syntax for such declarations. The decorator is also used by the Sage categories framework for automated tests. As an example, here we create a class with an abstract method: sage: class A(object): ....: @abstract_method ....: def my_method(self): ....: """ ....: The method :meth:`my_method` computes my_method ....: """ ....: pass ....: sage: A.my_method The current policy is that a NotImplementedError is raised when accessing the method through an instance, even before the method is called: sage: x = A() sage: x.my_method Traceback (most recent call last): ... NotImplementedError: <abstract method my_method at 0x7f53414a7410> It is also possible to mark abstract methods as optional: sage: class A(object): ....: @abstract_method(optional=True) ....: def my_method(self): ....: """ ....: The method :meth:`my_method` computes my_method ....: """ ....: pass ....: sage: A.my_method <optional abstract method my_method at 0x3b551b8> sage: x = A() sage: x.my_method NotImplemented

Notebook

Unicode in %latex cells (Peter Mora) — One can now enter Unicode characters directly in Notebook cells. Here is a screenshot illustrating this:



cells (Peter Mora) — One can now enter Unicode characters directly in Notebook cells. Here is a screenshot illustrating this: Allow \[ and \] to delimit math in %html blocks (John Palmieri) — One can now enter %html test \[ x^2 \] and the expression is typeset in math mode.

Number Theory

Improved random_element() method for number field orders and ideals (John Cremona) — The new method random_element() of the class NumberFieldIdeal in sage/rings/number_field/number_field_ideal.py returns a random element of a fractional ideal, computed as a random -linear combination of the basis. A similar method has also been implemented for the class Order in sage/rings/number_field/order.py }. Here are some examples on using this new method: sage: K.<a> = NumberField(x^3 + 2) sage: I = K.ideal(1 - a) sage: I.random_element() 2*a^2 + a + 3 sage: I.random_element(distribution="uniform") -a^2 + 2*a + 2 sage: I.random_element(-30, 30) -30*a^2 + 17*a - 11 sage: I.random_element(-30,30).parent() is K True sage: K.<a> = NumberField(x^3 + 2) sage: OK = K.ring_of_integers() sage: OK.random_element() 2*a^2 + 7*a + 2 sage: OK.random_element(distribution="uniform") -2*a^2 + a - 1 sage: K.order(a).random_element() -2*a^2 - a - 5

method for number field orders and ideals (John Cremona) — The new method of the class in returns a random element of a fractional ideal, computed as a random -linear combination of the basis. A similar method has also been implemented for the class in }. Here are some examples on using this new method: Support for Michael Stoll’s ratpoints package (Robert Miller, Michael Stoll) — Stoll’s ratpoints package is a program for finding points of bounded height on curves of the form . The library code is contained in the Cython module sage/libs/ratpoints.pyx . Here are some examples for working with ratpoints: sage: from sage.libs.ratpoints import ratpoints sage: for x,y,z in ratpoints([1..6], 200): ....: print -1*y^2 + 1*z^6 + 2*x*z^5 + 3*x^2*z^4 + 4*x^3*z^3 + 5*x^4*z^2 + 6*x^5*z ....: 0 0 0 0 0 0 0 sage: for x,y,z in ratpoints([1..5], 200): ....: print -1*y^2 + 1*z^4 + 2*x*z^3 + 3*x^2*z^2 + 4*x^3*z + 5*x^4 ....: 0 0 0 0 0 0 0 0

. The library code is contained in the Cython module . Here are some examples for working with ratpoints: Elliptic exponential (John Cremona) — New method elliptic_exponential() in the class EllipticCurve_rational_field of sage/schemes/elliptic_curves/ell_rational_field.py for computing the elliptic exponential of a complex number with respect to an elliptic curve. A similar method is also defined for the class PeriodLattice_ell in sage/schemes/elliptic_curves/period_lattice.py . Here are some examples: sage: E = EllipticCurve([1,1,1,-8,6]) sage: P = E([0,2]) sage: z = P.elliptic_logarithm() sage: E.elliptic_exponential(z) (-1.6171648557030742010940435588e-29 : 2.0000000000000000000000000000 : 1.0000000000000000000000000000) sage: z = E([0,2]).elliptic_logarithm(precision=200) sage: E.elliptic_exponential(z) (-1.6490990486332025523931769742517329237564168247111092902718e-59 : 2.0000000000000000000000000000000000000000000000000000000000 : 1.0000000000000000000000000000000000000000000000000000000000) And here are some torsion examples: sage: E = EllipticCurve('389a') sage: w1,w2 = E.period_lattice().basis() sage: E.two_division_polynomial().roots(CC,multiplicities=False) [-2.04030220028546, 0.135409240221753, 0.904892960063711] sage: [E.elliptic_exponential((a*w1+b*w2)/2)[0] for a,b in [(0,1),(1,1),(1,0)]] [-2.04030220028546, 0.135409240221753, 0.904892960063711] sage: E.division_polynomial(3).roots(CC,multiplicities=False) [-2.88288879135334, 1.39292799513138, 0.0783137314443164 - 0.492840991709879*I, 0.0783137314443164 + 0.492840991709879*I] sage: [E.elliptic_exponential((a*w1+b*w2)/3)[0] for a,b in [(0,1),(1,0),(1,1),(2,1)]] [-2.88288879135335, 1.39292799513138, 0.0783137314443165 - 0.492840991709879*I, 0.0783137314443168 + 0.492840991709879*I]

Numerical

Use mpmath to compute constants (Fredrik Johannson, Mike Hansen) — Previously the functions khinchin() , mertens() and twinprime() in sage/symbolic/constants.py were LimitedPrecisionConstant . Using mpmath, these functions now support arbitrary precision for the corresponding constants. There is now also support for the Glaisher-Kinkelin constant using mpmath. Here are some examples on using these functions with the mpmath backend. The Khinchin constant: sage: float(khinchin) 2.6854520010653062 sage: khinchin.n(digits=60) 2.68545200106530644530971483548179569382038229399446295305115 sage: khinchin._mpfr_(RealField(100)) 2.6854520010653064453097148355 sage: RealField(100)(khinchin) 2.6854520010653064453097148355 The Twin Primes constant: sage: float(twinprime) 0.66016181584686962 sage: twinprime.n(digits=60) 0.660161815846869573927812110014555778432623360284733413319448 sage: twinprime._mpfr_(RealField(100)) 0.66016181584686957392781211001 sage: RealField(100)(twinprime) 0.66016181584686957392781211001 The Mertens constant: sage: float(mertens) 0.26149721284764277 sage: mertens.n(digits=60) 0.261497212847642783755426838608695859051566648261199206192064 sage: mertens._mpfr_(RealField(100)) 0.26149721284764278375542683861 sage: RealField(100)(mertens) 0.26149721284764278375542683861 The Glaisher-Kinkelin constant: sage: float(glaisher) 1.2824271291006226 sage: glaisher.n(digits=60) 1.28242712910062263687534256886979172776768892732500119206374 sage: a = glaisher + 2 sage: parent(a) Symbolic Ring sage: glaisher._mpfr_(RealField(100)) 1.2824271291006226368753425689 sage: RealField(100)(glaisher) 1.2824271291006226368753425689

Packages

New package mpmath version 0.12 for multiprecision floating-point arithmetic (Fredrik Johannson, Mike Hansen) — The Python package mpmath is now a standard package of Sage. Functions in mpmath can be called from Sage using the library under sage/libs/mpmath , with automatic data conversion between Sage and mpmath.

, with automatic data conversion between Sage and mpmath. New package Ratpoints version 2.1.2 for computing rational points on hyperelliptic curves (Robert Miller, Michael Stoll) — The C package Ratpoints is now a standard spkg. The corresponding library file is sage/libs/ratpoints.pyx .

. Upgrade Singular to version singular-3-1-0-2-20090620 with support for compiling with GCC 4.4 (Andrzej Giniewicz, Martin Albrecht, Craig Citro).

Upgrade Sage’s Python spkg to the 2.6.x series (Mike Hansen).

Upgrade Twisted to version 8.2.0 latest upstream release (Mike Hansen).

Upgrade SCons to version 1.2.0 latest upstream release (Mike Hansen).

Update the Pynac spkg to version pynac-0.1.8.p1.spkg (Mike Hansen).

Update the IPython spkg to version ipython-0.9.1.p0.spkg (Mike Hansen).

Update the ATLAS spkg to version atlas-3.8.3.p5.spkg (David Kirkby).

Update the CVXOPT spkg to version cvxopt-0.9.p8.spkg (Gonzalo Tornaria).

Update the FreeType spkg to version freetype-2.3.5.p1.spkg (Peter Jeremy).

Update the GD spkg to version gd-2.0.35.p2.spkg (Peter Jeremy).

Update the libgcrypt spkg to version libgcrypt-1.4.3.p1.spkg (Peter Jeremy).

Update the libgpg_error spkg to version libgpg_error-1.6.p1.spkg (Peter Jeremy).

Update the linbox spkg to version linbox-1.1.6.p0.spkg (Peter Jeremy).

Update the NTL spkg to version ntl-5.4.2.p8.spkg (Peter Jeremy).

Update the Readline spkg to version readline-5.2.p7.spkg (Peter Jeremy).

Update the Tachyon spkg to version tachyon-0.98beta (Peter Jeremy).

Update the Rubik spkg to version rubiks-20070912.p9.spkg (William Stein) — This adds support for compiling Rubiks in parallel.

Update the python-gnutls spkg to version python_gnutls-1.1.4.p5.spkg (William Stein).

Update the ATLAS spkg to version atlas-3.8.3.p5.spkg (David Kirkby).

Symbolics

Symbolic arctan2() function (Karl-Dieter Crisman) — New symbolic trigonometric function arctan2() in sage/functions/trig.py . This symbolic function returns the arctangent (measured in radians) of y/x . Unlike arctan(y/x) , the signs of both x and y are considered. For example, note the difference between arctan2() and arctan() : sage: arctan2(1,-1) 3/4*pi sage: arctan(1/-1) -1/4*pi The new symbolic function arctan2() is also consistent with the implementations in Python and Maxima: sage: arctan2(1,-1) # the symbolic arctan2 3/4*pi sage: maxima.atan2(1,-1) # Maxima implementation 3*%pi/4 sage: math.atan2(1,-1) # Python implementation 2.3561944901923448 We can also compute an approximation: sage: arctan2(-.5,1).n(100) -0.46364760900080611621425623146

Rob Beezer and Franco Saliola contributed to writing this release tour. A big thank you to all the Sage bug report/patch authors who made my life as a release tour author easier through your comprehensive and concise documentation. There are too many to list here; you know who you are. A release tour can also be found on the Sage wiki.