When Can We Simplify Data Processing: An Algorithmic Answer

Authors

Julio Urenda, The University of Texas at El PasoFollow
Olga Kosheleva, The University of Texas at El PasoFollow
Vladik Kreinovich, The University of Texas at El PasoFollow
Berlin Wu, National Chengchi UniversityFollow

Publication Date

4-2015

Comments

Technical Report: UTEP-CS-15-30

Abstract

In many real-life situations, we are interested in the values of physical quantities x₁, ..., x_n which are difficult (or even impossible) to measure directly. To estimate these values, we measure easier-to-measure quantities y₁, ..., y_m which are related to the desired quantities by a known relation, and use these measurement results to estimate x_i. The corresponding data processing algorithms are sometimes very complex and time-consuming, so a natural question is: are simpler (and, thus, faster) algorithms possible for solving this data processing problem? In this paper, we show that by using the known Tarski-Seidenberg algorithm, we can check whether such a simplification exists and, if it exists, produce this simplification.