This erratum provides a correction to the proof of Theorem 3.1 of Friedlander and Schmidt [SIAM J. Sci. Comput., 34 (2012), pp. A1380--A1405].
Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements.
Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements.
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Apr 13, 2011 · We explore hybrid methods that exhibit the benefits of both approaches. Rate-of-convergence analysis shows that by controlling the sample size in an ...
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We explore hybrid methods that exhibit the benefits of both approaches. Rate-of-convergence analysis shows that by controlling the sample size in an incremental ...
This erratum provides a correction to the proof of Theorem 3.1 of Friedlander and Schmidt [SIAM J. Sci. Comput., 34 (2012), pp. A1380--A1405].
This erratum provides a correction to the proof of Theorem 3.1 of Friedlander and Schmidt [SIAM J. Sci. Comput., 34 (2012), pp. A1380--A1405].
Other applications where the gradient is measured with error. Michael Friedlander and Mark Schmdit. Hybrid Deterministic-Stochastic Methods for Data Fitting ...
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This erratum provides a correction to the proof of Theorem 3.1 of Friedlander and Schmidt [SIAM J. Sci. Comput., 34 (2012), pp. A1380--A1405].
Apr 12, 2011 · We explore hybrid methods that exhibit the benefits of both approaches. Rate of convergence analysis and numerical experiments illustrate the ...
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