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| Author(s): |
Lucas Eduardo Azevedo Simões
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2013-03-28 |
| Examining board members: |
Elias Salomão Helou Neto;
José Mario Martinez Perez;
Ricardo Hiroshi Caldeira Takahashi
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| Advisor: | Elias Salomão Helou Neto |
| Abstract | |
We present two new methods for solving bilevel convex optimization problems, where both functions are not necessarily differentiable, i.e., we show that the sequences generated by those methods converge to the optimal set of a nonsmooth function subject to a set that also involves a function minimization. Both algorithms do not require any kind of subproblems resolution or linear search during the iterations. At the end, to prove that our methods are viable, we solve a problem of tomographic image reconstruction (AU) | |
| FAPESP's process: | 11/02219-4 - New incremental methods for bi-level non-differentiable convex optimization with applications to image reconstruction for emission tomography |
| Grantee: | Lucas Eduardo Azevedo Simões |
| Support Opportunities: | Scholarships in Brazil - Master |