Mar 19, 2018 · As an alternative to the L1 norm, this paper proposes a class of non-convex penalty functions that maintain the convexity of the least squares ...
Sparse Regularization via Convex Analysis | IEEE Journals & Magazine
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Jun 2, 2017 · As an alternative to the L1 norm, this paper proposes a class of nonconvex penalty functions that maintain the convexity of the least squares ...
As an alternative to the L1 norm, this paper proposes a class of non- convex penalty functions that maintain the convexity of the least squares cost function to ...
Jan 19, 2024 · PDF | Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often ...
A class of nonconvex penalty functions that maintain the convexity of the least squares cost function to be minimized, and avoids the systematic ...
May 10, 2019 · Sparse approximate solutions to linear equations, which has numerous applications, can be obtained via L1 norm regularization.
Abstract: In this paper, we present a novel convex method for the graph-structured sparse recovery. While various structured sparsities can be represented ...
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Absence of suboptimal local minima. 2. Continuity of solution as a function of input data. 3. Algorithms guaranteed to converge to a global optimum.
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May 15, 2023 · Non-convex regularization holds the potential to promote sparsity more efficiently, and then further improves the accuracy of solutions. In this ...
Jul 14, 2023 · Abstract:Despite widespread adoption in practice, guarantees for the LASSO and Group LASSO are strikingly lacking in settings beyond ...
Missing: Analysis. | Show results with:Analysis.