On Friday, June 2, 2017 at 6:33:12 AM UTC-6, Stan wrote:
>
> I asked this question before on ask.sagemath (
> https://ask.sagemath.org/question/37744/why-is-assume-so-slow/) but
> haven't found an answer yet, so perhaps someone here has an idea.
>
> Basically, declaring assumptions using `assume()` or within `var('x',
> domain='real') takes an awful lot of time, e.g.:
>
> variables = ['a_' + str(i) for i in srange(10)]
> for var1 in variables: var(var1, domain='real')
>
> If I increase the number of variables, the time needed increases more than
> proportionally. See
> https://cocalc.com/projects/34b4b62a-2621-47c8-9bda-cde3a855f995/files/test_assumptions.ipynb
>
>
> Does anyone know why this is and how this could be accelerated?
>
There's a good chance that this is supposed to slow down, since the
assumption data base probably needs to be checked for inconsistencies. As
the data base grows, this gets more expensive. It's a little excessive,
though. The amount of interaction with the maxima library increases too!
D=(SR.symbol("x"+str(i)) for i in range(10000)).next
%prun assume(D(), 'real')
245 0.048 0.000 0.051 0.000 maxima_lib.py:408(_eval_line)
85 0.002 0.000 0.002 0.000
maxima_lib.py:270(max_to_string)
24 0.001 0.000 0.001 0.000 {sage.symbolic.ring.var}
92 0.001 0.000 0.004 0.000 {repr}
3 0.001 0.000 0.011 0.004 {method 'simplify_factorial'
of 'sage.symbolic.expression.Expression' objects}
13 0.001 0.000 0.007 0.001
calculus.py:1957(symbolic_expression_from_maxima_string)
Doing it another time:
%prun assume(D(), 'real')
ncalls tottime percall cumtime percall filename:lineno(function)
372 0.074 0.000 0.079 0.000 maxima_lib.py:408(_eval_line)
118 0.003 0.000 0.003 0.000
maxima_lib.py:270(max_to_string)
48 0.002 0.000 0.002 0.000 {sage.symbolic.ring.var}
158 0.001 0.000 0.006 0.000 {repr}
24 0.001 0.000 0.008 0.000
calculus.py:1957(symbolic_expression_from_maxima_string)
2 0.001 0.000 0.089 0.044 {cmp}
151/35 0.001 0.000 0.003 0.000
expression_conversions.py:155(__call__)
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