1. Amaldi, E., & Kann, V. (1998). On the approximability of minimizing non zero variables or unsatisfied relations in linear systems. Theoretical Computer Science, 209, 237–260.
2. Bach, F., Jenatton, R., Mairal, J., & Obzinski, G. (2012). Optimization with sparsity-inducing penalties foundations and trends. Foundations and Trends in Machine Learning, 4(1), 1–106.
3. Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines. In Proceeding of international conference on machine learning ICML’98.
4. Candes, E., Wakin, M., & Boyd, S. (2008). Enhancing sparsity by reweighted $$l_{1}$$ l 1 minimization. Journal of Mathematical Analysis and Applications, 14, 877–905.
5. Chartrand, R., & Yin, W. (2008). Iteratively reweighted algorithms for compressive sensing. Acoustics, speech and signal processing, IEEE international conference ICASSP, 2008, 3869–3872.