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== Algorithmic/Automatic Differentiation == '''People:''' William Stein I never thought much of this topic, but there is [[http://www.euroscipy.org/talk/2045|a talk at Euroscipy]] suggesting it could be useful. Examples of what you could imagine doing: {{{ sage: f(x,y,z)=sqrt(x^3+y)/(x-y^3*z)^(3/2) sage: a = fast_callable(f,CDF) sage: timeit('a(2,3,4)') 625 loops, best of 3: 24.2 µs per loop sage: g = f.derivative(x,10) sage: a = fast_callable(g,CDF) sage: timeit('a(2,3,4)') 625 loops, best of 3: 1.21 ms per loop }}} I imagine that one could instead do: {{{ sage: f(x,y,z)=sqrt(x^3+y)/(x-y^3*z)^(3/2) sage: a = fast_callable(g,CDF) sage-fantasy: b = a.derivative(x,10) sage-fantasy: timeit('b(2,3,4)') 625 loops, best of 3: 24.2 µs per loop }}} This is just a random example I made up, but it's the sort of massive performance one might hope for from AD; the literature should have better examples. There are *tons* of potential Python libraries to consider, e.g., [[http://github.com/b45ch1/algopy|algopy]] and [[http://www.coin-or.org/CppAD/|CppAD]] (which has Python bindings). |
Sage Days 24 Coding Sprint Projects
This is a list of projects suitable for Sage Days 24. Feel free to add your favourite ideas/wishes, and to put your name down for something you're interested in (you'll need to get an account on the wiki to do this).
Kovacic's Algorithm
People: Burcin Erocal
Implement Kovacic's algorithm in Sage.
Hypergeometric Functions
People: Flavia Stan, Karen Kohl, Fredrik Johansson
Plural support
People: Burcin Erocal
Add support for Singular's noncommutative component Plural, finish #4539.
Locapal support
People: Burcin Erocal
There is experimental support for computing Groebner bases over certain localizations of operator algebras in Singular. See this presentation for more details. Support for arithmetic needs to be provided in Sage.
Parallel Integration
People: Burcin Erocal
Integrate Stefan Boettner's parallel integration code in Sage. There are several prerequisites for this, such as
algebraic function fields (transcendence degree > 1)
- differential rings/fields
- proper to_polynomial(), to_rational() functions for symbolic expressions
Algorithmic/Automatic Differentiation
People: William Stein
I never thought much of this topic, but there is a talk at Euroscipy suggesting it could be useful. Examples of what you could imagine doing:
sage: f(x,y,z)=sqrt(x^3+y)/(x-y^3*z)^(3/2)
sage: a = fast_callable(f,CDF)
sage: timeit('a(2,3,4)')
625 loops, best of 3: 24.2 µs per loop
sage: g = f.derivative(x,10)
sage: a = fast_callable(g,CDF)
sage: timeit('a(2,3,4)')
625 loops, best of 3: 1.21 ms per loopI imagine that one could instead do:
sage: f(x,y,z)=sqrt(x^3+y)/(x-y^3*z)^(3/2)
sage: a = fast_callable(g,CDF)
sage-fantasy: b = a.derivative(x,10)
sage-fantasy: timeit('b(2,3,4)')
625 loops, best of 3: 24.2 µs per loopThis is just a random example I made up, but it's the sort of massive performance one might hope for from AD; the literature should have better examples. There are *tons* of potential Python libraries to consider, e.g., algopy and CppAD (which has Python bindings).