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Information complexity of functional optimization problems and their approximation schemes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gnecco, Giorgio Sanguineti, Marcello |
| Copyright Year | 2010 |
| Abstract | Functional optimization is investigated using tools from information-based complexity. In such optimization problems, a functional has to be minimized with respect to admissible solutions belonging to an infinite-dimensional space of functions. This context models tasks arising in optimal control, systems identification, machine learning, time-series analysis, etc. The solution via variable- basis approximation schemes, which provide a sequence of nonlinear programming problems approx- imating the original functional one, is considered. Also for such problems, the information complexity is estimated. |
| Starting Page | 303 |
| Ending Page | 317 |
| Page Count | 15 |
| File Format | PDF HTM / HTML |
| Volume Number | 1 |
| Alternate Webpage(s) | http://www.dist.unige.it/ggnecco/MESA.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
