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In the conventional FEM implementation, the most time-consuming procedures are assembling the stiffness matrix and solving linear equations.
The calculation times for assembling a matrix, rearranging a matrix, and solving a linear system of equations are plotted.
This association allows to reduce memory requirements and operation counts by avoiding the complete construction of the Jacobian matrix and solving a problem of reduced size.
A nested loop is used to run the solution code by assigning the constants of each equation in a 3-D matrix and solving it for the values of the variables (Daniel Arthur and Coughlin 2012).
Directly approximating images from biohash vectors may also be possible by integrating the PCA transformation with random projection matrix and solving the optimization problem by enforcing sparsity in the DCT or block-DCT domain.
Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-norm of a matrix, and solving optimisation problems such as maximum weight biclique, bipartite maximum weight cut, maximum weight induced sub-graph of a bipartite graph, etc.
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end{aligned} Then we can get the whole stiff matrix and solve the finite element equation.
Additionally, we also aggregate the prediction models for different saliency labels into a matrix, and solve saliency ranking via a low-rank matrix recovery problem.
It can also be represented as close linear system with seven diagonal sparse coefficient matrix, and solved by block pretreatment of generalized minimal residual algorithm.
Independent vector analysis (IVA) [12 14] is a sophisticated algorithm that can simultaneously estimate the frequency-wise demixing matrix and solve the permutation problem using only one objective function.
As an example, we obtain a fractional Legendre operational matrix and solve two fractional neutral pantograph differential equations by the constructed method.
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