Sentence examples similar to matrices is guaranteed from inspiring English sources

Positive semidefiniteness of the linear combination of kernel matrices is guaranteed by constraining the weights of the kernels to be non-negative.

In contrast, the shrinkage estimates of the covariance matrices are guaranteed to be positive definite and hence invertible.

Formulated by Equation (5), coefficient matrix K is guaranteed to accomplish the convolution operation without approximation.

The equality of polynomial matrix and dft matrix decomposed matrices decompositions are guaranteed via the finite duration constraint, which is investigated in Section 4. The finite duration constraint imposes the phase angles of singular vector (eigenvector) to minimize a nonlinear quadratic function.

In this method, we adopt the l 2,1-norm to regularize the projection matrix W, so that it is guaranteed that W is sparse in row for feature selection.

Using matrix inequality conditions on the terminal weighting matrix, the closed-loop system stability is guaranteed.

The linearized primal-dual method in [11] can be directly induced by setting (P=I-tau A^{T}A) and Q= left ( textstylebegin{array}c@{quad}c@ frac{1}{gamma}I&B B^{T}& frac{1}{tau}I-A^{T}A end{array}displaystyle right ). (34) We can also easily show that the positive definiteness of the matrices P and Q in (34) is guaranteed if (gamma>0), (0<tau<1/|A^{T}A| ), (0<tau<1/|A^{T}A+gamma B^{T}B|).

Note that the invertibility of the matrix 1 n − 3 20 T K is guaranteed by condition (9.7).

The 2D matrix also graphically represents the fact that the sentence is guaranteed to be true whenever uttered.

The robust stabilization is guaranteed when a set of linear matrix inequalities (LMIs) formulated get satisfied.

This is guaranteed by starting with a diagonal matrix with negative entries and performing a (random) orthonormal transformation.