Differences between revisions 2 and 10 (spanning 8 versions)
Revision 2 as of 2009-06-04 14:50:10
Size: 1443
Editor: Minh Nguyen
Comment: Reminders to showcase features
Revision 10 as of 2009-06-07 07:14:28
Size: 14516
Editor: Minh Nguyen
Comment: Summarize #5599
Deletions are marked like this. Additions are marked like this.
Line 13: Line 13:
 * FIXME: summarize #6016

 * FIXME: summarize #3699
 * Factoring rational functions (Soroosh Yazdani) -- New method {{{factor()}}} in the class {{{FractionFieldElement}}} of {{{sage/rings/fraction_field_element.pyx}}} to return the factorization of self over the base ring. Here's an example for working with this new method:
 {{{
sage: K.<x> = QQ["x"]
sage: f = (x^3 + x) / (x-3)
sage: f.factor()
(x - 3)^-1 * x * (x^2 + 1)
 }}}


 * Faster {{{basis_matrix()}}} for ambient modules (John Cremona) -- The speed-up can be up to 376x faster than previously. The following timing statistics were obtained using the machine sage.math:
 {{{
# BEFORE

sage: K = FreeModule(ZZ, 2000)
sage: %time I = K.basis_matrix()
CPU times: user 292.74 s, sys: 20.11 s, total: 312.85 s
Wall time: 312.90 s


# AFTER

sage: K = FreeModule(ZZ, 2000)
sage: %time I = K.basis_matrix()
CPU times: user 0.41 s, sys: 0.43 s, total: 0.84 s
Wall time: 0.83 s
 }}}


 * Optimize the construction of Lagrange interpolation polynomials (Minh Van Nguyen) -- Rewrite the method {{{lagrange_polynomial()}}} in the class {{{PolynomialRing_field}}} of {{{sage/rings/polynomial/polynomial_ring.py}}} for generating the {{{n}}}-th Lagrange interpolation polynomial. The method now provides two new options:

  * {{{algorithm}}} --- (default: {{{divided_difference}}}) If {{{algorithm="divided_difference"}}}, then use the method of divided difference. If {{{algorithm="neville"}}}, then use a variant of Neville's method to recursively generate the {{{n}}}-th Lagrange interpolation polynomial. This adaptation of Neville's method is more memory efficient than the original Neville's method, since the former doesn't generate the full Neville table resulting from Neville's recursive procedure. Instead the adaptation only keeps track of the current and previous rows of the said table.

  * {{{previous_row}}} --- (default: {{{None}}}) This is only relevant if used together with {{{algorithm="neville"}}}. Here "previous row" refers to the last row in the Neville table that was obtained from a previous computation of an {{{n}}}-th Lagrange interpolation polynomial using Neville's method. If the last row is provided, then use a memory efficient variant of Neville's method to recursively generate a better interpolation polynomial from the results of previous computation.

 There's also the new method {{{divided_difference()}}} to compute the Newton divided-difference coefficients of the {{{n}}}-th Lagrange interpolation polynomial. The following are some timing statistics obtained using sage.math. When the results of previous computations are fed to {{{lagrange_polynomial}}} in order to produce better interpolation polynomials, we can gain an efficiency of up to 42%.
 {{{
# BEFORE

# using the definition of Lagrange interpolation polynomial
sage: R = PolynomialRing(QQ, 'x')
sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
1000 loops, best of 3: 1.71 ms per loop
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: 233 µs per loop

# without using precomputed values to generate successively better interpolation polynomials

sage: R = PolynomialRing(QQ, 'x')
sage: timeit("R.lagrange_polynomial([(0,1),(2,2)])");
625 loops, best of 3: 571 µs per loop
sage: # add two more points
sage: timeit("R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])");
125 loops, best of 3: 2.29 ms per loop
sage:
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)])")
625 loops, best of 3: 76.1 µs per loop
sage: # add another point
sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])")
625 loops, best of 3: 229 µs per loop
sage:
sage: R = PolynomialRing(QQ, 'x')
sage: points = [(random(), random()) for i in xrange(100)]
sage: time R.lagrange_polynomial(points);
CPU times: user 1.21 s, sys: 0.00 s, total: 1.21 s
Wall time: 1.21 s
sage: # add three more points
sage: for i in xrange(3): points.append((random(), random()))
....:
sage: time R.lagrange_polynomial(points);
CPU times: user 1.28 s, sys: 0.01 s, total: 1.29 s
Wall time: 1.29 s
sage: # add another 100 points
sage: for i in xrange(100): points.append((random(), random()))
....:
sage: time R.lagrange_polynomial(points);
CPU times: user 5.87 s, sys: 0.02 s, total: 5.89 s
Wall time: 5.89 s


# AFTER

# using the method of divided-difference
sage: R = PolynomialRing(QQ, 'x')
sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
1000 loops, best of 3: 827 µs per loop
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

# using precomputed values to generate successively better interpolation polynomials

sage: R = PolynomialRing(QQ, 'x')
sage: timeit("R.lagrange_polynomial([(0,1),(2,2)], neville=True)");
625 loops, best of 3: 332 µs per loop
sage: p = R.lagrange_polynomial([(0,1),(2,2)], neville=True);
sage: # add two more points
sage: timeit("R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)], neville=True, previous_row=p)");
625 loops, best of 3: 1.41 ms per loop
sage:
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)], neville=True)");
625 loops, best of 3: 36.4 µs per loop
sage: p = R.lagrange_polynomial([(a^2+a,a),(a,1)], neville=True);
sage: # add another point
sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)], neville=True, previous_row=p)");
625 loops, best of 3: 131 µs per loop
sage:
sage: R = PolynomialRing(QQ, 'x')
sage: points = [(random(), random()) for i in xrange(100)]
sage: time R.lagrange_polynomial(points, neville=True);
CPU times: user 1.26 s, sys: 0.00 s, total: 1.26 s
Wall time: 1.26 s
sage: p = R.lagrange_polynomial(points, neville=True);
sage: # add three more points
sage: for i in xrange(3): points.append((random(), random()))
....:
sage: time R.lagrange_polynomial(points, neville=True, previous_row=p);
CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s
Wall time: 0.08 s
sage: p = R.lagrange_polynomial(points, neville=True, previous_row=p)
sage: # add another 100 points
sage: for i in xrange(100): points.append((random(), random()))
....:
sage: time R.lagrange_polynomial(points, neville=True, previous_row=p);
CPU times: user 4.62 s, sys: 0.00 s, total: 4.62 s
Wall time: 4.62 s
 }}}
Line 27: Line 157:
 * FIXME: summarize #5732  * Speed overhaul for {{{digits}}}, {{{exact_log}}} and {{{ndigits}}} (Joel B. Mohler) -- Speed-up for the cases where the method {{{exact_log}}} can conveniently be computed by log 2 estimation. In some cases, time efficiency can be up to 927x faster than previously. The following timing statistics were obtained using the machine sage.math:
 {{{
# BEFORE

sage: n = 5^1000
sage: m = 2975982357823879528793587928793592
sage: %timeit n.exact_log(m)
1000 loops, best of 3: 205 µs per loop
sage: n = 5^50
sage: m = 33
sage: %timeit n.exact_log(m)
10000 loops, best of 3: 29.6 µs per loop
sage: def zlog(m, n, k):
....: for i in xrange(0, m/1000):
....: a = ZZ.random_element(n) + 2
....: b = ZZ.random_element(k)
....: c = a^b
....: for i in xrange (0, 1000):
....: c.exact_log(a)
....:
sage: time zlog(100000, 2^100, 100)
CPU times: user 22.59 s, sys: 0.12 s, total: 22.71 s
Wall time: 22.70 s
sage: time zlog(100000, 100, 100)
CPU times: user 3.45 s, sys: 0.02 s, total: 3.47 s
Wall time: 3.47 s


# AFTER

sage: n = 5^1000
sage: m = 2975982357823879528793587928793592
sage: %timeit n.exact_log(m)
1000000 loops, best of 3: 221 ns per loop
sage: n = 5^50
sage: m = 33
sage: %timeit n.exact_log(m)
1000000 loops, best of 3: 526 ns per loop
sage: def zlog(m, n, k):
....: for i in xrange(0, m/1000):
....: a = ZZ.random_element(n) + 2
....: b = ZZ.random_element(k)
....: c = a^b
....: for i in xrange (0, 1000):
....: c.exact_log(a)
....:
sage: time zlog(100000, 2^100, 100)
CPU times: user 1.96 s, sys: 0.02 s, total: 1.98 s
Wall time: 1.99 s
sage: time zlog(100000, 100, 100)
CPU times: user 0.05 s, sys: 0.01 s, total: 0.06 s
Wall time: 0.05 s
 }}}


== Calculus ==


 * Deprecate the function {{{numerical_sqrt()}}} (Robert Bradshaw, John H. Palmieri) -- The function {{{numerical_sqrt()}}} in {{{sage/misc/functional.py}}} is now deprecated. Users are advised to instead use {{{sqrt()}}}.
Line 33: Line 221:
 * FIXME: summarize #6000

 * FIXME: summarize #6167

 * FIXME: summarize #6093


== Commutative Algebra ==


 * FIXME: summarize #6120
 * Sets enumerated by exploring a search space with a (lazy) tree or graph structure (Nicolas Thiery) -- Extend the {{{sage.combinat.backtrack}}} library with other generic tools for constructing large sets whose elements can be enumerated by exploring a search space with a (lazy) tree or graph structure. The following generic utilities have been added:
  1. {{{SearchForest}}}: Depth first search through a tree descrived by a "child" function.
  1. {{{GenericBacktracker}}}: Depth first search through a tree descrived by a "child" function, with branch pruning, etc.
  1. {{{TransitiveIdeal}}}: Depth first search through a graph described by a "neighbours" relation.
  1. {{{TransitiveIdealGraded}}}: Breath first search through a graph described by a "neighbours" relation.


 * The Sloane sequence A000008 (Joanna Gaski) -- The [[http://www.research.att.com/~njas/sequences/A000008|Sloane sequence A000008]] is concerned with the number of ways of making change for {{{n}}} cents where one is restricted to using only coins of denominations 1, 2, 5, and 10 cents. This is contained in the new class {{{A000008}}} in {{{sage/combinat/sloane_functions.py}}}. Here are some examples on using this class:
 {{{
sage: a = sloane.A000008; a
Number of ways of making change for n cents using coins of 1, 2, 5, 10 cents.
sage: a(0)
1
sage: a(1)
1
sage: a(13)
16
sage: a.list(14)
[1, 1, 2, 2, 3, 4, 5, 6, 7, 8, 11, 12, 15, 16]
 }}}


 * Read {{{ext_rep}}} format of combinatorial designs (Carlo Hamalainen) -- The new module {{{sage/combinat/designs/ext_rep.py}}} is an API to the abstract tree represented by an XML document containing the External Representation of a list of block designs. The relevant combinatorial designs are read from the online database at [[http://designtheory.org/database]]. This module also provides the related I/O operations for reading and writing ext-rep files or data. The parsing is based on {{{expat}}}.


 * Dynkin diagram ASCII art for reducible Cartan types (Dan Bump) -- Here are some examples on such ASCII art:
 {{{
sage: CartanType("F4xA2").dynkin_diagram()

O---O=>=O---O
1 2 3 4
O---O
5 6
F4xA2
sage: t = CartanType("A2xB2xF4")
sage: dd = DynkinDiagram(t); dd

O---O
1 2
O=>=O
3 4
O---O=>=O---O
5 6 7 8
A2xB2xF4
 }}}


 * Speed-up computation in symmetric algebra group (Dan Christensen) -- The previous code essentially reimplemented the multiplication in the group algebra. Now it accumulates the symmetrizers and antisymmetrizers separately, and then does one multiplication at the end. This probably results in the same number of operations, but it avoids creating many intermediate objects. The speed-up can be up to ...


 * Improve speed of combinatorial algebra multiplication (Dan Christensen) -- The speed-up concerns the method {{{multiply()}}} of the class {{{CombinatorialAlgebra}}} in {{{sage/combinat/combinatorial_algebra.py}}}. The speed-up can be up to ...
Line 57: Line 285:
 * FIXME: summarize #5599

 * Centering of contour and density plots (Jason Grout) -- The following example shows a "spotlight" on the origin:
 {{{
sage: x, y = var('x,y')
sage: density_plot(1/(x^10+y^10), (x, -10, 10), (y, -10, 10))
 }}}
{{attachment:spotlight.png}}

 This plots concentric circles centered at the origin:
 {{{
sage: x, y = var('x,y')
sage: contour_plot(x^2 + y^2 - 2, (x,-1,1), (y,-1,1)).show(aspect_ratio=1)
 }}}
{{attachment:concentric-circles.png}}

 The following example plots a disk centered at the origin:
 {{{
sage: x, y = var('x,y')
sage: region_plot(x^2 + y^2 < 1, (x,-1,1), (y,-1,1)).show(aspect_ratio=1)
 }}}
{{attachment:disk.png}}
Line 66: Line 313:
 * Improving the GAP interface by pre-compiling certain regular expressions (Simon King) -- The speed-up in the GAP interface is now up to 37% faster than previously. The following timing statistics were obtained using the machine sage.math:
 {{{
# BEFORE

sage: G = SymmetricGroup(7)
sage: g = G._gap_()
sage: l = g.Elements()
sage: time L = [gap.eval(l.name() + '[%d]^2' % (i)) for i in xrange(1, 7.factorial() + 1)]
CPU times: user 1.90 s, sys: 0.16 s, total: 2.06 s
Wall time: 2.08 s


# AFTER

sage: G = SymmetricGroup(7)
sage: g = G._gap_()
sage: l = g.Elements()
sage: time L = [gap.eval(l.name() + '[%d]^2' % (i)) for i in xrange(1, 7.factorial() + 1)]
CPU times: user 1.07 s, sys: 0.22 s, total: 1.29 s
Wall time: 1.31 s
 }}}

Line 76: Line 346:
 * FIXME: summarize #6139
Line 98: Line 370:
 * FIXME: summarize #6206


== Numerical ==


 * FIXME: summarize #5827
Line 112: Line 392:
 * FIXME: summarize #6209

 * FIXME: summarize #6219
Line 122: Line 406:
 * FIXME: summarize #6144

 * FIXME: summarize #6194

Sage 4.0.1 Release Tour

Sage 4.0.1 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

  • Factoring rational functions (Soroosh Yazdani) -- New method factor() in the class FractionFieldElement of sage/rings/fraction_field_element.pyx to return the factorization of self over the base ring. Here's an example for working with this new method:

    sage: K.<x> = QQ["x"]
    sage: f = (x^3 + x) / (x-3)
    sage: f.factor()
    (x - 3)^-1 * x * (x^2 + 1)
  • Faster basis_matrix() for ambient modules (John Cremona) -- The speed-up can be up to 376x faster than previously. The following timing statistics were obtained using the machine sage.math:

    # BEFORE
    
    sage: K = FreeModule(ZZ, 2000)
    sage: %time I = K.basis_matrix()
    CPU times: user 292.74 s, sys: 20.11 s, total: 312.85 s
    Wall time: 312.90 s
    
    
    # AFTER
    
    sage: K = FreeModule(ZZ, 2000)
    sage: %time I = K.basis_matrix()
    CPU times: user 0.41 s, sys: 0.43 s, total: 0.84 s
    Wall time: 0.83 s
  • Optimize the construction of Lagrange interpolation polynomials (Minh Van Nguyen) -- Rewrite the method lagrange_polynomial() in the class PolynomialRing_field of sage/rings/polynomial/polynomial_ring.py for generating the n-th Lagrange interpolation polynomial. The method now provides two new options:

    • algorithm --- (default: divided_difference) If algorithm="divided_difference", then use the method of divided difference. If algorithm="neville", then use a variant of Neville's method to recursively generate the n-th Lagrange interpolation polynomial. This adaptation of Neville's method is more memory efficient than the original Neville's method, since the former doesn't generate the full Neville table resulting from Neville's recursive procedure. Instead the adaptation only keeps track of the current and previous rows of the said table.

    • previous_row --- (default: None) This is only relevant if used together with algorithm="neville". Here "previous row" refers to the last row in the Neville table that was obtained from a previous computation of an n-th Lagrange interpolation polynomial using Neville's method. If the last row is provided, then use a memory efficient variant of Neville's method to recursively generate a better interpolation polynomial from the results of previous computation.

    There's also the new method divided_difference() to compute the Newton divided-difference coefficients of the n-th Lagrange interpolation polynomial. The following are some timing statistics obtained using sage.math. When the results of previous computations are fed to lagrange_polynomial in order to produce better interpolation polynomials, we can gain an efficiency of up to 42%.

    # BEFORE
    
    # using the definition of Lagrange interpolation polynomial
    sage: R = PolynomialRing(QQ, 'x')
    sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
    1000 loops, best of 3: 1.71 ms per loop
    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: 233 µs per loop
    
    # without using precomputed values to generate successively better interpolation polynomials
    
    sage: R = PolynomialRing(QQ, 'x')
    sage: timeit("R.lagrange_polynomial([(0,1),(2,2)])");
    625 loops, best of 3: 571 µs per loop
    sage: # add two more points
    sage: timeit("R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])");
    125 loops, best of 3: 2.29 ms per loop
    sage: 
    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)])")
    625 loops, best of 3: 76.1 µs per loop
    sage: # add another point
    sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)])")
    625 loops, best of 3: 229 µs per loop
    sage:
    sage: R = PolynomialRing(QQ, 'x')
    sage: points = [(random(), random()) for i in xrange(100)]
    sage: time R.lagrange_polynomial(points);
    CPU times: user 1.21 s, sys: 0.00 s, total: 1.21 s
    Wall time: 1.21 s
    sage: # add three more points
    sage: for i in xrange(3): points.append((random(), random()))
    ....: 
    sage: time R.lagrange_polynomial(points);
    CPU times: user 1.28 s, sys: 0.01 s, total: 1.29 s
    Wall time: 1.29 s
    sage: # add another 100 points
    sage: for i in xrange(100): points.append((random(), random()))
    ....: 
    sage: time R.lagrange_polynomial(points);
    CPU times: user 5.87 s, sys: 0.02 s, total: 5.89 s
    Wall time: 5.89 s
    
    
    # AFTER
    
    # using the method of divided-difference
    sage: R = PolynomialRing(QQ, 'x')
    sage: %timeit R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)])
    1000 loops, best of 3: 827 µs per loop
    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
    
    # using precomputed values to generate successively better interpolation polynomials
    
    sage: R = PolynomialRing(QQ, 'x')
    sage: timeit("R.lagrange_polynomial([(0,1),(2,2)], neville=True)");
    625 loops, best of 3: 332 µs per loop
    sage: p = R.lagrange_polynomial([(0,1),(2,2)], neville=True);
    sage: # add two more points
    sage: timeit("R.lagrange_polynomial([(0,1),(2,2),(3,-2),(-4,9)], neville=True, previous_row=p)");
    625 loops, best of 3: 1.41 ms per loop
    sage:
    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)], neville=True)");
    625 loops, best of 3: 36.4 µs per loop
    sage: p = R.lagrange_polynomial([(a^2+a,a),(a,1)], neville=True);
    sage: # add another point
    sage: timeit("R.lagrange_polynomial([(a^2+a,a),(a,1),(a^2,a^2+a+1)], neville=True, previous_row=p)");
    625 loops, best of 3: 131 µs per loop
    sage:
    sage: R = PolynomialRing(QQ, 'x')
    sage: points = [(random(), random()) for i in xrange(100)]
    sage: time R.lagrange_polynomial(points, neville=True);
    CPU times: user 1.26 s, sys: 0.00 s, total: 1.26 s
    Wall time: 1.26 s
    sage: p = R.lagrange_polynomial(points, neville=True);
    sage: # add three more points
    sage: for i in xrange(3): points.append((random(), random()))
    ....: 
    sage: time R.lagrange_polynomial(points, neville=True, previous_row=p);
    CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s
    Wall time: 0.08 s
    sage: p = R.lagrange_polynomial(points, neville=True, previous_row=p)
    sage: # add another 100 points
    sage: for i in xrange(100): points.append((random(), random()))
    ....: 
    sage: time R.lagrange_polynomial(points, neville=True, previous_row=p);
    CPU times: user 4.62 s, sys: 0.00 s, total: 4.62 s
    Wall time: 4.62 s

Algebraic Geometry

  • FIXME: summarize #5948

Basic Arithmetic

  • Speed overhaul for digits, exact_log and ndigits (Joel B. Mohler) -- Speed-up for the cases where the method exact_log can conveniently be computed by log 2 estimation. In some cases, time efficiency can be up to 927x faster than previously. The following timing statistics were obtained using the machine sage.math:

    # BEFORE
    
    sage: n = 5^1000
    sage: m = 2975982357823879528793587928793592
    sage: %timeit n.exact_log(m)
    1000 loops, best of 3: 205 µs per loop
    sage: n = 5^50
    sage: m = 33
    sage: %timeit n.exact_log(m)
    10000 loops, best of 3: 29.6 µs per loop
    sage: def zlog(m, n, k):
    ....:     for i in xrange(0, m/1000):
    ....:         a = ZZ.random_element(n) + 2
    ....:         b = ZZ.random_element(k)
    ....:         c = a^b
    ....:         for i in xrange (0, 1000):
    ....:             c.exact_log(a)
    ....:             
    sage: time zlog(100000, 2^100, 100)
    CPU times: user 22.59 s, sys: 0.12 s, total: 22.71 s
    Wall time: 22.70 s
    sage: time zlog(100000, 100, 100)
    CPU times: user 3.45 s, sys: 0.02 s, total: 3.47 s
    Wall time: 3.47 s
    
    
    # AFTER
    
    sage: n = 5^1000
    sage: m = 2975982357823879528793587928793592
    sage: %timeit n.exact_log(m)
    1000000 loops, best of 3: 221 ns per loop
    sage: n = 5^50
    sage: m = 33
    sage: %timeit n.exact_log(m)
    1000000 loops, best of 3: 526 ns per loop
    sage: def zlog(m, n, k):
    ....:     for i in xrange(0, m/1000):
    ....:         a = ZZ.random_element(n) + 2
    ....:         b = ZZ.random_element(k)
    ....:         c = a^b
    ....:         for i in xrange (0, 1000):
    ....:             c.exact_log(a)
    ....:             
    sage: time zlog(100000, 2^100, 100)
    CPU times: user 1.96 s, sys: 0.02 s, total: 1.98 s
    Wall time: 1.99 s
    sage: time zlog(100000, 100, 100)
    CPU times: user 0.05 s, sys: 0.01 s, total: 0.06 s
    Wall time: 0.05 s

Calculus

  • Deprecate the function numerical_sqrt() (Robert Bradshaw, John H. Palmieri) -- The function numerical_sqrt() in sage/misc/functional.py is now deprecated. Users are advised to instead use sqrt().

Combinatorics

  • Sets enumerated by exploring a search space with a (lazy) tree or graph structure (Nicolas Thiery) -- Extend the sage.combinat.backtrack library with other generic tools for constructing large sets whose elements can be enumerated by exploring a search space with a (lazy) tree or graph structure. The following generic utilities have been added:

    1. SearchForest: Depth first search through a tree descrived by a "child" function.

    2. GenericBacktracker: Depth first search through a tree descrived by a "child" function, with branch pruning, etc.

    3. TransitiveIdeal: Depth first search through a graph described by a "neighbours" relation.

    4. TransitiveIdealGraded: Breath first search through a graph described by a "neighbours" relation.

  • The Sloane sequence A000008 (Joanna Gaski) -- The Sloane sequence A000008 is concerned with the number of ways of making change for n cents where one is restricted to using only coins of denominations 1, 2, 5, and 10 cents. This is contained in the new class A000008 in sage/combinat/sloane_functions.py. Here are some examples on using this class:

    sage: a = sloane.A000008; a
    Number of ways of making change for n cents using coins of 1, 2, 5, 10 cents.
    sage: a(0)
    1
    sage: a(1)
    1
    sage: a(13)
    16
    sage: a.list(14)
    [1, 1, 2, 2, 3, 4, 5, 6, 7, 8, 11, 12, 15, 16]
  • Read ext_rep format of combinatorial designs (Carlo Hamalainen) -- The new module sage/combinat/designs/ext_rep.py is an API to the abstract tree represented by an XML document containing the External Representation of a list of block designs. The relevant combinatorial designs are read from the online database at http://designtheory.org/database. This module also provides the related I/O operations for reading and writing ext-rep files or data. The parsing is based on expat.

  • Dynkin diagram ASCII art for reducible Cartan types (Dan Bump) -- Here are some examples on such ASCII art:
    sage: CartanType("F4xA2").dynkin_diagram()
    
    O---O=>=O---O
    1   2   3   4
    O---O
    5   6
    F4xA2
    sage: t = CartanType("A2xB2xF4")
    sage: dd = DynkinDiagram(t); dd
    
    O---O
    1   2
    O=>=O
    3   4
    O---O=>=O---O
    5   6   7   8
    A2xB2xF4
  • Speed-up computation in symmetric algebra group (Dan Christensen) -- The previous code essentially reimplemented the multiplication in the group algebra. Now it accumulates the symmetrizers and antisymmetrizers separately, and then does one multiplication at the end. This probably results in the same number of operations, but it avoids creating many intermediate objects. The speed-up can be up to ...
  • Improve speed of combinatorial algebra multiplication (Dan Christensen) -- The speed-up concerns the method multiply() of the class CombinatorialAlgebra in sage/combinat/combinatorial_algebra.py. The speed-up can be up to ...

Geometry

Graph Theory

Graphics

  • FIXME: summarize #6184
  • Centering of contour and density plots (Jason Grout) -- The following example shows a "spotlight" on the origin:
    sage: x, y = var('x,y')
    sage: density_plot(1/(x^10+y^10), (x, -10, 10), (y, -10, 10))

spotlight.png

  • This plots concentric circles centered at the origin:
    sage: x, y = var('x,y')
    sage: contour_plot(x^2 + y^2 - 2, (x,-1,1), (y,-1,1)).show(aspect_ratio=1)

concentric-circles.png

  • The following example plots a disk centered at the origin:
    sage: x, y = var('x,y')
    sage: region_plot(x^2 + y^2 < 1, (x,-1,1), (y,-1,1)).show(aspect_ratio=1)

disk.png

Group Theory

Interfaces

  • Improving the GAP interface by pre-compiling certain regular expressions (Simon King) -- The speed-up in the GAP interface is now up to 37% faster than previously. The following timing statistics were obtained using the machine sage.math:
    # BEFORE
    
    sage: G = SymmetricGroup(7)
    sage: g = G._gap_()
    sage: l = g.Elements()
    sage: time L = [gap.eval(l.name() + '[%d]^2' % (i)) for i in xrange(1, 7.factorial() + 1)]
    CPU times: user 1.90 s, sys: 0.16 s, total: 2.06 s
    Wall time: 2.08 s
    
    
    # AFTER
    
    sage: G = SymmetricGroup(7)
    sage: g = G._gap_()
    sage: l = g.Elements()
    sage: time L = [gap.eval(l.name() + '[%d]^2' % (i)) for i in xrange(1, 7.factorial() + 1)]
    CPU times: user 1.07 s, sys: 0.22 s, total: 1.29 s
    Wall time: 1.31 s

Linear Algebra

Miscellaneous

  • FIXME: summarize #5967
  • FIXME: summarize #5483
  • FIXME: summarize #6139

Modular Forms

  • FIXME: summarize #5995

Notebook

  • FIXME: summarize #4575
  • FIXME: summarize #5895

Number Theory

  • FIXME: summarize #133
  • FIXME: summarize #6021
  • FIXME: summarize #6206

Numerical

  • FIXME: summarize #5827

Packages

  • FIXME: summarize #5840
  • FIXME: summarize #6173
  • FIXME: summarize #5817
  • FIXME: summarize #6156
  • FIXME: summarize #6169
  • FIXME: summarize #6209
  • FIXME: summarize #6219

P-adics

Quadratic Forms

Symbolics

  • FIXME: summarize #6144
  • FIXME: summarize #6194

Topology

  • FIXME: summarize #6141

ReleaseTours/sage-4.0.1 (last edited 2019-11-14 21:01:15 by chapoton)