Differences between revisions 3 and 8 (spanning 5 versions)
⇤ ← Revision 3 as of 2009-06-14 15:19:00 →
Size: 1886
Editor: Minh Nguyen
Comment: Reminders to showcase features
← Revision 8 as of 2009-06-21 16:01:48 → ⇥
Size: 6731
Editor: Minh Nguyen
Comment: Summarize #6218
Deletions are marked like this. Additions are marked like this.
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 * FIXME: summarize #5845

 * FIXME: summarize #6229

 * FIXME: summarize #6250		  * Correct precision bound in {{{hilbert_class_polynomial()}}} and miscellaneous new functions (John Cremona) -- The two new functions are {{{elliptic_j()}}} in {{{sage/functions/special.py}}}, and {{{is_primitive()}}} in the class {{{BinaryQF}}} of {{{sage/quadratic_forms/binary_qf.py}}}. The function {{{elliptic_j(z)}}} returns the elliptic modular {{{j}}}-function evaluated at {{{z}}}. The function {{{is_primitive()}}} determines whether the binary quadratic form {{{ax^2 + bxy + cy^2}}} satisfies {{{gcd(a,b,c) = 1}}}, i.e. that it is primitive. Here are some examples on using these new functions:
 {{{
sage: elliptic_j(CC(i))
1728.00000000000
sage: elliptic_j(sqrt(-2.0))
8000.00000000000
sage: Q = BinaryQF([6,3,9])
sage: Q.is_primitive()
False
sage: Q = BinaryQF([1,1,1])
sage: Q.is_primitive()
True
 }}}


 * Efficient Lagrange interpolation polynomial (Yann Laigle-Chapuy) -- Calculating the Lagrange interpolation polynomial of a set of points is now up to 48% faster than previously. The following timing statistics were obtained using the machine sage.math:
 {{{
# BEFORE

sage: R = PolynomialRing(QQ, 'x')
sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
1000 loops, best of 3: 824 µs per loop
sage: R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
-23/84*x^3 - 11/84*x^2 + 13/7*x + 1
sage: R = PolynomialRing(GF(2**3,'a'), 'x')
sage: a = R.base_ring().gen()
sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])")
625 loops, best of 3: 111 µs per loop
sage: R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])
a^2*x^2 + a^2*x + a^2


# AFTER

sage: R = PolynomialRing(QQ, 'x')
sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
1000 loops, best of 3: 425 µs per loop
sage: R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
-23/84*x^3 - 11/84*x^2 + 13/7*x + 1
sage: R = PolynomialRing(GF(2**3,'a'), 'x')
sage: a = R.base_ring().gen()
sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])")
625 loops, best of 3: 86.4 µs per loop
sage: R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])
a^2*x^2 + a^2*x + a^2
 }}}


 * Deprecate the method {{{__len__()}}} for a matrix group (Nicolas Thiery) -- The method {{{__len__()}}} of the class {{{MatrixGroup_gap}}} in {{{sage/groups/matrix_gps/matrix_group.py}}} is now deprecated and will be removed in a future release. To get the number of elements in a matrix group, users are advised to use the method {{{cardinality()}}} instead. The method {{{order()}}} is essentially the same as {{{cardinality()}}}, so {{{order()}}} will be deprecated in a future release.
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 * FIXME: summarize #6218  * Optimize hyperelliptic curve arithmetic (Nick Alexander) -- Arithmetics with hyperelliptic curves can be up to 6x faster than previously. The following timing statistics were obtained using the maching sage.math:
 {{{
#BEFORE

sage: F = GF(next_prime(10^30))
sage: x = F['x'].gen()
sage: f = x^7 + x^2 + 1
sage: H = HyperellipticCurve(f, 2*x)
sage: J = H.jacobian()(F)
verbose 0 (902: multi_polynomial_ideal.py, dimension) Warning: falling back to very slow toy implementation.
sage: Q = J(H.lift_x(F(1)))
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.65 s, sys: 0.02 s, total: 0.67 s
Wall time: 0.68 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 1.08 s, sys: 0.00 s, total: 1.08 s
Wall time: 1.08 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.72 s, sys: 0.02 s, total: 0.74 s
Wall time: 0.74 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.67 s, sys: 0.00 s, total: 0.67 s
Wall time: 0.67 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.66 s, sys: 0.00 s, total: 0.66 s
Wall time: 0.66 s


# AFTER

sage: F = GF(next_prime(10^30))
sage: x = F['x'].gen()
sage: f = x^7 + x^2 + 1
sage: H = HyperellipticCurve(f, 2*x)
sage: J = H.jacobian()(F)
verbose 0 (919: multi_polynomial_ideal.py, dimension) Warning: falling back to very slow toy implementation.
sage: Q = J(H.lift_x(F(1)))
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.14 s, sys: 0.01 s, total: 0.15 s
Wall time: 0.15 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.10 s, sys: 0.00 s, total: 0.10 s
Wall time: 0.10 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s
Wall time: 0.10 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.09 s, sys: 0.01 s, total: 0.10 s
Wall time: 0.10 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.10 s, sys: 0.00 s, total: 0.10 s
Wall time: 0.11 s
 }}}
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 * FIXME: summarize #6234  * FIXME: summarize #6170
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 * FIXME: summarize #6123
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 * FIXME: summarize #2256
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 * FIXME: summarize #6046
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 * Upgrade [[http://www.flintlib.org|FLINT]] to version 1.3.0 latest upstream release (Nick Alexander).


 * Update the [[http://www.mpir.org|MPIR]] spkg to version {{{mpir-1.2.p3.spkg}}} (Nick Alexander).
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 * FIXME: summarize #6298

Sage 4.0.2 Release Tour

Sage 4.0.2 was released on FIXME. For the official, comprehensive release note, please refer to FIXME. A nicely formatted version of this release tour can be found at FIXME. The following points are some of the foci of this release:

Algebra

  • Correct precision bound in hilbert_class_polynomial() and miscellaneous new functions (John Cremona) -- The two new functions are elliptic_j() in sage/functions/special.py, and is_primitive() in the class BinaryQF of sage/quadratic_forms/binary_qf.py. The function elliptic_j(z) returns the elliptic modular j-function evaluated at z. The function is_primitive() determines whether the binary quadratic form ax^2 + bxy + cy^2 satisfies gcd(a,b,c) = 1, i.e. that it is primitive. Here are some examples on using these new functions: 

    sage: elliptic_j(CC(i))
1728.00000000000
sage: elliptic_j(sqrt(-2.0))
8000.00000000000
sage: Q = BinaryQF([6,3,9])
sage: Q.is_primitive()
False
sage: Q = BinaryQF([1,1,1])
sage: Q.is_primitive()
True
  • Efficient Lagrange interpolation polynomial (Yann Laigle-Chapuy) -- Calculating the Lagrange interpolation polynomial of a set of points is now up to 48% faster than previously. The following timing statistics were obtained using the machine sage.math: 
    # BEFORE

sage: R = PolynomialRing(QQ, 'x')
sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
1000 loops, best of 3: 824 µs per loop
sage: R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
-23/84*x^3 - 11/84*x^2 + 13/7*x + 1
sage: R = PolynomialRing(GF(2**3,'a'), 'x')
sage: a = R.base_ring().gen()
sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])")
625 loops, best of 3: 111 µs per loop
sage: R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])
a^2*x^2 + a^2*x + a^2


# AFTER

sage: R = PolynomialRing(QQ, 'x')
sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
1000 loops, best of 3: 425 µs per loop
sage: R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
-23/84*x^3 - 11/84*x^2 + 13/7*x + 1
sage: R = PolynomialRing(GF(2**3,'a'), 'x')
sage: a = R.base_ring().gen()
sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])")
625 loops, best of 3: 86.4 µs per loop
sage: R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])
a^2*x^2 + a^2*x + a^2

  • Deprecate the method __len__() for a matrix group (Nicolas Thiery) -- The method __len__() of the class MatrixGroup_gap in sage/groups/matrix_gps/matrix_group.py is now deprecated and will be removed in a future release. To get the number of elements in a matrix group, users are advised to use the method cardinality() instead. The method order() is essentially the same as cardinality(), so order() will be deprecated in a future release.

Algebraic Geometry

  • Optimize hyperelliptic curve arithmetic (Nick Alexander) -- Arithmetics with hyperelliptic curves can be up to 6x faster than previously. The following timing statistics were obtained using the maching sage.math: 
    #BEFORE

sage: F = GF(next_prime(10^30))
sage: x = F['x'].gen()
sage: f = x^7 + x^2 + 1
sage: H = HyperellipticCurve(f, 2*x)
sage: J = H.jacobian()(F)
verbose 0 (902: multi_polynomial_ideal.py, dimension) Warning: falling back to very slow toy implementation.
sage: Q = J(H.lift_x(F(1)))
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.65 s, sys: 0.02 s, total: 0.67 s
Wall time: 0.68 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 1.08 s, sys: 0.00 s, total: 1.08 s
Wall time: 1.08 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.72 s, sys: 0.02 s, total: 0.74 s
Wall time: 0.74 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.67 s, sys: 0.00 s, total: 0.67 s
Wall time: 0.67 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.66 s, sys: 0.00 s, total: 0.66 s
Wall time: 0.66 s


# AFTER

sage: F = GF(next_prime(10^30))
sage: x = F['x'].gen()
sage: f = x^7 + x^2 + 1
sage: H = HyperellipticCurve(f, 2*x)
sage: J = H.jacobian()(F)
verbose 0 (919: multi_polynomial_ideal.py, dimension) Warning: falling back to very slow toy implementation.
sage: Q = J(H.lift_x(F(1)))
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.14 s, sys: 0.01 s, total: 0.15 s
Wall time: 0.15 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.10 s, sys: 0.00 s, total: 0.10 s
Wall time: 0.10 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s
Wall time: 0.10 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.09 s, sys: 0.01 s, total: 0.10 s
Wall time: 0.10 s
sage: %time ZZ.random_element(10**10) * Q;
CPU times: user 0.10 s, sys: 0.00 s, total: 0.10 s
Wall time: 0.11 s

Basic Arithmetic

Build

  • FIXME: summarize #6170

Calculus

Coding Theory

  • FIXME: summarize #6014

Combinatorics

Commutative Algebra

  • FIXME: summarize #6051

Cryptography

  • FIXME: summarize #6185

Graph Theory

  • FIXME: summarize #5975

Graphics

Group Theory

  • FIXME: summarize #6263
  • FIXME: summarize #6123

Interfaces

Linear Algebra

  • FIXME: summarize #6178
  • FIXME: summarize #5510
  • FIXME: summarize #2256

Miscellaneous

  • FIXME: summarize #6089
  • FIXME: summarize #6110

Modular Forms

Notebook

  • FIXME: summarize #6259
  • FIXME: summarize #6225
  • FIXME: summarize #5371

Number Theory

  • FIXME: summarize #5976
  • FIXME: summarize #5842
  • FIXME: summarize #6205
  • FIXME: summarize #6193
  • FIXME: summarize #6044
  • FIXME: summarize #6046

Numerical

Packages

  • Upgrade NumPy to version 1.3.0 latest upstream release (Jason Grout).

  • Upgrade SciPy to version 0.7 latest upstream release (Jason Grout).

  • Upgrade Singular to version 3-1-0 latest upstream release (Martin Albrecht).

  • Upgrade FLINT to version 1.3.0 latest upstream release (Nick Alexander).

  • Update the MPIR spkg to version mpir-1.2.p3.spkg (Nick Alexander).

  • Remove Guava as a standard Sage package (David Joyner).

  • FIXME: summarize #6298

Symbolics

Topology