In numerical analysis, the **Kahan summation algorithm** (also known as **compensated summation** ) significantly reduces the numerical error in the total obtained by adding a sequence of finite precision floating point numbers, compared to the obvious approach. This is done by keeping a separate *running compensation* (a variable to accumulate small errors).

In particular, simply summing *n* numbers in sequence has a worst-case error that grows proportional to *n*, and a root mean square error that grows as for random inputs (the roundoff errors form a random walk). With compensated summation, the worst-case error bound is independent of *n*, so a large number of values can be summed with an error that only depends on the floating-point precision.

The algorithm is attributed to William Kahan. Similar, earlier techniques are, for example, Bresenham's line algorithm, keeping track of the accumulated error in integer operations (although first documented around the same time) and the Delta-sigma modulation (integrating, not just summing the error).

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