@article{eprints1768,
           title = {Information Complexity of Functional Optimization Problems and Their Approximation Schemes},
         journal = {Mathematics in Engineering, Science and Aerospace (MESA)},
          author = {Giorgio Gnecco and Marcello Sanguineti},
            year = {2010},
          number = {3},
          volume = {1},
           pages = {303--317},
             url = {http://eprints.imtlucca.it/1768/},
        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 approximating the original functional one, is considered. Also for such problems, the information complexity is estimated.}
}