"Oxberry, Geoffrey Malcolm" writes:
> I can’t think of one either. What is the origin of the algorithm PETSc
> uses for MatMultEqual, and why use this algorithm instead of
> Freivalds' algorithm? I realize you can probably use similar
> probabilistic arguments for the PETSc algorithm as those used
> On Aug 19, 2016, at 4:08 PM, Jed Brown wrote:
>
> "Oxberry, Geoffrey Malcolm" writes:
>> Something similar can be used for quadratics. Here you evaluate
>> the Hessian at two random points. You really want to calculate
>> the distance between the matrices. I did not find that method
>> in
"Oxberry, Geoffrey Malcolm" writes:
> Something similar can be used for quadratics. Here you evaluate
> the Hessian at two random points. You really want to calculate
> the distance between the matrices. I did not find that method
> in the PETSc documentation. Therefore, I would compare the
>
On Aug 19, 2016, at 4:13 AM, Munson, Todd
mailto:tmun...@mcs.anl.gov>> wrote:
Hi!
The only provable way to test for linearity of a function is using
code analysis.
A quick hack that should work okay is to evaluate the gradient at
two (or more) random points and calculate the distance between
Hi!
The only provable way to test for linearity of a function is using
code analysis.
A quick hack that should work okay is to evaluate the gradient at
two (or more) random points and calculate the distance between
the gradients. Note: this is not a good test for piecewise
linear functions, as
How does a numerical method test for linearity (or in general order of
nonlinearity) of an operator? By comparing function value with the
first two Taylor terms, within machine precision?
I suspect only code analysis ("reification", as in analysis of the
expression syntax tree) would be feasible an
