Some large scale problems are solved in configurations of interest for the International Thermonuclear Experimental Reactor ITERR).
The method was shown to be efficient for medium size nonlinear problem, but its computational performance for realistic large scale problems was not satisfactory.
To find eigenvalues and eigenvectors of a large scale problem is another time consuming and disk critical phase for conventional structural analysis.
New models that can appropriately balance the biological and structural similarities and algorithms that can efficiently and effectively solve the large scale problem are extremely demanded in the fields of systems biology.
A scheme for solving fairly large-scale problems is also presented.
However, the application of the MSV rule to large-scale problems is difficult, as all feasible measurement subsets need to be evaluated to find the optimal solution.
Identifying retrofit opportunities in large-scale problems is extremely complex due to numerous interconnections and dependencies between process units, sub-units and utilities present on most industrial sites.
In the second stage, spectral bisection for large-scale problems is realized using a sparse direct linear solver to narrow down the interval of the k-th eigenvalue.
In this paper a "forward-advancing" field discretization method suitable for solving the Helmholtz equation in large-scale problems is proposed.
Numerical implementations that scale efficiently for large-scale problems are possible for nonsmooth DAEs.
