969 resultados para POLYNOMIAL-MAPPINGS
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The paper presents a short review of some systems for program transformations performed on the basis of the internal intermediate representations of these programs. Many systems try to support several languages of representation of the source texts of programs and solve the task of their translation into the internal representation. This task is still a challenge as it is effort-consuming. To reduce the effort, different systems of translator construction, ready compilers with ready grammars of outside designers are used. Though this approach saves the effort, it has its drawbacks and constraints. The paper presents the general idea of using the mapping approach to solve the task within the framework of program transformations and overcome the disadvantages of the existing systems. The paper demonstrates a fragment of the ontology model of high-level languages mappings onto the single representation and gives the example of how the description of (a fragment) a particular mapping is represented in accordance with the ontology model.
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Mathematics Subject Class.: 33C10,33D60,26D15,33D05,33D15,33D90
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The eccentric connectivity index of a graph G, ξ^C, was proposed by Sharma, Goswami and Madan. It is defined as ξ^C(G) = ∑ u ∈ V(G) degG(u)εG(u), where degG(u) denotes the degree of the vertex x in G and εG(u) = Max{d(u, x) | x ∈ V (G)}. The eccentric connectivity polynomial is a polynomial version of this topological index. In this paper, exact formulas for the eccentric connectivity polynomial of Cartesian product, symmetric difference, disjunction and join of graphs are presented.
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MSC 2010: 30C45, 30C55
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MSC 2010: 30C45, 30C55
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2000 Mathematics Subject Classification: 12D10.
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2000 Mathematics Subject Classification: 46B03
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000 Mathematics Subject Classification: Primary 16R50, Secondary 16W55.
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2000 Mathematics Subject Classification: 13P05, 14M15, 14M17, 14L30.
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2000 Mathematics Subject Classification: Primary 81R50, 16W50, 16S36, 16S37.
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2000 Mathematics Subject Classification: 12D10.
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2000 Mathematics Subject Classification: Primary 20F55, 13F20; Secondary 14L30.
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2010 Mathematics Subject Classification: Primary 35S05, 35J60; Secondary 35A20, 35B08, 35B40.
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AMS subject classification: Primary 49J52; secondary: 26A27, 90C48, 47N10.
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An iterative Monte Carlo algorithm for evaluating linear functionals of the solution of integral equations with polynomial non-linearity is proposed and studied. The method uses a simulation of branching stochastic processes. It is proved that the mathematical expectation of the introduced random variable is equal to a linear functional of the solution. The algorithm uses the so-called almost optimal density function. Numerical examples are considered. Parallel implementation of the algorithm is also realized using the package ATHAPASCAN as an environment for parallel realization.The computational results demonstrate high parallel efficiency of the presented algorithm and give a good solution when almost optimal density function is used as a transition density.