Nonmonotone Barzilai–Borwein Gradient Algorithm for $$\ell _{1}$$ℓ1-Regularized Nonsmooth Minimization in Compressive Sensing

This study aims to minimize the sum of a smooth function and a nonsmooth $$\ell _{1}$$ℓ1-regularized term. This problem as a special case includes the $$\ell _{1}$$ℓ1-regularized convex minimization problem in signal processing, compressive sensing, machine learning, data mining, and so on. However, the non-differentiability of the $$\ell _{1}$$ℓ1-norm causes more challenges especially in large problems encountered in many practical applications. This study proposes, analyzes, and tests a Barzilai–Borwein gradient algorithm. At each iteration, the generated search direction demonstrates descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneously taking the favorable structure of the $$\ell _{1}$$ℓ1-norm. A nonmonotone line search technique is incorporated to find a suitable stepsize along this direction. The algorithm is easily performed, where each iteration requiring the values of the objective function and the gradient of the smooth term. Under some conditions, the proposed algorithm appears globally convergent. The limited experiments using some nonconvex unconstrained problems from the CUTEr library with additive $$\ell _{1}$$ℓ1-regularization illustrate that the proposed algorithm performs quite satisfactorily. Extensive experiments for $$\ell _{1}$$ℓ1-regularized least squares problems in compressive sensing verify that our algorithm compares favorably with several state-of-the-art algorithms that have been specifically designed in recent years.