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- Title
Method of conjugate subgradients with constrained memory.
- Authors
Nurminskii, E.; Tien, D.
- Abstract
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.
- Subjects
CONVEX functions; NONDIFFERENTIABLE functions; MATHEMATICAL optimization; QUADRATIC equations; A priori; STOCHASTIC convergence
- Publication
Automation & Remote Control, 2014, Vol 75, Issue 4, p646
- ISSN
0005-1179
- Publication type
Article
- DOI
10.1134/S0005117914040055