Projected Subgradient Minimization Versus Superiorization

Yair Censor, Ran Davidi, Gabor T. Herman, Reinhard W. Schulte, Luba Tetruashvili

Research output: Contribution to journalArticlepeer-review


The projected subgradient method for constrained minimization repeatedly interlaces subgradient steps for the objective function with projections onto the feasible region, which is the intersection of closed and convex constraints sets, to regain feasibility. The latter poses a computational difficulty, and, therefore, the projected subgradient method is applicable only when the feasible region is "simple to project onto." In contrast to this, in the superiorization methodology a feasibility-seeking algorithm leads the overall process, and objective function steps are interlaced into it. This makes a difference because the feasibility-seeking algorithm employs projections onto the individual constraints sets and not onto the entire feasible region. We present the two approaches side-by-side and demonstrate their performance on a problem of computerized tomography image reconstruction, posed as a constrained minimization problem aiming at finding a constraint-compatible solution that has a reduced value of the total variation of the reconstructed image.

Original languageEnglish
Pages (from-to)730-747
Number of pages18
JournalJournal of Optimization Theory and Applications
Issue number3
StatePublished - Mar 2014

Bibliographical note

Funding Information:
Acknowledgements We thank the editor and reviewer for their constructive comments. We would like to acknowledge the generous support by Dr. Ernesto Gomez and Dr. Keith Schubert in allowing us to use the GPU cluster at the Department of Computer Science and Engineering at California State University San Bernardino. We are also grateful to Joanna Klukowska for her advice on using optimized compilation for speeding up SNARK09. This work was supported by the United States–Israel Binational Science Foundation (BSF) Grant No. 200912, the U.S. Department of Defense Prostate Cancer Research Program Award No. W81XWH-12-1-0122, the National Science Foundation Award No. DMS-1114901, the U.S. Department of Army Award No. W81XWH-10-1-0170, and by Grant No. R01EB013118 from the National Institute of Biomedical Imaging and Bioengineering and the National Science Foundation. The contents of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Biomedical Imaging and Bioengineering or the National Institutes of Health.


  • Bounded convergence
  • Computerized tomography
  • Constrained minimization
  • Feasibility-seeking
  • Image reconstruction
  • Projected subgradient method
  • Proximity function
  • Strong perturbation resilience
  • Superiorization

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics


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