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2011

Hybrid Deterministic-Stochastic Methods for Data Fitting

Friedlander, Michael P., Schmidt, Mark

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Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements.

Incremental gradient algorithms offer inexpensive iterations by sampling a subset of the terms in the sum.
These methods can make great progress initially, but often slow as they approach a solution.
In contrast, full-gradient methods achieve steady convergence at the expense of evaluating the full objective and gradient on each iteration.

Hybrid Deterministic-Stochastic Methods for Data Fitting · Around

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