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* FIXME: summarize ticket #5658. |
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Furthermore, on Debian 5.0 Lenny with the following system info: {{{ kernel: 2.6.24-1-686 CPU: Intel(R) Celeron(R) 2.00GHz RAM: 1.0GB }}} here are some timing statistics: |
Furthermore, on Debian 5.0 Lenny with kernel 2.6.24-1-686, an Intel(R) Celeron(R) CPU running at 2.00GHz with 1.0GB of RAM, one has the following timing statistics: |
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* FIXME: summarize #5629 |
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* Speed-up in dividing a polynomial by an integer (Burcin Erocal) -- Dividing a polynomial by an integer is now up to 7x faster than previously. On the machine sage.math, one has the following timing statistics: | * Speed-up in dividing a polynomial by an integer (Burcin Erocal) -- Dividing a polynomial by an integer is now up to 6x faster than previously. On Debian 5.0 Lenny with kernel 2.6.24-1-686, an Intel(R) Celeron(R) CPU running at 2.00GHz with 1.0GB of RAM, one has the following timing statistics: |
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625 loops, best of 3: 231 µs per loop |
625 loops, best of 3: 312 µs per loop |
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625 loops, best of 3: 32.4 µs per loop }}} * FIXME: summarize #5093 |
625 loops, best of 3: 48.3 µs per loop }}} * New {{{fast_float}}} supports more datatypes with improved performance (Carl Witty) -- A rewrite of {{{fast_float}}} to support multiple types. Here, we get accelerated evaluation over {{{RealField(k)}}} as well as {{{RDF}}}, real double field. As compared with the previous {{{fast_float}}}, improved performance can range from 2% faster to more than 2x as fast. An extended list of benchmark details is available at [[http://trac.sagemath.org/sage_trac/ticket/5093|ticket 5093]]. * FIXME: summarize #5622 |
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* FIXME: summarize #5413 | * Deprecate the calling of symbolic functions with unnamed arguments (Carl Witty, Michael Abshoff) -- Previous releases of Sage supported symbolic functions with "no arguments". This style of constructing symbolic functions is now deprecated. For example, previously Sage allowed for defining a symbolic function in the following way {{{ f2 = 5 - x^2 # bad; this is deprecated }}} But users are encouraged to explicitly declare the variables used in a symolic function. For instance, the following is encouraged: {{{ sage: x,y = var("x, y") # explicitly declare your variables sage: f(x, y) = x^2 + y^2 # this syntax is encouraged }}} |
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* FIXME: summarize #5200 | * Enhancements to the {{{Subsets}}} and {{{Subwords}}} modules (Florent Hivert) -- Numerous enhancements to the modules {{{Subsets}}} and {{{Subwords}}} include: 1. An implementation of subsets for finite multisets, i.e. sets with repetitions. 1. Adding the method {{{__contains__}}} for {{{Subsets}}} and {{{Subwords}}}. Here's an example for working with multisets: {{{ sage: S = Subsets([1, 2, 2], submultiset=True); S SubMultiset of [1, 2, 2] sage: S.list() [[], [1], [2], [1, 2], [2, 2], [1, 2, 2]] sage: Set([1,2]) in S # this uses __contains__ in Subsets True sage: Set([]) in S True sage: Set([3]) in S False }}} And here's an example of using {{{__contains__}}} with {{{Subwords}}}: {{{ sage: [] in Subwords([1,2,3,4,3,4,4]) True sage: [2,3,3,4] in Subwords([1,2,3,4,3,4,4]) True sage: [2,3,3,1] in Subwords([1,2,3,4,3,4,4]) False }}} * FIXME: summarize #5551 * First pass of cleanup of the interface of combinatorial classes -- Florent Hivert Before the patch the interface of combinatorial classes had two problems: - there where two redundant way to get the number of elements {{{len(C)}}} and {{{C.count()}}}. Moreover {{{len}}} must return a plain {{{int}}} where we want arbitrary large number and even {{{infinity}}}; - there where two redundant way to get at iterator for the elements {{{C.iterator()}}} and {{{iter(C)}}} via {{{C.__iter__}}}. The patch standardize those ways to - {{{C.cardinality}}} which is more explicit and consistent with the rest of Sage; - {{{iter(C)}}} via {{{C.__iter__}}} with is clearly more Pythonic. While the former use of {{{ iterator}}} and {{{count}}} are deprecated but still working, there is no way to keep {{{len}}} working. The reason is that {{{len}}} is called silently by a bunch of function such as {{{list / filter / map}}}, so that if we issue a warning it will pop up at a very large seemingly unrelated places which is way to much confusing. So it was decided to simply remove it and issue an error, telling to call {{{cardinality}}} instead. * FIXME: summarize #4549 |
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* FIXME: summarize #5318 |
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* FIXME: summarize #5623 |
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* Speed-up in calculating determinants of matrices (John H. Palmieri, William Stein) -- For matrices over {{{Z/nZ}}} with {{{n}}} composite, calculating their determinants is now up to 1.5% faster. On the machine sage.math, one can see the following improvement in computation time: {{{ # BEFORE sage: mat = random_matrix(Integers(256), 30) sage: timeit("Integers(256)(mat.lift().det())") 25 loops, best of 3: 9.53 ms per loop sage: sage: mat = random_matrix(Integers(256), 200) sage: timeit("Integers(256)(mat.lift().det())") 5 loops, best of 3: 779 ms per loop sage: sage: mat = random_matrix(Integers(2^20), 500) sage: timeit("Integers(256)(mat.lift().det())") 5 loops, best of 3: 13.5 s per loop # AFTER sage: mat = random_matrix(Integers(256), 30) sage: timeit("Integers(256)(mat.lift().det())") 25 loops, best of 3: 10 ms per loop sage: sage: mat = random_matrix(Integers(256), 200) sage: timeit("Integers(256)(mat.lift().det())") 5 loops, best of 3: 762 ms per loop sage: sage: mat = random_matrix(Integers(2^20), 500) sage: timeit("Integers(256)(mat.lift().det())") 5 loops, best of 3: 13.3 s per loop |
* Speed-up in calculating determinants of matrices (John H. Palmieri, William Stein) -- For matrices over {{{Z/nZ}}} with {{{n}}} composite, calculating their determinants is now up to 1500x faster. On Debian 5.0 Lenny with kernel 2.6.24-1-686, an Intel(R) Celeron(R) 2.00GHz CPU with 1.0GB of RAM, one has the following timing statistics: {{{ # BEFORE sage: time random_matrix(Integers(26), 10).determinant() CPU times: user 15.52 s, sys: 0.02 s, total: 15.54 s Wall time: 15.54 s 13 sage: time random_matrix(Integers(256), 10).determinant() CPU times: user 15.38 s, sys: 0.00 s, total: 15.38 s Wall time: 15.38 s 144 # AFTER sage: time random_matrix(Integers(26), 10).determinant() CPU times: user 0.01 s, sys: 0.00 s, total: 0.01 s Wall time: 0.01 s 23 sage: time random_matrix(Integers(256), 10).determinant() CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s Wall time: 0.00 s |
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* FIXME: summarize #5638 |
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* FIXME: summarize #5520 * FIXME: summarize #5648 * FIXME: summarize #5180 |
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FIXME: A number of tickets related to UTF-8 text got merged and should definitely be mentioned! #4547, #5211; #2896 and #1477 got fixed by those tickets. There's also #5564, which may not get merged for 3.4.1 but should get in soon; it pulls together a whole bunch of UTF-8 fixes and improvements. * FIXME: summarize #5681 |
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* FIXME: summarize #793 * FIXME: summarize #4667 * FIXME: summarize #5159 * FIXME: summarize #4990 |
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== Optional Packages == |
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* FIXME: summarize #4881 * FIXME: summarize #4880 * FIXME: summarize #4876 * FIXME: summarize #5672 * FIXME: summarize #5240 |
Sage 3.4.1 Release Tour
Sage 3.4.1 was released on FIXME. For the official, comprehensive release note, please refer to sage-3.4.1.txt. A nicely formatted version of this release tour can be found at FIXME. The following points are some of the foci of this release:
- Merging improvements during the Sage Days 13 coding sprint.
- Other bug fixes post Sage 3.4.
Algebra
- FIXME: summarize ticket #5535.
- FIXME: summarize ticket #5658.
Speed-up in irreducibility test (Ryan Hinton) -- For polynomials over the finite field GF(2), the test for irreducibility is now up to 40,000 times faster than previously. On a 64-bit Debian/squeeze machine with Core 2 Duo running at 2.33 GHz, one has the following timing improvements:
# BEFORE sage: P.<x> = GF(2)[] sage: f = P.random_element(1000) sage: %timeit f.is_irreducible() 10 loops, best of 3: 948 ms per loop sage: sage: f = P.random_element(10000) sage: %time f.is_irreducible() # gave up because it took minutes! # AFTER sage: P.<x> = GF(2)[] sage: f = P.random_element(1000) sage: %timeit f.is_irreducible() 10000 loops, best of 3: 22.7 µs per loop sage: sage: f = P.random_element(10000) sage: %timeit f.is_irreducible() 1000 loops, best of 3: 394 µs per loop sage: sage: f = P.random_element(100000) sage: %timeit f.is_irreducible() 100 loops, best of 3: 10.4 ms per loop
Furthermore, on Debian 5.0 Lenny with kernel 2.6.24-1-686, an Intel(R) Celeron(R) CPU running at 2.00GHz with 1.0GB of RAM, one has the following timing statistics:
# BEFORE sage: P.<x> = GF(2)[] sage: f = P.random_element(1000) sage: %timeit f.is_irreducible() 10 loops, best of 3: 1.14 s per loop sage: sage: f = P.random_element(10000) sage: %time f.is_irreducible() CPU times: user 4972.13 s, sys: 2.83 s, total: 4974.95 s Wall time: 5043.02 s False # AFTER sage: P.<x> = GF(2)[] sage: f = P.random_element(1000) sage: %timeit f.is_irreducible() 10000 loops, best of 3: 40.7 µs per loop sage: sage: f = P.random_element(10000) sage: %timeit f.is_irreducible() 1000 loops, best of 3: 930 µs per loop sage: sage: sage: f = P.random_element(100000) sage: %timeit f.is_irreducible() 10 loops, best of 3: 27.6 ms per loop
Algebraic Geometry
- FIXME: summarize #5629
Basic Arithmetic
- Speed-up in dividing a polynomial by an integer (Burcin Erocal) -- Dividing a polynomial by an integer is now up to 6x faster than previously. On Debian 5.0 Lenny with kernel 2.6.24-1-686, an Intel(R) Celeron(R) CPU running at 2.00GHz with 1.0GB of RAM, one has the following timing statistics:
# BEFORE sage: R.<x> = ZZ["x"] sage: f = 389 * R.random_element(1000) sage: timeit("f//389") 625 loops, best of 3: 312 µs per loop # AFTER sage: R.<x> = ZZ["x"] sage: f = 389 * R.random_element(1000) sage: timeit("f//389") 625 loops, best of 3: 48.3 µs per loop
New fast_float supports more datatypes with improved performance (Carl Witty) -- A rewrite of fast_float to support multiple types. Here, we get accelerated evaluation over RealField(k) as well as RDF, real double field. As compared with the previous fast_float, improved performance can range from 2% faster to more than 2x as fast. An extended list of benchmark details is available at ticket 5093.
- FIXME: summarize #5622
Build
Calculus
- Deprecate the calling of symbolic functions with unnamed arguments (Carl Witty, Michael Abshoff) -- Previous releases of Sage supported symbolic functions with "no arguments". This style of constructing symbolic functions is now deprecated. For example, previously Sage allowed for defining a symbolic function in the following way
f2 = 5 - x^2 # bad; this is deprecated
But users are encouraged to explicitly declare the variables used in a symolic function. For instance, the following is encouraged:sage: x,y = var("x, y") # explicitly declare your variables sage: f(x, y) = x^2 + y^2 # this syntax is encouraged
Coercion
Combinatorics
Enhancements to the Subsets and Subwords modules (Florent Hivert) -- Numerous enhancements to the modules Subsets and Subwords include:
- An implementation of subsets for finite multisets, i.e. sets with repetitions.
Adding the method __contains__ for Subsets and Subwords.
sage: S = Subsets([1, 2, 2], submultiset=True); S SubMultiset of [1, 2, 2] sage: S.list() [[], [1], [2], [1, 2], [2, 2], [1, 2, 2]] sage: Set([1,2]) in S # this uses __contains__ in Subsets True sage: Set([]) in S True sage: Set([3]) in S False
And here's an example of using __contains__ with Subwords:
sage: [] in Subwords([1,2,3,4,3,4,4]) True sage: [2,3,3,4] in Subwords([1,2,3,4,3,4,4]) True sage: [2,3,3,1] in Subwords([1,2,3,4,3,4,4]) False
- FIXME: summarize #5551
- First pass of cleanup of the interface of combinatorial classes -- Florent Hivert Before the patch the interface of combinatorial classes had two problems:
- there where two redundant way to get the number of elements len(C) and C.count(). Moreover len must return a plain int where we want arbitrary large number and even infinity;
- there where two redundant way to get at iterator for the elements C.iterator() and iter(C) via C.__iter__.
- C.cardinality which is more explicit and consistent with the rest of Sage;
- iter(C) via C.__iter__ with is clearly more Pythonic.
While the former use of iterator and count are deprecated but still working, there is no way to keep len working. The reason is that len is called silently by a bunch of function such as list / filter / map, so that if we issue a warning it will pop up at a very large seemingly unrelated places which is way to much confusing. So it was decided to simply remove it and issue an error, telling to call cardinality instead.
- FIXME: summarize #4549
Commutative Algebra
New function weil_restriction() on multivariate ideals (Martin Albrecht) -- The new function weil_restriction() computes the Weil restriction of a multivariate ideal over some extension field. A Weil restriction is also known as a restriction of scalars. Here's an example on computing a Weil restriction:
sage: k.<a> = GF(2^2) sage: P.<x,y> = PolynomialRing(k, 2) sage: I = Ideal([x*y + 1, a*x + 1]) sage: I.variety() [{y: a, x: a + 1}] sage: J = I.weil_restriction() sage: J Ideal (x1*y0 + x0*y1 + x1*y1, x0*y0 + x1*y1 + 1, x0 + x1, x1 + 1) of Multivariate Polynomial Ring in x0, x1, y0, y1 over Finite Field of size 2
- FIXME: summarize #5146
- FIXME: summarize #5353
Distribution
Doctest
- FIXME: summarize #5318
Documentation
Geometry
Graph Theory
- FIXME: summarize #5623
Graphics
Group Theory
- Speed-up in comparing elements of a permutation group (Robert Bradshaw, John H. Palmieri, Rob Beezer) -- For elements of a permutation group, comparison between those elements is now up to 13x faster. On Mac OS X 10.4 with Intel Core 2 duo running at 2.33 GHz, one has the following improvement in timing statistics:
# BEFORE sage: a = SymmetricGroup(20).random_element() sage: b = SymmetricGroup(10).random_element() sage: timeit("a == b") 625 loops, best of 3: 3.19 µs per loop # AFTER sage: a = SymmetricGroup(20).random_element() sage: b = SymmetricGroup(10).random_element() sage: time v = [a == b for _ in xrange(2000)] CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s Wall time: 0.00 s sage: timeit("a == b") 625 loops, best of 3: 240 ns per loop
Interfaces
Linear Algebra
Deprecate the function invert() (John H. Palmieri) -- The function invert() for calculating the inverse of a dense matrix with rational entries is now deprecated. Instead, users are now advised to use the function inverse(). Here's an example of using the function inverse():
sage: a = matrix(QQ, 2, [1, 5, 17, 3]) sage: a.inverse() [-3/82 5/82] [17/82 -1/82]
Speed-up in calculating determinants of matrices (John H. Palmieri, William Stein) -- For matrices over Z/nZ with n composite, calculating their determinants is now up to 1500x faster. On Debian 5.0 Lenny with kernel 2.6.24-1-686, an Intel(R) Celeron(R) 2.00GHz CPU with 1.0GB of RAM, one has the following timing statistics:
# BEFORE sage: time random_matrix(Integers(26), 10).determinant() CPU times: user 15.52 s, sys: 0.02 s, total: 15.54 s Wall time: 15.54 s 13 sage: time random_matrix(Integers(256), 10).determinant() CPU times: user 15.38 s, sys: 0.00 s, total: 15.38 s Wall time: 15.38 s 144 # AFTER sage: time random_matrix(Integers(26), 10).determinant() CPU times: user 0.01 s, sys: 0.00 s, total: 0.01 s Wall time: 0.01 s 23 sage: time random_matrix(Integers(256), 10).determinant() CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s Wall time: 0.00 s
Miscellaneous
- FIXME: summarize #5638
Modular Forms
- FIXME: summarize #5520
- FIXME: summarize #5648
- FIXME: summarize #5180
Notebook
FIXME: A number of tickets related to UTF-8 text got merged and should definitely be mentioned! #4547, #5211; #2896 and #1477 got fixed by those tickets. There's also #5564, which may not get merged for 3.4.1 but should get in soon; it pulls together a whole bunch of UTF-8 fixes and improvements.
- FIXME: summarize #5681
Number Theory
- FIXME: summarize #5518
- FIXME: summarize #5508
- FIXME: summarize #793
- FIXME: summarize #4667
- FIXME: summarize #5159
- FIXME: summarize #4990
Numerical
Packages
- FIXME: summarize #4987
- FIXME: summarize #4881
- FIXME: summarize #4880
- FIXME: summarize #4876
- FIXME: summarize #5672
- FIXME: summarize #5240
Quadratic Forms
Symbolics
User Interface