TheAlgorithms/Java · error · IllegalArgumentException

Matrix A and vector b dimensions do not match.

Error message

Matrix A and vector b dimensions do not match.

What it means

Thrown by ChebyshevIteration.validateInputs when the dimension of vector b does not match the dimension n of matrix A (a.length). The linear system Ax = b requires b to be in R^n where A is n x n. A dimension mismatch means the matrix-vector multiply Av in the residual computation (b - Ax) would fail or produce a meaningless result.

Source

Thrown at src/main/java/com/thealgorithms/maths/ChebyshevIteration.java:99

            alphaPrev = alpha;
        }

        return x; // Return best guess after maxIterations
    }

    /**
     * Validates the inputs for the Chebyshev solver.
     */
    private static void validateInputs(double[][] a, double[] b, double[] x0, double minEigenvalue, double maxEigenvalue, int maxIterations, double tolerance) {
        int n = a.length;
        if (n == 0) {
            throw new IllegalArgumentException("Matrix A cannot be empty.");
        }
        if (n != a[0].length) {
            throw new IllegalArgumentException("Matrix A must be square.");
        }
        if (n != b.length) {
            throw new IllegalArgumentException("Matrix A and vector b dimensions do not match.");
        }
        if (n != x0.length) {
            throw new IllegalArgumentException("Matrix A and vector x0 dimensions do not match.");
        }
        if (minEigenvalue <= 0) {
            throw new IllegalArgumentException("Smallest eigenvalue must be positive (matrix must be positive-definite).");
        }
        if (maxEigenvalue <= minEigenvalue) {
            throw new IllegalArgumentException("Max eigenvalue must be strictly greater than min eigenvalue.");
        }
        if (maxIterations <= 0) {
            throw new IllegalArgumentException("Max iterations must be positive.");
        }
        if (tolerance <= 0) {
            throw new IllegalArgumentException("Tolerance must be positive.");
        }
    }

View on GitHub (pinned to fdfb9a395b)

Solutions

1. Ensure b.length == a.length before calling solve.
2. Construct b and A from the same dimensional context or validate both against a shared n value.
3. Add a precondition: assert b.length == a.length or throw a descriptive error before the call.

Example fix

// before
ChebyshevIteration.solve(new double[3][3], new double[2], new double[3], 1, 2, 100, 1e-6);
// throws 'Matrix A and vector b dimensions do not match.'

// after
int n = a.length;
if (b.length == n) {
    double[] x = ChebyshevIteration.solve(a, b, x0, minEig, maxEig, maxIter, tol);
}

// Validate b dimension matches A before calling solve
int n = a.length;
if (b == null || b.length != n) {
    throw new IllegalArgumentException("b.length must equal A's dimension (" + n + ")");
}
double[] x = ChebyshevIteration.solve(a, b, x0, minEig, maxEig, maxIter, tol);

static boolean dimensionsMatch(double[][] a, double[] b) {
    return a != null && a.length > 0 && b != null && b.length == a.length;
}

When it happens

Trigger: Calling solve with a 3x3 matrix A but b.length == 2 or b.length == 4. For example: solve(new double[3][3], new double[2], new double[3], ...) where b has 2 elements but A has 3 rows.

Common situations: Assembling b from a different data source than A, leading to mismatched dimensions. A preprocessing step that resized b (e.g., adding or removing a boundary condition) without a corresponding change to A. Copy-paste errors where b was built for a different problem size.

Related errors

• Matrix A and vector x0 dimensions do not match.
• Matrix A cannot be empty.
• Matrix A must be square.
• Smallest eigenvalue must be positive (matrix must be positiv
• Max eigenvalue must be strictly greater than min eigenvalue.

AI-assisted analysis of TheAlgorithms/Java@fdfb9a395b (2026-08-13). Data as JSON: /api/errors/b74ec7675fa73dae. Report an issue: GitHub.