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1605. Solving large-scale nonlinear least-squares with random Gauss-Newton models
Invited abstract in session MB-34: Optimization and learning for data science and imaging (Part II), stream Advances in large scale nonlinear optimization.
Monday, 10:30-12:00Room: 43 (building: 303A)
Authors (first author is the speaker)
| 1. | Benedetta Morini
|
| Dipartimento di Ingegneria Industriale, Universita di Firenze | |
| 2. | Stefania Bellavia
|
| Dipartimento di Ingegneria Industriale, Universita di Firenze | |
| 3. | Greta Malaspina
|
| Department of Industrial Engineering, Università 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 per-iteration computational complexity lower than classical deterministic methods, due to the employment of random models inspired by randomized linear algebra tools. Under suitable assumptions, the stochastic optimization procedures can achieve a desired level of accuracy in the first-order optimality condition. We discuss the construction of the random models and the iteration complexity results to drive the gradient below a prescribed accuracy, then we present results from our computational experience.
Keywords
- Mathematical Programming
- Large Scale Optimization
- Algorithms
Status: accepted
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