Sentence examples for admissible class from inspiring English sources

The present result improves previous ones relaxing the smallness of Bα with respect to Aα to the milder assumption dom(A1/2)⊆dom(B1/2) and extending essentially the admissible class of Kato functions.

In this paper we consider an energy functional depending on the norm of the gradient and seek to extremise it over an admissible class of Sobolev maps defined on an annulus and taking values on the unit sphere whilst satisfying suitable boundary conditions.

We describe the affine linear elliptic and parabolic coercive settings in Section 3, discussing briefly admissible classes of piecewise-affine geometry and coefficients.

There is an integer h such that for (g' > h) begin{aligned} hat{epsilon }:{mathcal {A}}(g',d,G) rightarrow (K^cup )/_{Aut(G)} end{aligned}induces a bijection onto the set of admissible classes of refined homology invariants.

([115]) For the dihedral group (G=D_n) the connected components of the moduli space ({mathfrak {M}}_g (D_n)) are in bijection, via the map (hat{epsilon }), with the admissible classes of refined homology invariants.

4, we derive the necessary and sufficient conditions for linear simultaneous prediction to be admissible in class of nonhomogeneous linear predictors.

Necessary and sufficient conditions are derived for the simultaneous prediction to be admissible in classes of homogeneous and nonhomogeneous linear predictors, respectively.

Towards this end, let (u in {mathscr {A}}_{varphi}^{p}(mathbb{X})) be an admissible map of class (mathscr{C}^{1}) and pick (phiinmathscr{C}^{infty}_{0}(mathbb{X}^{n}, {mathbb {R}}^{n})) and, for (varepsiloninmathbb{R}) sufficiently small, set u_{varepsilon}= frac{u+varepsilonphi}{|u+varepsilonphi|}.

In 2013, Mohammadi et al. [7] extended the concept of an (alpha_ -admissible malpha_ -admissibles of α-admappinge mappings as follows.

The functional is said to be nonnegative over the class of admissible functions if J ( x ) ≥ 0 for every admissible x.

Briefly, the framework extends on the well-known recursive method for (uniform) random generation and uses the popular framework of admissible specifications of combinatorial classes, introducing weighted combinatorial classes to allow for the non-uniform generation by means of unranking.