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Special algorithms exist to carry out standard operations (e.g. matrix multiplication) directly on matrices stored in a sparse format (e.g. [ 8, 9]).
Close inspection on DOT-based functional brain images reveals that most of reconstructed images appear to be in a sparse format, where most of image elements are zeros with respect to the unchanged background; only a few of them are nonzero due to changes induced by cortical activations.
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In this work, the global stiffness matrix is stored in the COO format, as mentioned in "Efficient matrix/vector assembly in a sparse storage format".
However, if the matrix is stored in a sparse storage format, the implementation of permutation differs according to the storage format.
The efficient implementations are described in "Efficient matrix/vector assembly in a sparse storage format".
The details of the parallelization of the assembly are described in "Efficient matrix/vector assembly in a sparse storage format".
Before the real-time simulation loop, the element stiffness matrices of the linear elasticity ({mathbf {K}}^e_0) and the reduction lists described in "Efficient matrix/vector assembly in a sparse storage format" are calculated.
The associated feature matrix can be stored in the file train_x in sparse format.
The test data set can be stored in the file test_x in sparse format.
Moreover, we propose an efficient implementation, whereby the shape matrix of the lifted ellipsoid is stored in sparse format, and every atom operation corresponds to a sparse update in that matrix.
If stored in sparse format, l1_logreg_train uses the PCG method in solving the problem.
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