# Accumulator Error Feedback

(Difference between revisions)
 Revision as of 22:49, 28 November 2017 (edit)← Previous diff Revision as of 23:01, 28 November 2017 (edit) (undo)Next diff → Line 19: Line 19: % % Matlab csum() example: % % Matlab csum() example: % clear all % clear all - % csumv = 0; + % csumv=0; rsumv=0; - % while ~csumv + % while csumv <= rsumv % v = randn(13e6,1); % v = randn(13e6,1); % rsumv = abs(sum(v) - sum(v(end:-1:1))); % rsumv = abs(sum(v) - sum(v(end:-1:1))); Line 39: Line 39: return return + + === notes === + In practice, input sorting +
+                                                                [~, idx] = sort(abs(x),'descend');
+                                                                x = x(idx);
+
+ should begin the csum() subroutine to achieve a most accurate summation. + That is not presented here because the commented example (inspired by Higham) would then display false positive results. + Even in absence of sorting, csum() is more accurate than conventional summation. === links === === links ===

## Revision as of 23:01, 28 November 2017

CSUM() in Digital Signal Processing terms: z-1 is a unit delay, Q is a floating-point quantizer to 64 bits, qi represents error due to quantization (additive by definition).   $LaTeX: -$ Jon Dattorro
```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 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
```

### notes

In practice, input sorting

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

should begin the csum() subroutine to achieve a most accurate summation. That is not presented here because the commented example (inspired by Higham) would then display false positive results. Even in absence of sorting, csum() is more accurate than conventional summation.

For multiplier error feedback, see: