Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Upper-Bounding the Regularization Constant for Convex Sparse Signal
  Reconstruction   Consider reconstructing a signal $x$ by minimizing a weighted sum of a convex
differentiable negative log-likelihood (NLL) (data-fidelity) term and a convex
regularization term that imposes a convex-set constraint on $x$ and enforces
its sparsity using $\ell_1$-norm analysis regularization. We compute upper
bounds on the regularization tuning constant beyond which the regularization
term overwhelmingly dominates the NLL term so that the set of minimum points of
the objective function does not change. Necessary and sufficient conditions for
irrelevance of sparse signal regularization and a condition for the existence
of finite upper bounds are established. We formulate an optimization problem
for finding these bounds when the regularization term can be globally minimized
by a feasible $x$ and also develop an alternating direction method of
multipliers (ADMM) type method for their computation. Simulation examples show
that the derived and empirical bounds match.

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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
3
The categories of this paper are: physics
1
The categories of this paper are: cs
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph