<ul><li>This paper presents a unified study of L1 over L2 sparsity promoting models in coherent dictionaries.</li><li>Theoretical analysis is provided on the existence of global solutions for constrained and unconstrained L1/L2 models.</li><li>Sparse properties of local minimizers are analyzed to rule out nonlocal-minimizer stationary solutions.</li><li>The alternating direction method of multipliers (ADMM) is applied with nonnegative constraint, resulting in significant improvements in computational time and accuracy for sparse recovery.</li></ul>

Understanding L1/L2 minimization technique part2(Machine Learning 2024)

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