Now suppose that the set-theoretical universe forms a set: the set of all sets.
By evaluating the integration of Eq. (8), the system of equations forms a Tridiagonal matrix that can be solved easily by the Cholesky method.
All line vectors form a data set.
On the other hand, the linear KKT system for our dynamic optimization problem is sparse and structured, and can be permuted to form a block tridiagonal matrix.
They form a set of spectral filters.
The remaining ESTs formed a HQNC set.
Incorporating the initial and boundary conditions, we can write the spline in compression method in a tridiagonal form.
The iterative methods are preferred for big values of n and particular forms of the matrix A (tridiagonal, pentadiagonal, blocks modular) to solve the equivalent system X = BX + b, where B = In-A.
end{aligned} (30 For all (qge 2) and (nge q), as (30) indicates, (Sigma _{ZZ}^{-1}) has a tridiagonal symmetric form, with nonzero values only on the main diagonal and the q-th diagonals above and below it.
Using the fact that the mutation matrix in the case of a permutation invariant fitness landscape has a special tridiagonal form, a change of the basis is suggested such that in the new coordinates a number of analytical results can be obtained.
