I have developed a new method about mesh simplification on CGAL. But I don`t know which paper do the CGAL used? could you help me ?
In the User Manual of the mesh simplification package you find the sentence:
"The cost-driven method implemented in this package is mainly based on [4], [5], with contributions from [3], [2] and [1 ]."
so, please follow the links.
Related
I have not found the way to do it with cgal.
Please can you point me to some functions I have to use ?
This does not exist yet in CGAL but there is a draft implementation here.
I am trying to run some code which cannot be altered in functionality, but it would be great if I can somehow ignore the warnings or prevent them from being printed to the console, as it congests it and makes it unreadable. Thank you.
You can try tf.autograph.set_verbosity it's used to control how much info tensorflow logs as indicated in their docs for TF2.6
tf.autograph.set_verbosity(
level=0, alsologtostdout=False
)
Check also the answers here which covers solutions for multiple versions of tensorflow and python.
I've implemented my problem in IBM ILOG CPLEX Optimization Studio.
Now i would like to modify the objective function to be quadratic and solve the problem. However, it shows an Error 5002:objective is not convex.->problem can be solved to global optimality with solution target 3->.
I have read the user guide and manual and the different topics in stackoverflow, and I beleive version 12.8 can solve mixed-integer quadratic problems.
modify my objective from this
dexpr float overallcost[f in cars] = holdingTime[f];
to this
dexpr float overallcost[f in cars] = holdTime[f]*holdTime[f];
Error 5002:objective is not convex.->problem can be solved to global optimality with solution target 3->.
As suggested by the error message you should try setting the solution target parameter to 3 to force CPLEX to solve your model.
More details about this can be found in this chapter of the user manual and here is the documentation of the respective parameter.
To set this parameter in the IDE create/add a settings file to your project and then go to
Mathematical Programming -> General -> Type of solution to compute
and choose "Global optimal solution".
See
https://www.ibm.com/support/knowledgecenter/SSSA5P_12.9.0/ilog.odms.cplex.help/CPLEX/Parameters/topics/OptimalityTarget.html
In order to set that parameter in order to solve quadratic objectives.
In your model, you may add:
execute
{
cplex.optimalitytarget=3;
}
I'm interested to make some contributions to TensorFlow in future. I saw that some labels have "contributions welcome" and "triaged" at the same time. Can someone explain to me what "triaged" means?
https://github.com/tensorflow/tensorflow/labels/triaged
That's part of our internal process, to help us make sure we look through all of the Github issues that the community submits. Having the label is an indication that we've seen the issue, and usually means we've marked it as something we'd welcome help with, assigned it to a team member to be worked on, or taken some other action to handle it appropriately.
Does that help?
Currently I am trying to use Magma to do matrix operation on GPU, however, I found few documents about it. The only thing I can refer to is its testing program and the online generated document(here), which is not convenient to use. And the user guide seems outdated.
If you look here, getri and potri are supported.