Abstract
Only in rare cases, can problems in function spaces be solved analytically and exactly. In most occasions, it is necessary to apply computational methods to approximate the solutions. In this chapter, we discuss some of the basic general strategies that can be applied. First, we present several connections between optimization and discretization, along with their role in the problem-solving process. Next, we introduce the idea of iterative procedures, and discuss some abstract tools for proving their convergence. Finally, we comment some ideas that are useful to simplify or reduce the problems, in order to make them tractable or more efficiently solved.
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Or even a large-dimensional X by a subspace X n of smaller dimension.
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Peypouquet, J. (2015). Problem-Solving Strategies. In: Convex Optimization in Normed Spaces. SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-319-13710-0_5
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DOI: https://doi.org/10.1007/978-3-319-13710-0_5
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13709-4
Online ISBN: 978-3-319-13710-0
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