Abstract
Algorithms based on trust regions have been shown to be robust methods for unconstrained optimization problems. All existing methods, either based on the dogleg strategy or Hebden-More iterations, require solution of system of linear equations. In large scale optimization this may be prohibitively expensive. It is shown in this paper that an approximate solution of the trust region problem may be found by the preconditioned conjugate gradient method. This may be regarded as a generalized dogleg technique where we asymptotically take the inexact quasi-Newton step. We also show that we have the same convergence properties as existing methods based on the dogleg strategy using an approximate Hessian.
Keywords
Conjugate gradient methodTrust regionHessian matrixScale (ratio)MathematicsConvergence (economics)Mathematical optimizationNonlinear conjugate gradient methodOptimization problemApplied mathematicsComputer scienceGradient descentArtificial intelligence
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Publication Info
- Year
- 1983
- Type
- article
- Volume
- 20
- Issue
- 3
- Pages
- 626-637
- Citations
- 826
- Access
- Closed
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Cite This
Trond Steihaug
(1983).
The Conjugate Gradient Method and Trust Regions in Large Scale Optimization.
SIAM Journal on Numerical Analysis
, 20
(3)
, 626-637.
https://doi.org/10.1137/0720042
Identifiers
- DOI
- 10.1137/0720042