40. Randomized Gauss-Newton methods for large scale nonlinear least squares
Invited abstract in session WD-4: Large scale optimization and applications 2, stream Large scale optimization and applications.
Wednesday, 12:00 - 13:30Room: C105
Authors (first author is the speaker)
| 1. | Greta Malaspina
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| Department of Industrial Engineering, Università di Firenze | |
| 2. | Stefania Bellavia
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| Dipartimento di Ingegneria Industriale, Universita di Firenze | |
| 3. | Benedetta Morini
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| Dipartimento di Ingegneria Industriale, Universita di Firenze |
Abstract
We address the solution of large-scale nonlinear least-squares problems by stochastic Gauss-Newton methods combined with a line-search strategy. The algorithms proposed have computational complexity lower than classical deterministic methods due to the employment of random models inspired by randomized linear algebra tools. Under suitable assumptions, results on the ability to achieve a desired level of accuracy in the first-order optimality condition can be established. We discuss the construction of the random models, the iteration complexity results to drive the gradient below a prescribed accuracy and present results from our computational experience.
Keywords
- Linear and nonlinear optimization
- Large- and Huge-scale optimization
- Optimization under uncertainty and applications
Status: accepted
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