930 resultados para Irreducible polynomial
Resumo:
Dubrovin type equations for the N -gap solution of a completely integrable system associated with a polynomial pencil is constructed and then integrated to a system of functional equations. The approach used to derive those results is a generalization of the familiar process of finding the 1-soliton (1-gap) solution by integrating the ODE obtained from the soliton equation via the substitution u = u(x + λt).
Resumo:
* Dedicated to the memory of Prof. N. Obreshkoff
Resumo:
Mathematics Subject Class.: 33C10,33D60,26D15,33D05,33D15,33D90
Resumo:
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.
Resumo:
2000 Mathematics Subject Classification: 12D10.
Resumo:
000 Mathematics Subject Classification: Primary 16R50, Secondary 16W55.
Resumo:
2000 Mathematics Subject Classification: 13P05, 14M15, 14M17, 14L30.
Resumo:
2000 Mathematics Subject Classification: Primary 81R50, 16W50, 16S36, 16S37.
Resumo:
2000 Mathematics Subject Classification: 12D10.
Resumo:
2000 Mathematics Subject Classification: Primary 20F55, 13F20; Secondary 14L30.
Resumo:
2010 Mathematics Subject Classification: Primary 35S05, 35J60; Secondary 35A20, 35B08, 35B40.
Resumo:
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.
Resumo:
ACM Computing Classification System (1998): F.2.1, G.1.5, I.1.2.
Resumo:
Given the polynomials f, g ∈ Z[x] of degrees n, m, respectively, with n > m, three new, and easy to understand methods — along with the more efficient variants of the last two of them — are presented for the computation of their subresultant polynomial remainder sequence (prs). All three methods evaluate a single determinant (subresultant) of an appropriate sub-matrix of sylvester1, Sylvester’s widely known and used matrix of 1840 of dimension (m + n) × (m + n), in order to compute the correct sign of each polynomial in the sequence and — except for the second method — to force its coefficients to become subresultants. Of interest is the fact that only the first method uses pseudo remainders. The second method uses regular remainders and performs operations in Q[x], whereas the third one triangularizes sylvester2, Sylvester’s little known and hardly ever used matrix of 1853 of dimension 2n × 2n. All methods mentioned in this paper (along with their supporting functions) have been implemented in Sympy and can be downloaded from the link http://inf-server.inf.uth.gr/~akritas/publications/subresultants.py
Resumo:
2010 Mathematics Subject Classification: 14L99, 14R10, 20B27.