As I See Tech

CVS is coordinate-wise in many ways. However, some operations are challenging. Performing CVS-to-CVS multiplication is possible but with a catch (at least as of this writing). CVS's own rule about keeping distinct non-zero values and aligning their lists of linear indexes creates a challenge when performing operations that can create zeroes or repeating values.

CVS is coordinate-wise in many ways. However, some operations are challenging. Performing CVS-to-CVS addition/subtraction is possible but with a catch (at least as of this writing). CVS's own rule about keeping distinct non-zero values and aligning their lists of linear indexes creates a challenge when performing operations that can create zeroes or repeating values.

The compressed value storage (CVS) is new. I say that because I can't find materials about it or something similar to it online. Or perhaps I am searching with the wrong keywords. Basically, my problem is that there is not much to find about how CVS can be used in matrix operations. So, for now, I have to figure things out on my own.

My current study is to add more features/methods for the compressed value storage (CVS) format. CVS is a lossless compression format for sparse matrices. A lot of things are easier with CVS than CRS/CCS. Let's look at MatrixScalarProduct() and MatrixScalarProductInPlace().

My current study is to add more features/methods for the compressed value storage (CVS) format. CVS is a lossless compression format for sparse matrices. A lot of things are easier with CVS than CRS/CCS. Let's look at Transpose(), SetLinearIndexMode() and TransposeToNewCVS().