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Error Estimation for Indirect Measurements: Interval Computation Problem is (Slightly) Harder than a Similar Probabilistic Computational Problem

  • Vladik Kreinovich
  • Anatoly Lakeyev
  • Jiří Rohn
  • Patrick Kahl
Part of the Applied Optimization book series (APOP, volume 10)

Abstract

One of main applications of interval computations is estimating errors of indirect measurements. A quantity y is measured indirectly if we measure some quantities xi related to y and then estimate y from the results x̄, of these measurements as f(x̄1,..., x̄n) by using a known relation f. Interval computations are used “to find the range of f(x1,...,xn) when xi are known to belong to intervals xi = [x̄i — Δi,x̄i + Δi],” where Δi are guaranteed accuracies of direct measurements. It is known that the corresponding problem is intractable (NP-hard) even for polynomial functions f.

In some real-life situations, we know the probabilities of different value of xi; usually, the errors xi — x̄i are independent Gaussian random variables with 0 average and known standard deviations σi. For such situations, we can formulate a similar probabilistic problem: “ given σi, compute the standard deviation of f(x1,..., xn)”. It is reasonably easy to show that this problem is feasible for polynomial functions f. So, for polynomial f, this probabilistic computation problem is easier than the interval computation problem.

It is not too much easier: Indeed, polynomials can be described as functions obtained from xi by applying addition, subtraction, and multiplication. A natural expansion is to add division, thus getting rational functions. We prove that for rational functions, the probabilistic computational problem (described above) is NP-hard.

The results of this chapter appear in Kosheleva et al. [186].

Keywords

Rational Function Polynomial Function Indirect Measurement Probabilistic Computation Boolean Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Vladik Kreinovich
    • 1
  • Anatoly Lakeyev
    • 2
  • Jiří Rohn
    • 3
  • Patrick Kahl
    • 4
  1. 1.University of Texas at El PasoUSA
  2. 2.Computing CenterRussian Academy of SciencesIrkutskRussia
  3. 3.Charles University and Academy of SciencesPragueCzech Republic
  4. 4.IBMTucsonUSA

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