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The sparse problems provide incumbent solutions.
Numerical experiments on large arbitrarily sparse problems taken from the engineering finite elements (f.e).
This example is given to show the performance of two new methods on the large sparse problems.
This property is called "group sparsity" or "joint sparsity," and many literature have considered these new sparse problems [1 6].
On the other hand, the smoothed L0 (SL0) algorithm has been presented for two-dimensional (2D) sparse problems [23].
To accelerate the convergence, we applied the Krylov subspace method for solving the correction equations in large sparse problems.
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As discussed in the previous section, the above problem can be cast as a group sparse problem.
At each node in the search path, a relaxed problem and a sparse problem are solved and a constraint is added to the relaxed problem.
Furthermore, a highly sparse problem-dependent preconditioner is developed to significantly reduce the number of iterations used by the iterative solvers.
For this sparse problem, our Weighted MPLE and Modified TV MPLE methods maintain the boundary of the invalid region and appear close to the true solution.
To overcome the data sparse problem in collaborative filtering, structural similarity between users [18] and objects based on bipartite network model [10] were proposed.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com