Hybrid Deterministic-Stochastic Methods for Data Fitting
By: Researcher II
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| Authors | Michael P. Friedlander, Mark W. Schmidt |
| Journal/Conference Name | SIAM Journal on Scientific Computing |
| Paper Category | Computer Science |
| Paper Abstract | 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. 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-gradient algorithm, it is possible to maintain the steady convergence rates of full-gradient methods. We detail a practical quasi-Newton implementation based on this approach. Numerical experiments illustrate its potential benefits. |
| Date of publication | 2012 |
| Code Programming Language | MATLAB |
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