Computational complexity in analysis and geometry

Computable analysis studies problems involving real numbers, sets and functions from the viewpoint of computability. Elements of uncountable sets (such as real numbers) are represented through approximation and processed by Turing machines. However, application of this approach to computational complexity has been limited in generality. In this thesis, we present a refined framework that is suitable for discussing computational complexity. The key idea is to use (a certain class of) string functions as names representing these objects. These are more expressive than infinite sequences, which served as names in prior work that formulated complexity in more restricted settings. An important advantage of using string functions is that we can define their size in the way inspired by higher-type complexity theory. This enables us to talk about computation on string functions whose time or space is bounded polynomially in the input size, giving rise to more general analogues of the classes P, NP, and PSPACE. We also define NP- and PSPACE-completeness under suitable many-one reductions.\n\nBecause our framework separates machine computation and semantics, it can be applied to problems on sets of interest in analysis once we specify a suitable representation (encoding). As prototype applications, we consider the complexity of several problems whose inputs and outputs are real numbers, real sets, and real functions. The latter two cannot be represented succinctly using existing approaches based on infinite sequences, so ours is the first treatment of functions on them. As an interesting example, the task of numerical algorithms for solving the initial value problem of differential equations is naturally viewed as an operator taking real functions to real functions. Because there was no complexity theory for operators, previous results could only state how complex the solution can be. We now reformulate them and show that the operator itself is polynomial-space complete. We survey some of such complexity results involving real numbers and cast them in our framework.