https://github.com/cschen1205/cs-linear-algebra
C# implementation of linear algebra and matrix manipulation
https://github.com/cschen1205/cs-linear-algebra
linear-algebra matrix
Last synced: 30 days ago
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C# implementation of linear algebra and matrix manipulation
- Host: GitHub
- URL: https://github.com/cschen1205/cs-linear-algebra
- Owner: cschen1205
- License: mit
- Created: 2017-05-07T00:57:58.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-06-18T06:18:21.000Z (almost 8 years ago)
- Last Synced: 2025-01-29T17:16:07.063Z (3 months ago)
- Topics: linear-algebra, matrix
- Language: C#
- Size: 37.1 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# cs-linear-algebra
C# implementation of linear algebra and matrix manipulation. The cs-matrix library is writen in .NET Core
# Install
```bash
Install-Package cs-matrix
```
# Usage
### Create and update matrix
The code below shows how to create a matrix from a two-dimension array
```cs
double[][] Data = new double[][]{
new double[] { 12, -51, 4},
new double[] { 6, 167, -68},
new double[] { -4, 24, -41}
};
SparseMatrix A = new SparseMatrix(Data);
```
The code below shows how to create a sparse matrix with 3 rows and 3 columns and has cell(1, 1) = 1and other cells equal to 0
```cs
SparseMatrix A = new SparseMatrix(3, 3);
A[1, 1] = 1;
```
### Matrix Factorization and Inversion
The code below shows how to SVD decomposition
```cs
double[][] Data = new double[][]{
new double[] { 12, -51, 4},
new double[] { 6, 167, -68},
new double[] { -4, 24, -41}
};
SparseMatrix A = new SparseMatrix(Data);
IMatrix Sigma, U, Vstar;
SVD.Factorize(A, out U, out Sigma, out Vstar);
```
The code below shows how to use SVD for matrix inversion
```cs
double[][] Data = new double[][]{
new double[] { 12, -51, 4},
new double[] { 6, 167, -68},
new double[] { -4, 24, -41}
};
SparseMatrix A = new SparseMatrix(Data);
IMatrix Ainv = SVDSolver.Invert(A);
```
The code below shows how to QR decomposition
```cs
double[][] Data = new double[][]{
new double[] { 12, -51, 4},
new double[] { 6, 167, -68},
new double[] { -4, 24, -41}
};
SparseMatrix A = new SparseMatrix(Data);
IMatrix Sigma, U, Vstar;
QR.Factorize(A, out U, out Sigma, out Vstar);
```
The code below shows how to use QR for matrix inversion
```cs
double[][] Data = new double[][]{
new double[] { 12, -51, 4},
new double[] { 6, 167, -68},
new double[] { -4, 24, -41}
};
SparseMatrix A = new SparseMatrix(Data);
IMatrix Ainv = QRSolver.Invert(A);
```
<<<<<<< HEAD
The code below shows how to Cholesky decomposition
```cs
=======
The code below shows how to do symmetric matrix inversion using eigen vector decomposition:
```cs
>>>>>>> 54a732fc26f15cf888a7c97f8b4ffbb2a3f4cbea
double[][] Data = new double[][]{
new double[] { 12, -51, 4},
new double[] { 6, 167, -68},
new double[] { -4, 24, -41}
};
SparseMatrix A = new SparseMatrix(Data);
<<<<<<< HEAD
IMatrix Sigma, U, Vstar;
Cholesky.Factorize(A, out U, out Sigma, out Vstar);
```
The code below shows how to use Cholesky for matrix inversion
```cs
double[][] Data = new double[][]{
new double[] { 12, -51, 4},
new double[] { 6, 167, -68},
new double[] { -4, 24, -41}
};
SparseMatrix A = new SparseMatrix(Data);
IMatrix Ainv = CholeskySolver.Invert(A);
```
### Solving Linear System
The code below shows how to solve a set of equations in a linear system, A * x = b:
```cs
IVector x = new SparseVector(new double[] { 2, 4, 1 });
IVector b = A.Multiply(x);
IVector x_pi = QRSolver.Solve(A, b); // solve for x = b \ A using QR factorization
IVector x_pi = CholeskySolver.Solve(A, b); // solve for x = b \ A using Cholesky factorization
IVector x_pi = SVDSolver.Solve(A, b); // solve for x = b \ A using SVD factorization
```