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Third, in order to make it possible to incorporate some a priori information, if available, in the stabilizer of the objective functional subject to minimization, which can help minimize the non-uniqueness nature of this inverse problem.
It defines a variety of approaches to logic function minimization, which can be divided into two main categories: two-level and multilevel approaches.
John Wright proposed a dense error correction technique based on L1 minimization, which can recover nearly 100% of the corrupted observations.
An example of this approach is Total Variation minimization, which can be used to accurately reconstruct objects that have a sparse gradient in a wide variety of applications [11, 12].
Inaccurate atomic positions can cause extreme forces in the initial steps of the energy minimization, which can in turn confuse the energy-minimization algorithm.
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Specifically, we address representatives selection as a row-sparsity regularized minimization problem which can be effectively solved via convex programming.
They are optimally chosen by solving the L2 gain minimization problem, which can be rewritten as an equivalent LMI problem.
Employing a generalized Ambrosio-Tortorelli functional, the authors obtain a bound-constrained minimization problem, which can be expressed in terms of a variational inequality.
Wu et al. [18] formulated the problem of surface normal recovery as a rank minimization problem, which can be solved via convex optimization.
PAST interprets the signal subspace estimate as the solution of a minimization problem which can be simplified via an appropriate projection approximation and then applies a recursive least squares (RLS) procedure to track the signal subspace.
This is a reweighted l1-minimization problem, which can effectively be solved by the iterative shrinkage algorithm [21].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com