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.
Gould N.I.M., Simoncini V. (2020). Error estimates for iterative algorithms for minimizing regularized quadratic subproblems. OPTIMIZATION METHODS & SOFTWARE, 35(2), 304-328 [10.1080/10556788.2019.1670177].
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.File in questo prodotto:
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AAM_Error estimates for iterative algorithms for minimizing regularized quadratic subproblems.pdf
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