|Re: [eigen] Fast method for computing Gramian Matrix|
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- To: eigen@xxxxxxxxxxxxxxxxxxx
- Subject: Re: [eigen] Fast method for computing Gramian Matrix
- From: Jim Bosch <talljimbo@xxxxxxxxx>
- Date: Fri, 30 Mar 2012 17:20:26 -0400
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Is it necessary to do something like this to form the full dense matrix
when using SelfAdjointEigenSolver? I noticed that it doesn't have a
UpLo template parameter to tell it which triangle to use.
On 03/29/2012 06:55 PM, Gael Guennebaud wrote:
with a copy:
C = B.selfadjointView<Upper>();
or in-place using triangularView as a writing mask:
B.triangularView<StrictlyLower>() = B.adjoint();
On Fri, Mar 30, 2012 at 12:18 AM, Mark Borgerding<mark@xxxxxxxxxxxxxx> wrote:
On 03/29/2012 01:18 AM, Douglas Bates wrote:
On Thu, Mar 29, 2012 at 3:50 AM, Mark Borgerding<mark@xxxxxxxxxxxxxx>
(sorry for hitting send too soon)
What is the fastest way to compute MM' when M is wide?
The diagonal elements are the norm-squared of the rows of M. The result
Is there an optimized method in Eigen or otherwise?
I have used the rankUpdate method for a selfadjoint view to calculate
this. I assume that it is an efficient way to calculate the result
but I haven't done any timings.
MatrixXcd MMt(M.rows(), M.rows();
Thanks. That cut the time to about 80% of the full matrix product.
Is there an elegant way to recover the full, square self-adjoint matrix from
a SelfAdjointView ?
I've been adding the SA view to its own adjoint and then subtracting the