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In algebraic statistics, the models of interest are algebraic sets, i.e., solution sets to a system of multivariate polynomial equations.
When the modification is cast as an additional forcing term on the original structure a system of multivariate polynomials in the parameters of the beam cross-section are revealed.
This system of multivariate variational inequalities has a solution.
The set of solutions of this system of multivariate variational inequalities is closed convex in (K^{N}).
From the above, we claim that the system of multivariate variational inequalities (3.1) is equivalent to the variational inequality (3.2).
The purpose of this paper is to study a kind of system of multivariate variational inequalities and to prove the existence theorem of solutions.
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We generalize the notion of characteristic polynomial for a system of linear equations to systems of multivariate polynomial equations.
In this paper, we present a hybrid parallel algorithm for solving systems of multivariate constraints by exploiting both the CPU and the GPU multicore architectures.
To date, two general systems of multivariate regressions have been proposed to account for dependencies between pairs of species in a regional species pool.
Our purpose in this paper is to prove some existence theorems for solutions of a certain system of the multivariate nonexpansive operator equations and to calculate the solutions by using the generalized Mann and Halpern iterative algorithms in uniformly convex and uniformly smooth Banach spaces.
GPS NMR exploits the unique ability of NMR to simultaneously record signals from individual hydrogen atoms in complex macromolecular systems and of multivariate analysis to describe spectral variations from these by a few variables for establishment of, and positioning in, protein-folding state maps.
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