public MyMatrix inverseLU(MyMatrix m)

As an AI language model, I don't have access to the previous context or variables. However, here is a possible implementation of the `inverseLU` method for a square matrix `m` using LU decomposition:

public MyMatrix inverseLU(MyMatrix m) {
    int n = m.getRows();
    // Check if matrix is square
    if (n != m.getCols()) {
        throw new IllegalArgumentException("Matrix must be square");
    }
    // Compute LU decomposition
    MyMatrix[] lu = luDecomposition(m);
    MyMatrix L = lu[0];
    MyMatrix U = lu[1];
    // Solve L*Y = I for Y using forward substitution
    MyMatrix Y = new MyMatrix(n, n);
    for (int j = 0; j < n; j++) {
        MyVector e = new MyVector(n);
        e.set(j, 1.0);
        for (int i = 0; i < n; i++) {
            double sum = 0.0;
            for (int k = 0; k < i; k++) {
                sum += L.get(i, k) * Y.get(k, j);
            }
            Y.set(i, j, e.get(i) - sum);
        }
    }
    // Solve U*X = Y for X using backward substitution
    MyMatrix X = new MyMatrix(n, n);
    for (int j = 0; j < n; j++) {
        for (int i = n - 1; i >= 0; i--) {
            double sum = 0.0;
            for (int k = i + 1; k < n; k++) {
                sum += U.get(i, k) * X.get(k, j);
            }
            X.set(i, j, (Y.get(i, j) - sum) / U.get(i, i));
        }
    }
    return X;
}

private MyMatrix[] luDecomposition(MyMatrix m) {
    int n = m.getRows();
    MyMatrix L = new MyMatrix(n, n);
    MyMatrix U = new MyMatrix(n, n);
    for (int j = 0; j < n; j++) {
        // Compute U[j][k] for k >= j
        for (int k = j; k < n; k++) {
            double sum = 0.0;
            for (int i = 0; i < j; i++) {
                sum += L.get(k, i) * U.get(i, j);
            }
            U.set(k, j, m.get(k, j) - sum);
        }
        // Compute L[i][j] for i > j
        for (int i = j + 1; i < n; i++) {
            double sum = 0.0;
            for (int k = 0; k < j; k++) {
                sum += L.get(i, k) * U.get(k, j);
            }
            L.set(i, j, (m.get(i, j) - sum) / U.get(j, j));
        }
        // Set L[j][j] to 1.0
        L.set(j, j, 1.0);
    }
    return new MyMatrix[] { L, U };
}

The `inverseLU` method first checks if the input matrix is square, then computes its LU decomposition using the `luDecomposition` method. It then solves the systems of equations L*Y = I and U*X = Y for Y and X using forward and backward substitution, respectively, where I is the identity matrix. Finally, it returns the inverse matrix X.

The `luDecomposition` method computes the LU decomposition of a matrix using Gaussian elimination with partial pivoting. It returns a two-element array containing the lower triangular matrix L and the upper triangular matrix U.