Re: [julia-users] Re: build-in function to find inverse of a matrix
in the REPL, would it be reasonable to have ?? be able to do an apropos() search? Cameron On Fri, Jul 18, 2014 at 4:40 PM, Viral Shah vi...@mayin.org wrote: I think that most new users are unlikely to know about apropos. Perhaps we should put it in the julia banner. We can say something like: Type help() for function usage or apropos() to search the documentation. apropos() could then just print a message about how to use it, just like help() does. -viral On Wednesday, July 16, 2014 7:35:44 PM UTC+5:30, Hans W Borchers wrote: But apropos does find it: julia apropos(matrix inverse) INFO: Loading help data... Base.inv(M) On Wednesday, July 16, 2014 2:59:05 PM UTC+2, Tomas Lycken wrote: `inv` will do that for you. http://docs.julialang. org/en/latest/stdlib/linalg/#Base.inv It's a little unfortunate that the help text is Matrix inverse, yet searching for that exact phrase yields no results at all. Is it possible to make documentation search be full-text for help text as well? // T On Wednesday, July 16, 2014 2:39:53 PM UTC+2, Alan Chan wrote: sorry if it's a stupid question. But I cannot find one in the online doc. Thanks
Re: [julia-users] Re: build-in function to find inverse of a matrix
+1. For some reason my fingers always get tied up when typing apropos
Re: [julia-users] Re: build-in function to find inverse of a matrix
There's one complication here, which is that a single ? already changes the mode of the REPL into help mode. So this would have to be a mode only triggerable from inside of help mode. -- John On Jul 21, 2014, at 6:47 PM, Ethan Anderes ethanande...@gmail.com wrote: +1. For some reason my fingers always get tied up when typing apropos
Re: [julia-users] Re: build-in function to find inverse of a matrix
Would it be crazy to make ? just do apropos? Interestingly, I was just thinking of making ;; trigger sticky shell mode where you have to actively escape to get back to julia mode. On Mon, Jul 21, 2014 at 6:48 PM, John Myles White johnmyleswh...@gmail.com wrote: There's one complication here, which is that a single ? already changes the mode of the REPL into help mode. So this would have to be a mode only triggerable from inside of help mode. -- John On Jul 21, 2014, at 6:47 PM, Ethan Anderes ethanande...@gmail.com wrote: +1. For some reason my fingers always get tied up when typing apropos
Re: [julia-users] Re: build-in function to find inverse of a matrix
Indeed it is one of the first things one learns in numerical lectures that one has to avoid explicitely calculating an inverse matrix. Still, I think that there various small problems in geometry where I don't see an issue of invering a 2x2 or 3x3 matrix. It depends, as so often, a lot on the context. When considering not so well posed problems it is quite essential to take regularization into account. A simple x = A\b would not produce satisfying results in those cases. Am Donnerstag, 17. Juli 2014 06:25:27 UTC+2 schrieb Stefan Karpinski: It's a bit of numerical computing lore that inv is bad – both for performance and for numerical accuracy. It turns out it may not be so bad http://arxiv.org/pdf/1201.6035v1.pdf after all, but everyone is still kind of wary of it and there are often better ways to solve problems where inv would be the naive way to do it. On Wed, Jul 16, 2014 at 3:59 PM, Alan Chan szelo...@gmail.com javascript: wrote: any reason of avoiding inv?
Re: [julia-users] Re: build-in function to find inverse of a matrix
Hello colleague, On Thursday, July 17, 2014 6:25:27 AM UTC+2, Stefan Karpinski wrote: It's a bit of numerical computing lore that inv is bad – both for performance and for numerical accuracy. It turns out it may not be so bad http://arxiv.org/pdf/1201.6035v1.pdf after all, but everyone is still kind of wary of it and there are often better ways to solve problems where inv would be the naive way to do it. i scanned the paper and follows the fashion that people proof numerical assumptions by running matlab experiments ... so i cannot take this seriously. The paper has a point, that if you have random matrix input the method to get Ax = b is -using recent FP implementation- not so influential, by missing the point, that in random input you are not really interested in the output... For real world problems in evaluating ODEs or PDEs where you are especially interested in the result and in ill-conditioned systems, because the impact to the real world would have a price tag (at least, or fatal consequences), i still prefer the conservative solution (of factorisation) which btw (cite from paper): Computing the inverse requires more arithmetic operations than computing an LU factorization.
Re: [julia-users] Re: build-in function to find inverse of a matrix
If the inverse of a matrix is your final result, rather than some intermediate calculation, it might still be possible to use \ (and, at least in some cases, advantageous): julia A = rand(10,10) julia @time inv(A) elapsed time: 0.002185216 seconds (21336 bytes allocated) julia @time inv(A) elapsed time: 0.000202789 seconds (7536 bytes allocated) julia @time A\eye(10) elapsed time: 0.000117989 seconds (3280 bytes allocated) julia @time A\eye(10) elapsed time: 0.000117989 seconds (3280 bytes allocated) I show the timings twice to illustrate the kind of weird behavior that the second time inv(A) is called, it’s 10x faster and allocates only a third of the memory (which is from the third time on consistent). These measurements are after precompilation (using different matrices of the same size, i.e. inv(rand(10,10)) and rand(10,10)\eye(10) to eliminate any caching or other magic), so it’s not JIT-ting. Anyone got any ideas here? Also, the results aren’t exactly equal: inv(A) == A\eye(10) returns false. However, that’s just floating point inaccuracies - all(map(isapprox, inv(A), A\eye(10)) returns true. I can’t vouch for which result is closer to the mathematically exact correct matrix inverse - it probably depends on the input. // T On Thursday, July 17, 2014 10:11:10 AM UTC+2, Tobias Knopp wrote: Indeed it is one of the first things one learns in numerical lectures that one has to avoid explicitely calculating an inverse matrix. Still, I think that there various small problems in geometry where I don't see an issue of invering a 2x2 or 3x3 matrix. It depends, as so often, a lot on the context. When considering not so well posed problems it is quite essential to take regularization into account. A simple x = A\b would not produce satisfying results in those cases. Am Donnerstag, 17. Juli 2014 06:25:27 UTC+2 schrieb Stefan Karpinski: It's a bit of numerical computing lore that inv is bad – both for performance and for numerical accuracy. It turns out it may not be so bad http://arxiv.org/pdf/1201.6035v1.pdf after all, but everyone is still kind of wary of it and there are often better ways to solve problems where inv would be the naive way to do it. On Wed, Jul 16, 2014 at 3:59 PM, Alan Chan szelo...@gmail.com wrote: any reason of avoiding inv?
Re: [julia-users] Re: build-in function to find inverse of a matrix
I agree. What I meant is that since inv is avoided, it's not used nor well referenced in search engines or on this list. :)
Re: [julia-users] Re: build-in function to find inverse of a matrix
It's a bit of numerical computing lore that inv is bad – both for performance and for numerical accuracy. It turns out it may not be so bad http://arxiv.org/pdf/1201.6035v1.pdf after all, but everyone is still kind of wary of it and there are often better ways to solve problems where inv would be the naive way to do it. On Wed, Jul 16, 2014 at 3:59 PM, Alan Chan szelok.c...@gmail.com wrote: any reason of avoiding inv?
