# # # Nim's Runtime Library # (c) Copyright 2019 b3liever # # See the file "copying.txt", included in this # distribution, for details about the copyright. ## Accurate summation functions. runnableExamples: import std/math template `~=`(x, y: float): bool = abs(x - y) < 1e-4 let n = 1_000_000 first = 1e10 small = 0.1 var data = @[first] for _ in 1 .. n: data.add(small) let result = first + small * n.float doAssert abs(sum(data) - result) > 0.3 doAssert sumKbn(data) ~= result doAssert sumPairs(data) ~= result ## See also ## ======== ## * `math module `_ for a standard `sum proc `_ func sumKbn*[T](x: openArray[T]): T = ## Kahan-Babuška-Neumaier summation: O(1) error growth, at the expense ## of a considerable increase in computational cost. ## ## See: ## * https://en.wikipedia.org/wiki/Kahan_summation_algorithm#Further_enhancements if len(x) == 0: return var sum = x[0] var c = T(0) for i in 1 ..< len(x): let xi = x[i] let t = sum + xi if abs(sum) >= abs(xi): c += (sum - t) + xi else: c += (xi - t) + sum sum = t result = sum + c func sumPairwise[T](x: openArray[T], i0, n: int): T = if n < 128: result = x[i0] for i in i0 + 1 ..< i0 + n: result += x[i] else: let n2 = n div 2 result = sumPairwise(x, i0, n2) + sumPairwise(x, i0 + n2, n - n2) func sumPairs*[T](x: openArray[T]): T = ## Pairwise (cascade) summation of `x[i0:i0+n-1]`, with O(log n) error growth ## (vs O(n) for a simple loop) with negligible performance cost if ## the base case is large enough. ## ## See, e.g.: ## * https://en.wikipedia.org/wiki/Pairwise_summation ## * Higham, Nicholas J. (1993), "The accuracy of floating point ## summation", SIAM Journal on Scientific Computing 14 (4): 783–799. ## ## In fact, the root-mean-square error growth, assuming random roundoff ## errors, is only O(sqrt(log n)), which is nearly indistinguishable from O(1) ## in practice. See: ## * Manfred Tasche and Hansmartin Zeuner, Handbook of ## Analytic-Computational Methods in Applied Mathematics (2000). let n = len(x) if n == 0: T(0) else: sumPairwise(x, 0, n)