Diff for "ReleaseTours/sage-3.1.2"

⇤ ← Revision 15 as of 2008-09-19 19:37:56 →

Size: 7547

Editor: jason

Comment:

← Revision 29 as of 2024-08-19 01:28:11 → ⇥

Size: 91

Editor: mkoeppe

Comment:

Deletions are marked like this.

Additions are marked like this.

= Sage 3.1.2 Release Tour =

## page was renamed from sage-3.1.2

Sage 3.1.2 was released on XXX, 2008. For the official, comprehensive release notes, see the HISTORY.txt file that comes with the release. For the latest changes see sage-3.1.2.txt.

== Doctest Coverage Hits 60% ==
* Mike Hansen wrote doctests for almost all pexpect interfaces, which will ensure greater stability across the board.

== Hidden Markov Models ==
* William Stein wrote Cython bindings for the GHMM C library for computing with Hidden Markov Models, which are a statistical tool that is important in machine learning, natural language processing, bioinformatics, and other areas. GHMM is also now included standard in Sage.

== Notebook Bugs ==
* Many bugs introduced in 3.1.1 were fixed by Mike Hansen and Timothy Clemans.
* A new testing procedure was implemented, hopefully preventing regressions like in 3.1.1 in the future.

== New Structures for Partition Refinement ==

Robert Miller

* Hypergraphs (i.e. incidence structures) -- this includes simplicial complexes and block designs
* Matrices -- the automorphism group of a matrix is the set of column permutations which leave the (unordered) set of rows unchanged

== Improved Dense Linear Algebra over GF(2) ==
* M4RI (http://m4ri.sagemath.org) was updated to the newest upstream release which
  * provides much improved performance for multiplication (see [http://m4ri.sagemath.org/performance.html M4RI's "performance" page]),
  * provides improved performance for elimination,
  * contains several build and bugfixes.
* hashs and matrix pickling was much improved (Martin Albrecht)

'''Before'''
{{{
sage: A = random_matrix(GF(2),10000,10000)
sage: A.set_immutable()
sage: %time _ = hash(A)
CPU times: user 3.96 s, sys: 0.62 s, total: 4.58 s
sage: A = random_matrix(GF(2),2000,2000)
sage: %time _ = loads(dumps(A))
CPU times: user 4.00 s, sys: 0.07 s, total: 4.07 s
}}}

'''After'''
{{{
sage: A = random_matrix(GF(2),10000,10000)
sage: A.set_immutable()
sage: %time _ = hash(A)
CPU times: user 0.02 s, sys: 0.00 s, total: 0.02 s
sage: A = random_matrix(GF(2),2000,2000)
sage: %time _ = loads(dumps(A))
CPU times: user 1.35 s, sys: 0.01 s, total: 1.37 s
}}}

* dense matrices over $\mathbb{F}_2$ can now be written to/read from 1-bit PNG images (Martin Albrecht)

== New PolyBoRi Version (0.5) and Improved Interface ==
* PolyBoRi was upgraded from 0.3 to 0.5rc (Tim Abbott, Michael Abshoff, Martin Albrecht)
* {{{mq.SR}}} now returns PolyBoRi equation systems if asked to
* support for boolean polynomial interpolation was added

'''Example'''

First we create a random-ish boolean polynomial.

{{{
sage: B.<a,b,c,d,e,f> = BooleanPolynomialRing(6)
sage: f = a*b*c*e + a*d*e + a*f + b + c + e + f + 1
}}}
Now we find interpolation points mapping to zero and to one.

{{{
sage: zeros = set([(1, 0, 1, 0, 0, 0), (1, 0, 0, 0, 1, 0), \
                   (0, 0, 1, 1, 1, 1), (1, 0, 1, 1, 1, 1), \
                   (0, 0, 0, 0, 1, 0), (0, 1, 1, 1, 1, 0), \
                   (1, 1, 0, 0, 0, 1), (1, 1, 0, 1, 0, 1)])
sage: ones = set([(0, 0, 0, 0, 0, 0), (1, 0, 1, 0, 1, 0), \
                  (0, 0, 0, 1, 1, 1), (1, 0, 0, 1, 0, 1), \
                  (0, 0, 0, 0, 1, 1), (0, 1, 1, 0, 1, 1), \
                  (0, 1, 1, 1, 1, 1), (1, 1, 1, 0, 1, 0)])
sage: [f(*p) for p in zeros]
[0, 0, 0, 0, 0, 0, 0, 0]
sage: [f(*p) for p in ones]
[1, 1, 1, 1, 1, 1, 1, 1]
}}}

Finally, we find the lexicographically smallest interpolation polynomial using PolyBoRi .

{{{
sage: g = B.interpolation_polynomial(zeros, ones); g
b*f + c + d*f + d + e*f + e + 1
}}}

{{{
sage: [g(*p) for p in zeros]
[0, 0, 0, 0, 0, 0, 0, 0]
sage: [g(*p) for p in ones]
[1, 1, 1, 1, 1, 1, 1, 1]
}}}

== QEPCAD Interface ==

== Developer's Handbook ==
* John H Palmieri rewrote/rearranged large parts of the 'Programming Guide' (now 'Developer's Guide') which should make getting started easier for new developers.

== Improved 64-bit OSX Support ==

== Fast Numerical Integration ==

== GAP Meataxe Interface ==
* In the {{{module matrix_group}}}, the method {{{module_composition_factors}}} interfaces with GAP's [http://www.gap-system.org/Manuals/doc/htm/ref/CHAP067.htm Meataxe] implementation. This will return decomposition information for a G-module, for any matrix group G over a finite field.

== Better SymPy Integration ==
* Ondrej Cetrik implemented more conversions from Sage native types to SymPy native types.

== Faster Determinants of Dense Matrices over Multivariate Polynomial Rings ==
* Martin Albrecht modified Sage to use Singular

'''Before'''
{{{
sage: P.<x,y> = QQ[]
sage: C = random_matrix(P,8,8)
sage: %time d = C.det()
CPU times: user 2.78 s, sys: 0.02 s, total: 2.80 s
}}}

'''After'''
{{{
sage: P.<x,y> = QQ[]
sage: C = random_matrix(P,8,8)
sage: %time d = C.det()
CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s
}}}
* a discussion about this issue can be found on [http://groups.google.com/group/sage-devel/browse_thread/thread/7aa1bd1e945ff372/ sage-devel]

== Real Number Inputs Improved ==
* Robert Bradshaw improved real number input so that the precision is preserved better:

'''Before'''

{{{
sage: RealField(256)(1.2)
1.199999999999999955591079014993738383054733276367187500000000000000000000000
}}}

'''After'''
{{{
sage: RealField(256)(1.2)
1.200000000000000000000000000000000000000000000000000000000000000000000000000
}}}

== Arrow drawing improved ==
* Jason Grout redid the arrows to look nicer and behave better with graphs:

{{{
sage: g = DiGraph({0:[1,2,3],1:[0,3,4], 3:[4,6]})
sage: show(g)
}}}

== Eigen functions for matrices ==
* Jason Grout added a few standard functions to compute eigenvalues and left and right eigenvectors, returning exact results in QQbar.

{{{
sage: a = matrix(QQ, 4, range(16)); a
[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]
sage: a.eigenvalues()
[0, 0, -2.464249196572981?, 32.46424919657298?]
sage: a.eigenvectors_right()
[(0, [
(1, 0, -3, 2),
(0, 1, -2, 1)
], 2),
(-2.464249196572981?,
  [(1, 0.3954107716733585?, -0.2091784566532830?, -0.8137676849799244?)],
  1),
(32.46424919657298?,
  [(1, 2.890303514040928?, 4.780607028081855?, 6.670910542122782?)],
  1)]
sage: D,P=a.eigenmatrix_right()
sage: P
[ 1 0 1 1]
[ 0 1 0.3954107716733585? 2.890303514040928?]
[ -3 -2 -0.2091784566532830? 4.780607028081855?]
[ 2 1 -0.8137676849799244? 6.670910542122782?]
sage: D

[ 0 0 0 0]
[ 0 0 0 0]
[ 0 0 -2.464249196572981? 0]
[ 0 0 0 32.46424919657298?]
sage: a*P==P*D
True
}}}

The question marks at the end of the numbers in the previous example mean that Sage is printing out an approximation of an exact value that it uses. In particular, the question mark means that the last digit can vary by plus or minus 1. In other words, 32.46424919657298? means that the exact number is really between 32.46424919657297 and 32.46424919657299. Sage knows what the exact number is and uses the exact number in calculations.

moved to https://github.com/sagemath/sage/releases

-  ⇤ ← Revision 15 as of 2008-09-19 19:37:56 → 
  Size: 7547
  Editor: jason
  Comment:
+   ← Revision 29 as of 2024-08-19 01:28:11 → ⇥
  Size: 91
  Editor: mkoeppe
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 1:
-= Sage 3.1.2 Release Tour =
+## page was renamed from sage-3.1.2
 Line 3:
-Sage 3.1.2 was released on XXX, 2008. For the official, comprehensive release notes, see the HISTORY.txt file that comes with the release. For the latest changes see sage-3.1.2.txt. 

== Doctest Coverage Hits 60% ==
 * Mike Hansen wrote doctests for almost all pexpect interfaces, which will ensure greater stability across the board.

== Hidden Markov Models ==
 * William Stein wrote Cython bindings for the GHMM C library for computing with Hidden Markov Models, which are a statistical tool that is important in machine learning, natural language processing, bioinformatics, and other areas.  GHMM is also now included standard in Sage.

== Notebook Bugs ==
 * Many bugs introduced in 3.1.1 were fixed by Mike Hansen and Timothy Clemans. 
 * A new testing procedure was implemented, hopefully preventing regressions like in 3.1.1 in the future.

== New Structures for Partition Refinement ==

Robert Miller

 * Hypergraphs (i.e. incidence structures) -- this includes simplicial complexes and block designs
 * Matrices -- the automorphism group of a matrix is the set of column permutations which leave the (unordered) set of rows unchanged

== Improved Dense Linear Algebra over GF(2) ==
 * M4RI (http://m4ri.sagemath.org) was updated to the newest upstream release which
  * provides much improved performance for multiplication (see [http://m4ri.sagemath.org/performance.html M4RI's "performance" page]),
  * provides improved performance for elimination,
  * contains several build and bugfixes.
 * hashs and matrix pickling was much improved (Martin Albrecht)

'''Before'''
{{{
sage: A = random_matrix(GF(2),10000,10000)
sage: A.set_immutable()
sage: %time _ = hash(A)
CPU times: user 3.96 s, sys: 0.62 s, total: 4.58 s
sage: A = random_matrix(GF(2),2000,2000)
sage: %time _ = loads(dumps(A))
CPU times: user 4.00 s, sys: 0.07 s, total: 4.07 s
}}}

'''After'''
{{{
sage: A = random_matrix(GF(2),10000,10000)
sage: A.set_immutable()
sage: %time _ = hash(A)
CPU times: user 0.02 s, sys: 0.00 s, total: 0.02 s
sage: A = random_matrix(GF(2),2000,2000)
sage: %time _ = loads(dumps(A))
CPU times: user 1.35 s, sys: 0.01 s, total: 1.37 s
}}}

 * dense matrices over $\mathbb{F}_2$ can now be written to/read from 1-bit PNG images (Martin Albrecht)

== New PolyBoRi Version (0.5) and Improved Interface ==
 * PolyBoRi was upgraded from 0.3 to 0.5rc (Tim Abbott, Michael Abshoff, Martin Albrecht)
 * {{{mq.SR}}} now returns PolyBoRi equation systems if asked to
 * support for boolean polynomial interpolation was added

'''Example'''

First we create a random-ish boolean polynomial.

{{{
sage: B.<a,b,c,d,e,f> = BooleanPolynomialRing(6)
sage: f = a*b*c*e + a*d*e + a*f + b + c + e + f + 1
}}}            
Now we find interpolation points mapping to zero and to one.

{{{
sage: zeros = set([(1, 0, 1, 0, 0, 0), (1, 0, 0, 0, 1, 0), \
                   (0, 0, 1, 1, 1, 1), (1, 0, 1, 1, 1, 1), \
                   (0, 0, 0, 0, 1, 0), (0, 1, 1, 1, 1, 0), \
                   (1, 1, 0, 0, 0, 1), (1, 1, 0, 1, 0, 1)])
sage: ones = set([(0, 0, 0, 0, 0, 0), (1, 0, 1, 0, 1, 0), \
                  (0, 0, 0, 1, 1, 1), (1, 0, 0, 1, 0, 1), \
                  (0, 0, 0, 0, 1, 1), (0, 1, 1, 0, 1, 1), \
                  (0, 1, 1, 1, 1, 1), (1, 1, 1, 0, 1, 0)])
sage: [f(*p) for p in zeros]
[0, 0, 0, 0, 0, 0, 0, 0]
sage: [f(*p) for p in ones]
[1, 1, 1, 1, 1, 1, 1, 1]
}}}

Finally, we find the lexicographically smallest interpolation polynomial using PolyBoRi .

{{{
sage: g = B.interpolation_polynomial(zeros, ones); g
b*f + c + d*f + d + e*f + e + 1
}}}

{{{
sage: [g(*p) for p in zeros]
[0, 0, 0, 0, 0, 0, 0, 0]
sage: [g(*p) for p in ones]
[1, 1, 1, 1, 1, 1, 1, 1]
}}}


== QEPCAD Interface ==

== Developer's Handbook ==
 *  John H Palmieri rewrote/rearranged large parts of the 'Programming Guide' (now 'Developer's Guide') which should make getting started easier for new developers.

== Improved 64-bit OSX Support ==

== Fast Numerical Integration ==

== GAP Meataxe Interface ==
 * In the {{{module matrix_group}}}, the method {{{module_composition_factors}}} interfaces with GAP's [http://www.gap-system.org/Manuals/doc/htm/ref/CHAP067.htm Meataxe] implementation. This will return decomposition information for a G-module, for any matrix group G over a finite field.

== Better SymPy Integration ==
 * Ondrej Cetrik implemented more conversions from Sage native types to SymPy native types.

== Faster Determinants of Dense Matrices over Multivariate Polynomial Rings ==
 * Martin Albrecht modified Sage to use Singular 

'''Before'''
{{{
sage: P.<x,y> = QQ[]
sage: C = random_matrix(P,8,8)
sage: %time d = C.det()
CPU times: user 2.78 s, sys: 0.02 s, total: 2.80 s
}}}

'''After'''
{{{
sage: P.<x,y> = QQ[]
sage: C = random_matrix(P,8,8)
sage: %time d = C.det()
CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s
}}}
 * a discussion about this issue can be found on [http://groups.google.com/group/sage-devel/browse_thread/thread/7aa1bd1e945ff372/ sage-devel]

== Real Number Inputs Improved ==
 * Robert Bradshaw improved real number input so that the precision is preserved better:

'''Before'''

{{{
sage: RealField(256)(1.2)
1.199999999999999955591079014993738383054733276367187500000000000000000000000
}}}

'''After'''
{{{
sage: RealField(256)(1.2)
1.200000000000000000000000000000000000000000000000000000000000000000000000000
}}}

== Arrow drawing improved ==
 * Jason Grout redid the arrows to look nicer and behave better with graphs:

{{{
sage: g = DiGraph({0:[1,2,3],1:[0,3,4], 3:[4,6]})
sage: show(g)
}}}

== Eigen functions for matrices ==
 * Jason Grout added a few standard functions to compute eigenvalues and left and right eigenvectors, returning exact results in QQbar.

{{{
sage: a = matrix(QQ, 4, range(16)); a
[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]
[12 13 14 15]
sage: a.eigenvalues()
[0, 0, -2.464249196572981?, 32.46424919657298?]
sage: a.eigenvectors_right()
[(0, [
(1, 0, -3, 2),
(0, 1, -2, 1)
], 2),
 (-2.464249196572981?,
  [(1, 0.3954107716733585?, -0.2091784566532830?, -0.8137676849799244?)],
  1),
 (32.46424919657298?,
  [(1, 2.890303514040928?, 4.780607028081855?, 6.670910542122782?)],
  1)]
sage: D,P=a.eigenmatrix_right()
sage: P
[                   1                    0                    1                    1]
[                   0                    1  0.3954107716733585?   2.890303514040928?]
[                  -3                   -2 -0.2091784566532830?   4.780607028081855?]
[                   2                    1 -0.8137676849799244?   6.670910542122782?]
sage: D

[                  0                   0                   0                   0]
[                  0                   0                   0                   0]
[                  0                   0 -2.464249196572981?                   0]
[                  0                   0                   0  32.46424919657298?]
sage: a*P==P*D
True
}}}

The question marks at the end of the numbers in the previous example mean that Sage is printing out an approximation of an exact value that it uses.  In particular, the question mark means that the last digit can vary by plus or minus 1.  In other words, 32.46424919657298? means that the exact number is really between 32.46424919657297 and 32.46424919657299.  Sage knows what the exact number is and uses the exact number in calculations.
+moved to https://github.com/sagemath/sage/releases