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Long-step path-following algorithm for solving symmetric programming problems with nonlinear objective functions

  • Leonid FaybusovichEmail author
  • Cunlu Zhou
Article
  • 41 Downloads

Abstract

We developed a long-step path-following algorithm for a class of symmetric programming problems with nonlinear convex objective functions. The theoretical framework is developed for functions compatible in the sense of Nesterov and Nemirovski with \(-\,\ln \det \) barrier function. Complexity estimates similar to the case of a linear-quadratic objective function are established, which gives an upper bound for the total number of Newton steps. The theoretical scheme is implemented for a class of spectral objective functions which includes the case of quantum (von Neumann) entropy objective function, important from the point of view of applications. We explicitly compare our numerical results with the only known competitor.

Keywords

Convex optimization Symmetric programming Nonlinear objective functions Self-concordance Interior-point methods Matrix monotonicity Von Neumann entropy 

Notes

Acknowledgements

This research is supported in part by Simmons Foundation Grant 275013.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of Notre DameNotre DameUSA

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