Tractable ADMM schemes for computing KKT points and local minimizers for ℓ -minimization problems

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We consider an ℓ-minimization problem where f(x) + γ‖ x‖ is minimized over a polyhedral set and the ℓ-norm regularizer implicitly emphasizes the sparsity of the solution. Such a setting captures a range of problems in image processing and statistical learning. Given the nonconvex and discontinuous nature of this norm, convex regularizers as substitutes are often employed and studied, but less is known about directly solving the ℓ-minimization problem. Inspired by Feng et al. (Pac J Optim 14:273–305, 2018), we consider resolving an equivalent formulation of the ℓ-minimization problem as a mathematical program with complementarity constraints (MPCC) and make the following contributions towards the characterization and computation of its KKT points: (i) First, we show that feasible points of this formulation satisfy the relatively weak Guignard constraint qualification. Furthermore, if f is convex, an equivalence is derived between first-order KKT points and local minimizers of the MPCC formulation. (ii) Next, we apply two alternating direction method of multiplier (ADMM) algorithms, named (ADMMcfμ,α,ρ) and (ADMMcf), to exploit the special structure of the MPCC formulation. Both schemes feature tractable subproblems. Specifically, in spite of the overall nonconvexity, it is shown that the first update can be effectively reduced to a closed-form expression by recognizing a hidden convexity property while the second necessitates solving a tractable convex program. In (ADMMcfμ,α,ρ), subsequential convergence to a perturbed KKT point under mild assumptions is proved. Preliminary numerical experiments suggest that the proposed tractable ADMM schemes are more scalable than their standard counterpart while (ADMMcf) compares well with its competitors in solving the ℓ-minimization problem.

Original languageEnglish (US)
Pages (from-to)43-85
Number of pages43
JournalComputational Optimization and Applications
Issue number1
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics


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