Novel Approaches to Numerical Software with Result Verification
Identifieur interne : 001677 ( Istex/Corpus ); précédent : 001676; suivant : 001678Novel Approaches to Numerical Software with Result Verification
Auteurs : Laurent Granvilliers ; Vladik Kreinovich ; Norbert MüllerSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
Abstract
Abstract: Traditional design of numerical software with result verification is based on the assumption that we know the algorithm f(x 1,...,x n ) that transforms inputs x 1,...,x n into the output y=f(x 1,...,x n ), and we know the intervals of possible values of the inputs. Many real-life problems go beyond this paradigm. In some cases, we do not have an algorithm f, we only know some relation (constraints) between x i and y. In other cases, in addition to knowing the intervals x i , we may know some relations between x i ; we may have some information about the probabilities of different values of x i , and we may know the exact values of some of the inputs (e.g., we may know that x 1 = π/2). In this paper, we describe the approaches for solving these real-life problems. In Section 2, we describe interval consistency techniques related to handling constraints; in Section 3, we describe techniques that take probabilistic information into consideration, and in Section 4, we overview techniques for processing exact real numbers.
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DOI: 10.1007/978-3-540-24738-8_17
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<front><div type="abstract" xml:lang="en">Abstract: Traditional design of numerical software with result verification is based on the assumption that we know the algorithm f(x 1,...,x n ) that transforms inputs x 1,...,x n into the output y=f(x 1,...,x n ), and we know the intervals of possible values of the inputs. Many real-life problems go beyond this paradigm. In some cases, we do not have an algorithm f, we only know some relation (constraints) between x i and y. In other cases, in addition to knowing the intervals x i , we may know some relations between x i ; we may have some information about the probabilities of different values of x i , and we may know the exact values of some of the inputs (e.g., we may know that x 1 = π/2). In this paper, we describe the approaches for solving these real-life problems. In Section 2, we describe interval consistency techniques related to handling constraints; in Section 3, we describe techniques that take probabilistic information into consideration, and in Section 4, we overview techniques for processing exact real numbers.</div>
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<abstract xml:lang="en"><p>Abstract: Traditional design of numerical software with result verification is based on the assumption that we know the algorithm f(x 1,...,x n ) that transforms inputs x 1,...,x n into the output y=f(x 1,...,x n ), and we know the intervals of possible values of the inputs. Many real-life problems go beyond this paradigm. In some cases, we do not have an algorithm f, we only know some relation (constraints) between x i and y. In other cases, in addition to knowing the intervals x i , we may know some relations between x i ; we may have some information about the probabilities of different values of x i , and we may know the exact values of some of the inputs (e.g., we may know that x 1 = π/2). In this paper, we describe the approaches for solving these real-life problems. In Section 2, we describe interval consistency techniques related to handling constraints; in Section 3, we describe techniques that take probabilistic information into consideration, and in Section 4, we overview techniques for processing exact real numbers.</p>
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<Para>Traditional design of numerical software with result verification is based on the assumption that we know the algorithm <Emphasis Type="Italic">f</Emphasis>
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into the output <Emphasis Type="Italic">y</Emphasis>
=<Emphasis Type="Italic">f</Emphasis>
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, we only know some relation (constraints) between <Emphasis Type="Italic">x</Emphasis>
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and <Emphasis Type="Italic">y</Emphasis>
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, we may know some relations between <Emphasis Type="Italic">x</Emphasis>
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; we may have some information about the probabilities of different values of <Emphasis Type="Italic">x</Emphasis>
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, and we may know the exact values of some of the inputs (e.g., we may know that <Emphasis Type="Italic">x</Emphasis>
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= <Emphasis Type="Italic">π</Emphasis>
/2). In this paper, we describe the approaches for solving these real-life problems. In Section 2, we describe interval consistency techniques related to handling constraints; in Section 3, we describe techniques that take probabilistic information into consideration, and in Section 4, we overview techniques for processing exact real numbers.</Para>
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<abstract lang="en">Abstract: Traditional design of numerical software with result verification is based on the assumption that we know the algorithm f(x 1,...,x n ) that transforms inputs x 1,...,x n into the output y=f(x 1,...,x n ), and we know the intervals of possible values of the inputs. Many real-life problems go beyond this paradigm. In some cases, we do not have an algorithm f, we only know some relation (constraints) between x i and y. In other cases, in addition to knowing the intervals x i , we may know some relations between x i ; we may have some information about the probabilities of different values of x i , and we may know the exact values of some of the inputs (e.g., we may know that x 1 = π/2). In this paper, we describe the approaches for solving these real-life problems. In Section 2, we describe interval consistency techniques related to handling constraints; in Section 3, we describe techniques that take probabilistic information into consideration, and in Section 4, we overview techniques for processing exact real numbers.</abstract>
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