I am trying to profile the conjugated gradient method for Ax = b. A is a large sparse matrix. The conjugated gradient method could be code in a .m file. The method is fromwiki. But the speed of this two ways differs in a stable manner. The matlab...
Yelick. When cache blocking of sparse matrix vector multiply works and why[J]. Applicable Algebra in Engineering, Communication and Computing . 2007 (3)Rajesh Nishtala, Richard W. Vuduc, James W. Demmel, and Katherine A. Yelick. When cache blocking of sparse matrix vector multiply ...
I have a sparse matrix C(1500*1500) and d(1500*1) and constraint lb=zeros(1500,1), ub=ones(1500,1). I can solve this system of linear equations by C\d very easily, but in some cases due to the negative results, I can not use that anymore. I found that I can use "lsqli...
The problem of policy gradient methods is that they are extremely sensitive to the step size choice - if it is small the progress takes too long (most probably mainly due to the need of a second-order derivatives matrix); if it is large, there is a lot noise which significantly reduces ...
a decision matrix helps them pick the best course of action. With that knowledge, they develop an action plan and throughout the project update the RAID log to ensure they’re ready if the price spikes or materials are sparse. Here’s what this RAID log example looks after some of these...
We then learn locally weighted linear models on this neighborhood data to explain each of the classes in an interpretable way (see lime_base.py). Args: data_row: 1d numpy array or scipy.sparse matrix, corresponding to a row predict_fn: prediction function. For classifiers, this should be...
So, with L1 regularization you can end up with a sparse model - one with fewer parameters. In both cases the parameters of the L1 and L2 regularized models "shrink", but in the case of L1 regularization the shrinkage directly impacts the complexity (the number of parameters) of the model...
However, NumPy strongly recommends that you use the ndarray type because it is more flexible and because matrix will eventually be removed.In the rest of this section, you will get to know the major differences between MATLAB and NumPy arrays. You can go in-depth on how to use NumPy ...
If you are still using MATLAB 6.1, the only workaround for this problem is to perform large matrix multiplications in Double and then use the SPARSE function to change the matrix back to a sparse matrix.
As the kid in the Oracle's living room said in the Matrix, "There is no spoon". A UX that you find cumbersome and unintuitive does not make it "wrong". If others find that same UX to be intuitive and easy to use, that doesn't make it "right". ...