TheAlgorithms/Python · error · ValueError

Iterations must be at least 1

Error message

Iterations must be at least 1

What it means

Thrown by jacobi_iteration_method() when iterations <= 0. The routine performs exactly the requested number of fixed-point sweeps (it does not test convergence), so zero or negative iterations would return the initial guess unchanged and create the illusion of a solved system; the guard makes that a hard error.

Source

Thrown at linear_algebra/jacobi_iteration_method.py:109

        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
            for col in range(cols):
                if col == row:
                    denom = table[row][col]

View on GitHub (pinned to f5988cc097)

Solutions

1. Pass a positive iteration count (typically 100+ depending on needed accuracy).
2. Short-circuit at the caller: if iterations <= 0, skip the call or raise your own 'converged/invalid' error with context.
3. Do not use 0 as an 'auto' sentinel — pick an explicit large count.

Example fix

# before
jacobi_iteration_method(A, b, x0, remaining_iters)  # may be 0

# after
if remaining_iters <= 0:
    raise ValueError(f"non-positive iteration budget: {remaining_iters}")
jacobi_iteration_method(A, b, x0, remaining_iters)

if iterations <= 0:
    raise ValueError(f"iteration budget must be positive, got {iterations}")

def is_positive_iterations(n: int) -> bool:
    return isinstance(n, int) and n > 0

try:
    x = jacobi_iteration_method(A, b, x0, iters)
except ValueError as e:
    if "Iterations" in str(e):
        x = jacobi_iteration_method(A, b, x0, max(iters, 100))
    else:
        raise

When it happens

Trigger: Calling jacobi_iteration_method(A, b, init_val, 0) or with a negative iteration count; also passing iterations computed as (target - current) that hit zero when convergence was already reached.

Common situations: Loop drivers that compute remaining iterations and hit 0; config defaults of 0 meaning 'auto'; refactoring from tolerance-based solvers where 0 meant 'iterate to convergence'.

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
• Number of initial values must be equal to number of rows in
• Coefficient matrix is not strictly diagonally dominant

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/3f41e6c84228b2eb. Report an issue: GitHub.