maxIters

MaxIters, short for maximum iterations, is a parameter used in many iterative algorithms to cap the number of update steps the algorithm may perform. It is typically represented as a non-negative integer and is used to ensure that computation terminates even if the method has not yet met its convergence criteria.

The primary purpose of maxIters is to provide a safety net against infinite loops and excessively long runtimes. If the algorithm converges before reaching the limit, it stops early. If convergence is not achieved within the allotted iterations, the algorithm stops at the final iterate and usually returns a status indicator such as “converged,” “did not converge,” or “max iterations reached.” The exact behavior, including what is returned at termination, depends on the specific implementation.

MaxIters is widely used across domains that rely on iterative procedures, including numerical optimization, linear system solvers, eigenvalue computations, root finding, and machine learning training loops. Examples include gradient descent, Newton-Raphson methods, Jacobi and Gauss-Seidel solvers, and Expectation-Maximization algorithms.

Choosing an appropriate value for maxIters involves a trade-off between computation time and solution quality. A value that is too small may yield insufficient convergence, producing imprecise results. A value that is too large can waste time when convergence is slow or unattainable. Some implementations allow 0 or null to indicate no limit or to apply a library-specific default. In practice, maxIters is often augmented with other stopping criteria to balance efficiency and accuracy.