Sentence examples for dimensional coefficient from inspiring English sources

Without appropriately unbiased initial guess of the baseline parameters, it is not easy to stably obtain the converging solution of the five location-dependent parameters in (5) due to the search in very high dimensional coefficient space.

For the calculation, we adopt a linear search using the incomplete Cholesky conjugate gradient (ICCG) method for 5(N + n) dimensional coefficient vectors, where N + n is the same number as given in Section 5.3.

For the maximization, we adopt a linear search procedure in conjunction with the incomplete Cholesky conjugate gradient (ICCG) method for 2(N + n) dimensional coefficient vectors by using a suitable approximate Hessian matrix (see Appendix), where N is the number of earthquakes and n is the number of the additional points on the rectangular boundary including the corners (see Fig. 2(b)).

The p dimensional coefficient vector β = (β1,…, β p ) t can be estimated by minimizing the penalized negative log-likelihood: (2) 1 n ∑ i = 1 n − y i x i t β + log ⁡ 1 + exp ⁡ x i t β + ∑ j = 1 p J λ β j, where J λ is a penalty function and λ is a vector of tuning parameter that can be determined by a search on an appropriate grid.

Let ad and na denote the NSV-dimensional coefficient vectors of ad and na, respectively.

To simplify the problem, lift is typically measured as a non-dimensional coefficient.

To compare geometries, the non-dimensional coefficient of power was used as a fitness function.

Based on the rough estimation of Nishizawa et al. (2015), the non-dimensional coefficient should be smaller than O (10−3)–O (100) for n = 4.

Table 3 List of non-dimensional coefficient for sensitivity experiment Non-dimensional coefficient γ = 10−3 γ = 10−5 γ = 10−7 The strength of the numerical filter 1.25 × 105 1.25 × 103 1.25 × 101 The value of the numerical filter (m4 s−1) for each experiment.

The cubic hard spring and cubic soft spring are characterized by the non-dimensional coefficient σ2s0ε where σs0 is the r.m.s.

If the attribute vectors are projected into the subspace spanned by P significant principal components, then we can represent the attribute of web user i in terms of the following P-dimensional coefficient vector: (5).