We derive bounds for the objective errors and gradient residuals when finding approximations to the solution of common regularized quadratic optimization problems within evolving Krylov spaces. These provide upper bounds on the number of iterations required to achieve a given stated accuracy. We illustrate the quality of our bounds on given test examples.

Error estimates for iterative algorithms for minimizing regularized quadratic subproblems

Simoncini V.
2020

Abstract

We derive bounds for the objective errors and gradient residuals when finding approximations to the solution of common regularized quadratic optimization problems within evolving Krylov spaces. These provide upper bounds on the number of iterations required to achieve a given stated accuracy. We illustrate the quality of our bounds on given test examples.
2020
Gould N.I.M.; Simoncini V.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/714899
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