semvec/pypi-packages huggingface dataset:
Neux 1.1.0
Neux, a package intended to make all of the python users life easier

Howdie Programmers,
Neux is the library to help you make your python life easier.

Need Help?

If you are an experienced programmer You guys know you can use help() function in python
If you are a beginner be sure to keep checking the Github repo for Neux and I may upload YouTube videos explaining how Neux works
The docs will be released after the third major update because all the important things will be added by then.


Issues: 
Want to talk create an issue on github or send an email to the given email located somewhere in the repo.
Can't Find Email, Here it is: saadyarkhan11@gmail.com
I am on Hangouts, but not Discord.
Thanks for Downloading
skyline-solver 0.0.1
A Skyline solver for linear sets
Requires Python: >=3

# Skyline Solver

Skyline Solver is a Python library for solving a linear set with symmetric and [skyline matrix](https://en.wikipedia.org/wiki/Skyline_matrix) of coefficients. It uses [Cholesky decomposition](https://en.wikipedia.org/wiki/Cholesky_decomposition) for matrix factorization. 

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Skyline Solver.

```bash
pip install skyline_solver
``` 

For installation from the source:

```bash
python setup.py install
```

## Usage
The following example has been used for usage and validation.
This is a combination of **Example 8.4** and **8.5** of [*Finite Element Procedures*](http://web.mit.edu/kjb/www/Books/FEP_2nd_Edition_4th_Printing.pdf) by K.J. Bathe. 


```python
from skyline_solver import skyline_solver

# Skyline vector of a symmetric matrix
# 1-based indexing must be used
m=[1,1,2,3,1]

# The matrix initialization
sk=skyline_solver(m)

# The matrix is initialized with zero values by default. 
# However, the initial values can be set using the set method:
sk.set(0.0)


print("The rank is:", sk.rank)
print("The number of non-zero values is:", sk.nnz)

# Adding values according to 1-based indexing scheme.
sk.add_value(1,1,2.0)
sk.add_value(1,2,-2.0)
sk.add_value(1,5,-1.0)

sk.add_value(2,2,3.0)
sk.add_value(2,3,-2.0)

sk.add_value(3,3,5.0)
sk.add_value(3,4,-3.0)

sk.add_value(4,4,10.0)
sk.add_value(4,5,4.0)

sk.add_value(5,5,10.0)

print("The matrix in the dense format is:")
print(sk.to_dense())

# The factorization is done inplace.
sk.decompose()

print("The factorized matrix in the dense format is:")
print(sk.to_dense())

# Constants or load vectors 
f=[0,1,0,0,0]

# Providing in place solution
f = sk.solve(f)

print("The solution is:")
print(f) 
print("The solution from Finite Element Procedures By K. J. Bathe (Example 8.5) is:")
print([636, 619, 292, 74, 34])

```

## License
[MIT](https://choosealicense.com/licenses/mit/)