Skip to main content
Log in

Dual versus primal-dual interior-point methods for linear and conic programming

  • FULL LENGTH PAPER
  • Published:
Mathematical Programming Submit manuscript

Abstract

We observe a curious property of dual versus primal-dual path-following interior-point methods when applied to unbounded linear or conic programming problems in dual form. While primal-dual methods can be viewed as implicitly following a central path to detect primal infeasibility and dual unboundedness, dual methods can sometimes implicitly move away from the analytic center of the set of infeasibility/unboundedness detectors.

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.

Similar content being viewed by others

References

  1. Adler I., Resende M.G.C., Veiga G. and Karmarkar N.K. (1989). An implementation of Karmarkar’s algorithm for linear programming. Math. Program. 44(3): 297–335

    Article  MATH  MathSciNet  Google Scholar 

  2. Benson S.J., Ye Y. and Zhang X. (2000). Solving large-scale sparse semidefinite programs for combinatorial optimization. SIAM J. Optim. 10(2): 443–461

    Article  MATH  MathSciNet  Google Scholar 

  3. Gay D.M. (1985). Electronic mail distribution of linear programming test problems. Math. Program. Soc. Comm. Algorithm. Newslett. 13: 10–12

    Google Scholar 

  4. Gonzaga C.C. (1992). Path following methods for linear programming. SIAM Rev. 34(2): 167–224

    Article  MATH  MathSciNet  Google Scholar 

  5. Gonzaga C.C. and Todd M.J. (1992). SIAM J. Optim. 2: 349–359

    Article  MATH  MathSciNet  Google Scholar 

  6. Güler O. (1996). Barrier functions in interior point methods. Math. Oper. Res. 21: 860–885

    Article  MATH  MathSciNet  Google Scholar 

  7. Nesterov Y.E. and Nemirovskii A.S. (1993). Interior Point Polynomial Methods in Convex Programming: Theory and Algorithms. SIAM, Philadelphia

    Google Scholar 

  8. Renegar J. (1988). A polynomial-time algorithm based on Newton’s method for linear programming. Math. Program. 40: 59–93

    Article  MATH  MathSciNet  Google Scholar 

  9. Roos C., Terlaky T. and Vial J.P. (1997). Theory and Algorithms for Linear Optimization: An Interior Point Approach. Wiley, Chichester

    MATH  Google Scholar 

  10. Todd M.J. (2004). Detecting infeasibility in infeasible-interior-point methods for optimization. In: Cucker, F., DeVore, R., Olver, P. and Süli, E. (eds) Foundations of Computational Mathematics, Minneapolis 2002, pp 157–192. Cambridge University Press, Cambridge

    Google Scholar 

  11. Tütüncü R.H., Toh K.C. and Todd M.J. (2003). Solving semidefinite-quadratic-linear programs using SDPT3. Math. Program. 95(2): 189–217

    Article  MATH  MathSciNet  Google Scholar 

  12. Wright S. (1996). Primal-Dual Interior Point Methods. SIAM, Philadelphia

    Google Scholar 

  13. Ye Y. (1997). Interior Point Algorithms: Theory and Analysis. Wiley, New York

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. J. Todd.

Additional information

Dedicated to Clovis Gonzaga on the occassion of his 60th birthday.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Todd, M.J. Dual versus primal-dual interior-point methods for linear and conic programming. Math. Program. 111, 301–313 (2008). https://doi.org/10.1007/s10107-006-0067-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10107-006-0067-3

Mathematics Subject Classification (2000)

Navigation