I think I understand, but is there an implementation of this technique that
can actually perform the linear algebra on a symbolic matrix at such
improved compute-time?
On Tuesday, October 9, 2018 at 1:58:04 PM UTC-6, Isuru Fernando wrote:
>
> First k-1 entries of the k th eigenvector for an
First k-1 entries of the k th eigenvector for an upper triangular matrix U
is U[:k-1,:k-1]^-1 @ U[:k-1,k], which is a triangular solve since
U[:k-1,:k-1] is a triangular matrix and it can be done in O(k^2) time.
Isuru
On Tue, Oct 9, 2018 at 1:27 PM Jacob Miner wrote:
> Isuru,
>
> I went into
Isuru,
I went into Heath's text to get your reference, and it helps layout the
method, but can you please clarify what you meant by 'triangular solves'?
Thank you.
On Tue, Oct 9, 2018, 10:45 Aaron Meurer wrote:
> Your matrix is far simpler than I had imagined (you should have
> mentioned that
Your matrix is far simpler than I had imagined (you should have
mentioned that it was triangular). I think as Isuru said we can likely
implement a faster method for triangular matrices. The eigenvalues
themselves (the diagonals) are already computed very quickly.
Aaron Meurer
On Tue, Oct 9, 2018
Hi,
For triangular matrices, it's straightforward to calculate eigenvectors.
You just need triangular solves. See Section 4.4.1 of Heath's Scientific
Computing 2nd Edition.
Isuru
On Tue, Oct 9, 2018 at 11:27 AM Jacob Miner wrote:
> I will show you a representation of the 7x7 form of my
I will show you a representation of the 7x7 form of my matrix, the 10x10
includes a couple additional elements, but has the same overall structure
and layout.
The key point is that the diagonal elements are differences of multiple
values, and each of these values occupies a certain element in
How sparse is the matrix, and what do the entries look like?
One thing that can help depending on what your matrix looks like is to
replace large subexpressions with symbols (if there are common
subexpressions, cse() can help with this). That way the simplification
algorithms don't get caught up
On Friday, July 10, 2015 at 3:07:17 PM UTC-6, Ondřej Čertík wrote:
>
> Hi,
>
> On Fri, Jul 10, 2015 at 7:30 AM, 刘金国 >
> wrote:
> > 4 x 4 is needed ~~
> > mathematica runs extremely fast for 4 x 4 matrix as it should be, but
> ...
>
> Can you post the Mathematica result? So that we know
4 x 4 is needed ~~
mathematica runs extremely fast for 4 x 4 matrix as it should be, but ...
在 2014年2月12日星期三 UTC+8上午5:40:19，Vinzent Steinberg写道：
On Monday, February 10, 2014 11:27:09 PM UTC-5, monde wilson wrote:
why eigenvectors very slow
what is the difference between numpy and sympy when
Hi,
On Fri, Jul 10, 2015 at 7:30 AM, 刘金国 cacate0...@gmail.com wrote:
4 x 4 is needed ~~
mathematica runs extremely fast for 4 x 4 matrix as it should be, but ...
Can you post the Mathematica result? So that we know what you are
trying to get and we can then help you get it with SymPy.
Ondrej
On Monday, February 10, 2014 11:27:09 PM UTC-5, monde wilson wrote:
why eigenvectors very slow
what is the difference between numpy and sympy when doing matrix
calculation
Sympy calculates eigenvectors symbolically (thus exactly), numpy calculates
them numerically using floating point
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