MatrixX

MatrixX is a Java library designed to provide a robust and intuitive API for matrix manipulation and algebra.

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Features

Matrix Creation

• Identity Matrix: Generate identity matrices of any size.
• Zero Matrix: Create zero matrices.
• From Array: Instantiate matrices directly from 2D arrays.
• Static Generators: Scaffold matrices quickly using ones(), constant(), and random().
• String Parsing: Instantiate matrices intuitively from MATLAB-style strings (e.g., "[1 2; 3 4]").

Print / Display

• Easily print matrices in a readable format.

Matrix Operations

• Addition: Element-wise matrix addition.
• Subtraction: Element-wise matrix subtraction.
• Multiplication: Standard matrix multiplication.
• Hadamard Product: Element-wise matrix multiplication (hadamardMultiply()).
• Dot Product: Perform inner dot products on vector constraints (dotProduct()).
• Map Function: Pass functional Lambda equations (DoubleUnaryOperator) over every matrix item (map()).

Utility Operations

• Transpose: Get the transpose of a matrix.
• Scalar Multiplication: Multiply a matrix by a scalar.
• Equality Check: Compare matrices for equality.
• Matrix Power (Aⁿ): Raise a matrix to an integer power.
• Determinant: Compute determinants (supports 2x2, 3x3, and recursive for larger matrices).
• Inverse: Calculate the inverse of a matrix (if it exists).

Algebraic Tools

• Linear Solver: Analytically solve linear equations of the form $A \cdot X = B$ using solve().
• Decompositions:
  • LU: Decompose a matrix into lower and upper triangular matrices.
  • Cholesky: Robust decomposition for symmetric, positive-definite matrices.
  • QR: Gram-Schmidt orthogonalization yielding $Q$ and $R$ factors.
• Eigen Solvers: Analytically process real Eigenvalues and cross-product Eigenvectors for 2x2 and 3x3 matrices using eigen().
• Gauss-Jordan Elimination: Perform row reduction for solving systems or finding inverses.
• Rank of Matrix: Compute the rank.
• Reduced Row Echelon Form (RREF): Reduce matrices to their canonical form.

Special Matrix Types Support

• Diagonal Matrices
• Symmetric Matrices
• Identity Matrices
• Orthogonal Matrices

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Installation

Step 1. Add the JitPack repository

Add the JitPack repository to your pom.xml file:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

Step 2. Add the dependency

Add MatrixX to your project dependencies:

<dependency>
    <groupId>com.github.ayoubMah</groupId>
    <artifactId>MatrixX</artifactId>
    <version>v0.2</version>
</dependency>

Alternatively, you can clone the repository and build it locally using Maven:

git clone https://github.com/ayoubMah/MatrixX.git
cd MatrixX
mvn clean install

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Basic Usage

import matrixx.Matrix;

public class Example {
    public static void main(String[] args) {
        // 1. Matrix Creation
        double[][] data = {
            {1, 2, 3},
            {4, 5, 6},
            {7, 8, 9}
        };
        Matrix A = new Matrix(data);
        Matrix I = Matrix.identity(3);

        // 2. Arithmetic Operations
        Matrix sum = A.add(I);
        System.out.println("A + I =");
        sum.print();

        // 3. Advanced Algebraic Tools
        Matrix B = new Matrix(new double[][]{
            {4, 7}, 
            {2, 6}
        });
        
        System.out.println("Determinant of B: " + B.determinant());
        
        System.out.println("Inverse of B:");
        B.inverse().print();
        
        // 4. Matrix Decompositions (LU Factorization)
        Matrix.LUPair lu = B.lu();
        System.out.println("L Matrix:");
        lu.L().print();
        System.out.println("U Matrix:");
        lu.U().print();
        
        // 5. Matrix Typed Validations
        System.out.println("Is I a Diagonal Matrix? " + I.isDiagonal());
        
        // 6. Linear Solvers & MATLAB String Parsing
        Matrix equations = Matrix.fromMatlab("[1 2; 3 4]");
        Matrix constants = Matrix.fromMatlab("[5; 11]");
        Matrix solution = equations.solve(constants);
        
        System.out.println("Solution to linear system:");
        solution.print();
    }
}

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Roadmap & Future Development

MatrixX is under active development. We are pushing towards v0.4, targeting Float Matrices, Vector classes, and overall syntax polish!

To see what's planned for future releases—including Complex Numbers, Matrix Statistics, Singular Value Decomposition (SVD), and SIMD hardware acceleration—check out our detailed Development Roadmap.

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Contributing

PRs, feature requests, and bug reports are welcome :)

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Author

ayoubMah