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To estimate a graph G, the training points for the n th gene, D n, are used to solve a sparse linear regression problem posed as a LASSO and formulated as an LP.
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The points are mapped into a planar parameter domain by solving a sparse linear system.
A quadratic optimization controls the mesh warping and can be achieved by solving a sparse linear system.
Given the block-partition information of a model, the interior control points can be obtained efficiently by solving a sparse linear system.
The energy function has been carefully designed to be a quadratic function so that it can be efficiently minimized via solving a sparse linear system.
Starting from an initial path for each step, its shape is firstly optimized by solving a sparse linear system and then the output floating path is projected to the surface to obtain the refined one for further propagation.
The framework consists of two steps: first, from a dense initial knot vector, a set of active knots is selected at which certain order derivative of the spline is discontinuous by solving a sparse optimization problem; second, we further remove redundant knots and adjust the positions of active knots to obtain the final knot vector.
By discretizing the variational principle in a natural way we obtain discrete conformal maps which can be computed by solving a sparse linear system.
In the sparse matrix domain, we solve a core problem, sparse-matrix vector product (SMVP).
Different from traditional manifold learning methods, which construct an adjacency graph from K-nearest neighbors or ɛ-ball method, the SRPE algorithm constructs the adjacency graph by solving a robust sparse representation problem through convex optimization.
where the index set E denotes the successfully received locations in M. According to the definition of R and the expression in (4), the detection can be achieved by solving a jointly sparse recovery problem.
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