TheAlgorithms/Python · error · ValueError

Number of initial values must be equal to number of rows in

Error message

Number of initial values must be equal to number of rows in coefficient matrix but received {len(init_val)} and {rows1}

What it means

Thrown by jacobi_iteration_method() when len(init_val) != rows1 — the initial guess vector does not have one entry per unknown. The iteration indexes x_old[i] for every row i; a shorter guess raises IndexError and a longer one indicates a wrong system, so the function validates the count upfront.

Source

Thrown at linear_algebra/jacobi_iteration_method.py:106

        raise ValueError(msg)

    if cols2 != 1:
        msg = f"Constant matrix must be nx1 but received {rows2}x{cols2}"
        raise ValueError(msg)

    if rows1 != rows2:
        msg = (
            "Coefficient and constant matrices dimensions must be nxn and nx1 but "
            f"received {rows1}x{cols1} and {rows2}x{cols2}"
        )
        raise ValueError(msg)

    if len(init_val) != rows1:
        msg = (
            "Number of initial values must be equal to number of rows in coefficient "
            f"matrix but received {len(init_val)} and {rows1}"
        )
        raise ValueError(msg)

    if iterations <= 0:
        raise ValueError("Iterations must be at least 1")

    table: NDArray[float64] = np.concatenate(
        (coefficient_matrix, constant_matrix), axis=1
    )

    rows, _cols = table.shape

    strictly_diagonally_dominant(table)

    """
    # Iterates the whole matrix for given number of times
    for _ in range(iterations):
        new_val = []
        for row in range(rows):
            temp = 0

View on GitHub (pinned to f5988cc097)

Solutions

1. Always derive the guess from the matrix: init_val = np.zeros(coefficient_matrix.shape[0]).
2. Build A, b, and init_val from the same n variable in one place.
3. Prefer letting the function's own default/zero start be used rather than hand-building guesses.

Example fix

# before
init_val = [0.0, 0.0]  # but A is 3x3

# after
init_val = np.zeros(coefficient_matrix.shape[0])

x0 = np.zeros(A.shape[0])  # derive guess length from the matrix

def is_matching_guess(A: np.ndarray, x0: np.ndarray) -> bool:
        return A.ndim == 2 and x0.shape[0] == A.shape[0]

try:
    x = jacobi_iteration_method(A, b, x0, iters)
except ValueError as e:
    if "initial values" in str(e):
        x = jacobi_iteration_method(A, b, np.zeros(A.shape[0]), iters)
    else:
        raise

When it happens

Trigger: Calling with init_val = [0, 0] for a 3x3 system, or passing a 1-D numpy array of a different length (e.g. built with np.zeros(n) where n was hardcoded or from a previous problem size).

Common situations: Reusing an initial guess across problems of different dimensionality; hardcoded np.zeros(3) copied from an example; n changed in the system builder but not in the guess construction.

Related errors

• Coefficient matrix dimensions must be nxn but received {rows
• Constant matrix must be nx1 but received {rows2}x{cols2}
• Coefficient and constant matrices dimensions must be nxn and
• Iterations must be at least 1
• Coefficient matrix is not strictly diagonally dominant

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/db472c4baba824e1. Report an issue: GitHub.