In many problems in science and engineering ranging from astrophysics to geosciences to financial analysis, we know that a physical quantity y depends on the physical quantity x, i.e., y = f(x) for some function f(x), and we want to check whether this dependence is monotonic. Specifically, finitely many measurements of x i and y = f(x) have been made, and we want to check whether the results of these measurements are consistent with the monotonicity of f(x). An efficient parallelizable algorithm is known for solving this problem when the values x i are known precisely, while the values y i are known with interval uncertainty. In this paper, we extend this algorithm to a more general (and more realistic) situation when both x i and y i are known with interval uncertainty.
Reliable Computing – Springer Journals
Published: Jan 1, 2005
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