Method of conjugate subgradients with constrained memory

E. A. Nurminskii, David Tien

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


A method to solve the convex problems of nondifferentiable optimization relying on the basic philosophy of the method of conjugate gradients and coinciding with it in the case of quadratic functions was presented. Its basic distinction from the earlier counterparts lies in the a priori fixed constraint on the memory size which is independent of the accuracy of the resulting solution. Numerical experiments suggest practically linear rate of convergence of this algorithm.
Original languageEnglish
Pages (from-to)646-656
Number of pages11
JournalAutomation and Remote Control
Issue number4
Publication statusPublished - Apr 2014


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