In relation to optimising the wavelet base, sparsity is usually applied in order to evaluate the wavelet base.
First, by ignoring the detailed specific structures of 60 GHz multipath channels, the ROMP algorithm may impractically require at least K (K denotes sparsity of signal) iterations in order to reconstruct the original signal, which results in a much higher complexity O KMN), where M denotes the number of measurements and N denotes the length of the signals.
Other methods rely on external single-sequence folding and probabilistic alignment programs to generate base pairing probability matrices (Torarinsson et al., 2007; Will et al., 2007) or alignment match posterior probability matrices (Kiryu et al., 2007), and then exploit the sparsity of these matrices in order to reduce the amount of computation required.
In this work, we take advantage of the inherent sparsity of the cyclic spectrum in order to estimate CA from a low number of linear measurements and enable blind cyclostationary spectrum sensing.
Sparsity in Jacobian is carefully exploited in order to obtain computational efficiency.
Our methods help the user to identify cube schemata with structural sparsity, and to change the design in order to obtain more economy of space.
Alternatively, an L1 constraint can be easily added to the optimization problem as a convex variant of a cardinality constraint, in order to induce sparsity on the controller matrices.
We perform a dynamic linearization, followed by a state estimation where sparsity and non-negativity is utilized, in order to achieve a stable solution from the underdetermined measurement setup.
A general image restoration problem with non-negative constraint and sparse regularization can be written as min_{xin{C}} frac{1}{2}|Ax-a|^{2} +mu|Bx| _{1}, (5.2) where A is some linear operator describing the image formation process, (|Bx|_{1}) is the usual (ell_{1}) based regularization in order to promote sparsity under the transform B, (mu>0) is the regularization parameter.
