references
https://groups.google.com/forum/#!msg/julia-users/Ui977brdzAU/u4rWiDeJv-MJ
https://github.com/andrewcooke/IntModN.jl
On Tuesday, 25 March 2014 20:19:32 UTC-3, andrew cooke wrote:
>
>
> something like this does work:
>
> julia> using IntModN
>
> julia> a = [GF2(1) GF2(1); GF2(0) GF2(1)]
> 2x2 Array{ZField{2,Int64},2}:
> 1 1
> 0 1
>
> ulia> b = inv(a)
> 2x2 Array{ZField{2,Int64},2}:
> 1 1
> 0 1
>
> julia> b * a
> 2x2 Array{ZField{2,Int64},2}:
> 1 0
> 0 1
>
>
> that's all GF(2), not integers.
>
> i do subclass Number (i can't remember why, but it's necessary). i also
> have promotion *from* Int, so that you can add and compare against constant
> integers in the code (typically 0 and 1).
>
> andrew
>
>
> On Tuesday, 25 March 2014 13:18:06 UTC-3, Andreas Noack Jensen wrote:
>>
>> Okay. Now I understand what is going on. I would like something like this
>> to work. I'll have to go now, but will think about what to do.
>>
>>
>> 2014-03-25 16:40 GMT+01:00 James Crist <[email protected]>:
>>
>>> I just realized that my test case is obviously singular, so not
>>> invertible. But I don't think it even gets to the actual inversion code
>>> before erroring, so I doubt that's the problem.
>>>
>>> -Jim
>>>
>>>
>>> On Tuesday, March 25, 2014 10:38:45 AM UTC-5, James Crist wrote:
>>>>
>>>> Here's the gist: https://gist.github.com/jcrist/ad663d6bdc4d82896176
>>>>
>>>> I tried to simplify everything down to just the bare essentials, but
>>>> there may be something I missed. Gives the same error as it did in the full
>>>> code though, so I think I got it all.
>>>>
>>>> I'm running version 0.2.0.
>>>>
>>>> Thanks,
>>>>
>>>> -Jim
>>>>
>>>> On Tuesday, March 25, 2014 10:21:10 AM UTC-5, Andreas Noack Jensen
>>>> wrote:
>>>>>
>>>>> A gist would be helpful. By the way, which version of Julia are you
>>>>> running?
>>>>>
>>>>>
>>>>> 2014-03-25 16:19 GMT+01:00 James Crist <[email protected]>:
>>>>>
>>>>>> I'm probably not. New to this language, still figuring things out.
>>>>>> The matrix type seems to be inferred correctly though. I'll put a gist
>>>>>> up
>>>>>> in a bit to try and get some more relevant feedback.
>>>>>>
>>>>>>
>>>>>> On Tuesday, March 25, 2014 9:54:53 AM UTC-5, Andreas Noack Jensen
>>>>>> wrote:
>>>>>>
>>>>>>> I don't think you are right about LAPACK. The code tries to promote
>>>>>>> to a type which is stable under lu factorizing which is the
>>>>>>> intermediate
>>>>>>> step in the calculation. The problem could be that your matrix type is
>>>>>>> not
>>>>>>> inferred correctly. Please try to let your type by subtype of Number
>>>>>>> and
>>>>>>> then define your matrix by
>>>>>>>
>>>>>>> a = Mytype[mytype(1) mytype(2); mytype(3) mytype(4)]
>>>>>>>
>>>>>>> and see if it works.
>>>>>>>
>>>>>>>
>>>>>>> 2014-03-25 15:29 GMT+01:00 James Crist <[email protected]>:
>>>>>>>
>>>>>>> Yeah, I get a "ERROR: no method Triangular{..." error, because my
>>>>>>>> type doesn't subtype Number. If I do subtype number, then it wants a
>>>>>>>> conversion function to convert it to a float, so it can use the LAPACK
>>>>>>>> routines.
>>>>>>>>
>>>>>>>> -Jim
>>>>>>>>
>>>>>>>>
>>>>>>>> On Tuesday, March 25, 2014 9:22:29 AM UTC-5, Andreas Noack Jensen
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Have you tried to invert it? Maybe it works already. There is a
>>>>>>>>> generic inv in base/linalg/generic.jl. You'll have to define a one
>>>>>>>>> method
>>>>>>>>> for you type and maybe also a zero method.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> 2014-03-25 15:14 GMT+01:00 James Crist <[email protected]>:
>>>>>>>>>
>>>>>>>>> I have a type I've defined. It's not a number, but it has all
>>>>>>>>>> arithmetic operations defined for it. Is there a way to calculate
>>>>>>>>>> the
>>>>>>>>>> inverse of a matrix of a user defined type? For example, if I was to
>>>>>>>>>> define:
>>>>>>>>>>
>>>>>>>>>> a = [mytype(1) mytype(2); mytype(3) mytype(4)]
>>>>>>>>>> b = inv(a)
>>>>>>>>>>
>>>>>>>>>> Looking through base, there doesn't seem to be a way to find
>>>>>>>>>> inverses of non-numeric matrices (although I may be missing it). For
>>>>>>>>>> my
>>>>>>>>>> case, even a simple algorithm that only works well for small
>>>>>>>>>> matrices
>>>>>>>>>> (<10x10) would be more than sufficient. If a way for doing this
>>>>>>>>>> doesn't
>>>>>>>>>> currently exist, I'll probably try to roll my own.
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> Med venlig hilsen
>>>>>>>>>
>>>>>>>>> Andreas Noack Jensen
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Med venlig hilsen
>>>>>>>
>>>>>>> Andreas Noack Jensen
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Med venlig hilsen
>>>>>
>>>>> Andreas Noack Jensen
>>>>>
>>>>
>>
>>
>> --
>> Med venlig hilsen
>>
>> Andreas Noack Jensen
>>
>
