On the Global Convergence of Trust Region Algorithms Using Inexact Gradient Information | Zendy

Richard G. Carter | Zendy

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On the Global Convergence of Trust Region Algorithms Using Inexact Gradient Information

Author(s) -

Richard G. Carter

Publication year - 1991

Publication title -

siam journal on numerical analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.78

H-Index - 134

eISSN - 1095-7170

pISSN - 0036-1429

DOI - 10.1137/0728014

Subject(s) - trust region , convergence (economics) , mathematics , class (philosophy) , simple (philosophy) , algorithm , proximal gradient methods , mathematical optimization , computer science , artificial intelligence , convex function , computer security , economics , radius , economic growth , philosophy , geometry , epistemology , regular polygon

Trust region algorithms are an important class of methods that can be used to solve unconstrained optimization problems. Strong global convergence results are demonstrated for a class of methods where the gradient values are approximated rather than computed exactly, provided they obey a simple relative error condition. No requirement is made that gradients be recomputed to successively greater accuracy after unsuccessful iterations.

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