# Accumulator Error Feedback

### From Wikimization

(Difference between revisions)

Line 12: | Line 12: | ||

% can cause significant roundoff errors. | % can cause significant roundoff errors. | ||

% | % | ||

- | % This code implements a variant of Kahan's compensated | + | % This Matlab code implements a variant of Kahan's compensated |

% summation algorithm which often takes about twice as long, | % summation algorithm which often takes about twice as long, | ||

% but produces more accurate sums when the number of | % but produces more accurate sums when the number of | ||

Line 48: | Line 48: | ||

x = x(idx); | x = x(idx); | ||

</pre> | </pre> | ||

- | should begin the <tt>csum()</tt> subroutine to achieve the most accurate summation. | + | should begin the <tt>csum()</tt> subroutine inside to achieve the most accurate summation. |

That is not presented here because the commented Example (inspired by Higham) would then display false positive results. | That is not presented here because the commented Example (inspired by Higham) would then display false positive results. | ||

Even in complete absence of sorting, <tt>csum()</tt> can be more accurate than conventional summation by orders of magnitude. | Even in complete absence of sorting, <tt>csum()</tt> can be more accurate than conventional summation by orders of magnitude. |

## Revision as of 23:50, 28 November 2017

function s_hat = csum(x) % CSUM Sum of elements using a compensated summation algorithm. % % For large vectors, the native sum command in Matlab does % not appear to use a compensated summation algorithm which % can cause significant roundoff errors. % % This Matlab code implements a variant of Kahan's compensated % summation algorithm which often takes about twice as long, % but produces more accurate sums when the number of % elements is large. -David Gleich % % Also see SUM. % % % Matlab csum() Example: % clear all % csumv=0; rsumv=0; % while csumv <= rsumv % v = randn(13e6,1); % rsumv = abs(sum(v) - sum(v(end:-1:1))); % disp(['rsumv = ' num2str(rsumv,'%18.16f')]); % [~, idx] = sort(abs(v),'descend'); % x = v(idx); % csumv = abs(csum(x) - csum(x(end:-1:1))); % disp(['csumv = ' num2str(csumv,'%18.16e')]); % end s_hat=0; e=0; for i=1:numel(x) s_hat_old = s_hat; y = x(i) + e; s_hat = s_hat_old + y; e = (s_hat_old - s_hat) + y; %calculate difference first (Higham) end return

### sorting

In practice, input sorting

[~, idx] = sort(abs(x),'descend'); x = x(idx);

should begin the `csum()` subroutine inside to achieve the most accurate summation.
That is not presented here because the commented Example (inspired by Higham) would then display false positive results.
Even in complete absence of sorting, `csum()` can be more accurate than conventional summation by orders of magnitude.

### links

Accuracy and Stability of Numerical Algorithms 2e, ch.4.3, Nicholas J. Higham, 2002

For multiplier error feedback, see:

Implementation of Recursive Digital Filters for High-Fidelity Audio

Comments on Implementation of Recursive Digital Filters for High-Fidelity Audio