Method of conjugate subgradients with constrained memory

E. A. Nurminskii; David Tien

doi:10.1134/S0005117914040055

Method of conjugate subgradients with constrained memory

E. A. Nurminskii, David Tien

Russian Academy of Sciences

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

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.

Original language	English
Pages (from-to)	646-656
Number of pages	11
Journal	Automation and Remote Control
Volume	75
Issue number	4
DOIs	https://doi.org/10.1134/S0005117914040055
Publication status	Published - Apr 2014

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10.1134/S0005117914040055

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title = "Method of conjugate subgradients with constrained memory",

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.",

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T1 - Method of conjugate subgradients with constrained memory

AU - Nurminskii, E. A.

AU - Tien, David

N1 - Includes bibliographical references.

PY - 2014/4

Y1 - 2014/4

N2 - 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.

AB - 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.

KW - Automation

KW - Control

KW - Convex problems

KW - Linear rate of convergences

KW - Memory size

KW - Non-differentiable optimization

KW - Numerical experiments

KW - Quadratic function

KW - Quadratic functionConvex problems

KW - Bundle Method

KW - Remote Control

KW - Conjugate Gradient

U2 - 10.1134/S0005117914040055

DO - 10.1134/S0005117914040055

M3 - Article

SN - 0005-1179

VL - 75

SP - 646

EP - 656

JO - Automation and Remote Control

JF - Automation and Remote Control

IS - 4

ER -

Method of conjugate subgradients with constrained memory

Abstract

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