Skip to main content
Log in

Using LASSO for formulating constraint of least-squares programming for solving one-norm equality constrained problem

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The paper proposes an efficient method for solving a one- norm equality constrained optimization problem. In fact, this kind of optimization problems is nonconvex. First, the problem is formulated as the least absolute shrinkage and selection operator (LASSO) optimization problem. Then, it is solved by iterative shrinkage algorithms such as the fast iterative shrinkage thresholding algorithm. Next, the solution of the LASSO optimization problem is employed for formulating the constraint of the corresponding least-squares constrained optimization problem. The solution of the least-squares constrained optimization problem is taken as a near globally optimal solution of the one-norm equality constrained optimization problem. The main advantage of this proposed method is that a solution with both lower one-norm constraint error and two-norm reconstruction error can be obtained compared to those of the LASSO problem, while the required computational power is significantly reduced compared to the full search approach. Computer numerical simulation results are illustrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Nguyen, S.L.H., Ghrayeb, A., Hasna, M.: Iterative compressive estimation and decoding for network-channel-coded two-way relay sparse ISI channels. IEEE Commun. Lett. 16(12), 1992–1995 (2012)

    Article  Google Scholar 

  2. Yang, S., Wang, M., Chen, Y., Sun, Y.: Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding. IEEE Trans. Image Process. 21(9), 4016–4028 (2012)

    Article  MathSciNet  Google Scholar 

  3. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Cai, Tony: T., Wang, L., Xu, G.: New bounds for restricted isometry constants. IEEE Trans. Inf. Theory 56(9), 4388–4394 (2010)

    Article  MathSciNet  Google Scholar 

  5. Donoho, D.L., Elad, M.: On the stability of the basis pursuit in the presence of noise. Signal Process. 86, 511–532 (2006)

    Article  MATH  Google Scholar 

  6. Yuan, L., Liu, J., Ye, J.: Efficient methods for overlapping group Lasso. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2104–2116 (2013)

    Article  Google Scholar 

  7. Bunea, F., Lederer, J., She, Y.: The group square-root Lasso: theoretical properties and fast algorithms. IEEE Trans. Inf. Theory 60(2), 1313–1325 (2014)

    Article  MathSciNet  Google Scholar 

  8. Xu, H., Caramanis, C., Mannor, S.: Robust regression and Lasso. IEEE Trans. Inf. Theory 56(7), 3561–3574 (2010)

    Article  MathSciNet  Google Scholar 

  9. Elad, M.: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer, Berlin (2010)

    Book  MATH  Google Scholar 

  10. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhu, X., Dingguo, P.: A restoration-free filter SQP algorithm for equality constrained optimization. Appl. Math. Comput. 219, 6016–6029 (2013)

    MathSciNet  MATH  Google Scholar 

  12. Jian, J.-B., Xu, Q.-J., Han, D.-L.: A strongly convergent norm-relaxed method of strongly sub-feasible direction for optimization with nonlinear equality and inequality constraints. Appl. Math. Comput. 182, 854–870 (2006)

    MathSciNet  MATH  Google Scholar 

  13. Yuan, J.Y.: Numerical methods for generalized least squares problems. J. Comput. Appl. Math. 66, 571–584 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  14. Liang, Y., Weston, J., Szularz, M.: Generalized least-squares polynomial preconditioners for symmetric indefinite linear equations. Parallel Comput. 28, 323–341 (2002)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This paper was supported partly by the National Nature Science Foundation of China (Nos. 61372173 and 61471132), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (No. 501130144), the Hundred People Plan from the Guangdong University of Technology and the Young Thousand People Plan from the Ministry of Education of China.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bingo Wing-Kuen Ling.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Ling, B.WK., Xie, L. et al. Using LASSO for formulating constraint of least-squares programming for solving one-norm equality constrained problem. SIViP 11, 179–186 (2017). https://doi.org/10.1007/s11760-016-0917-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0917-2

Keywords

Navigation