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63 lines
1.8 KiB
Nim
63 lines
1.8 KiB
Nim
#
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#
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# Nim's Runtime Library
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# (c) Copyright 2019 b3liever
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#
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# See the file "copying.txt", included in this
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# distribution, for details about the copyright.
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## Fast sumation functions.
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func sumKbn*[T](x: openArray[T]): T =
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## Kahan-Babuška-Neumaier summation: O(1) error growth, at the expense
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## of a considerable increase in computational expense.
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if len(x) == 0: return
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var sum = x[0]
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var c = T(0)
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for i in 1 ..< len(x):
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let xi = x[i]
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let t = sum + xi
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if abs(sum) >= abs(xi):
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c += (sum - t) + xi
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else:
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c += (xi - t) + sum
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sum = t
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result = sum + c
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func sumPairwise[T](x: openArray[T], i0, n: int): T =
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if n < 128:
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result = x[i0]
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for i in i0 + 1 ..< i0 + n:
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result += x[i]
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else:
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let n2 = n div 2
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result = sumPairwise(x, i0, n2) + sumPairwise(x, i0 + n2, n - n2)
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func sumPairs*[T](x: openArray[T]): T =
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## Pairwise (cascade) summation of ``x[i0:i0+n-1]``, with O(log n) error growth
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## (vs O(n) for a simple loop) with negligible performance cost if
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## the base case is large enough.
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##
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## See, e.g.:
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## * http://en.wikipedia.org/wiki/Pairwise_summation
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## Higham, Nicholas J. (1993), "The accuracy of floating point
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## summation", SIAM Journal on Scientific Computing 14 (4): 783–799.
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##
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## In fact, the root-mean-square error growth, assuming random roundoff
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## errors, is only O(sqrt(log n)), which is nearly indistinguishable from O(1)
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## in practice. See:
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## * Manfred Tasche and Hansmartin Zeuner, Handbook of
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## Analytic-Computational Methods in Applied Mathematics (2000).
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##
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let n = len(x)
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if n == 0: T(0) else: sumPairwise(x, 0, n)
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runnableExamples:
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static:
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block:
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const data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
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doAssert sumKbn(data) == 45
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doAssert sumPairs(data) == 45
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