Quantitative Coding and Complexity Theory of Continuous Data

Specifying a computational problem requires fixing encodings for input and output: encoding graphs as adjacency matrices, characters as integers, integers as bit strings, and vice versa. For such discrete data, the actual encoding is usually straightforward and/or complexity-theoretically inessential (up to polynomial time, say); but concerning continuous data, already real numbers naturally suggest various encodings (so-called REPRESENTATIONS) with very different properties, ranging from the computably 'unreasonable' binary expansion via qualitatively to polynomially and even linearly complexity-theoretically 'reasonable' signed-digit expansion. But how to distinguish between un/suitable encodings of other spaces common in Calculus and Numerics, such as Sobolev? With respect to qualitative computability, Kreitz and Weihrauch (1985) had identified ADMISSIBILITY as crucial criterion for a representation over the Cantor space of infinite binary sequences to be 'reasonable'; cmp. [doi:10.1007/11780342_48]. Refining computability over topological to complexity over metric spaces, we develop the theory of POLYNOMIAL/LINEAR ADMISSIBILITY as two quantitative refinements of qualitative admissibility. We also rephrase quantitative admissibility as quantitative continuity of both the representation and of its set-valued inverse, the latter adopting from [doi:10.4115/jla.2013.5.7] a new notion of 'sequential' continuity for multifunctions. By establishing a quantitative continuous selection theorem for multifunctions between compact ultrametric spaces, we can extend our above quantitative MAIN THEOREM from functions to multifunctions aka search problems. Higher-type complexity is captured by generalizing Cantor's (and Baire's) ground space for encodings to other (compact) ULRAmetric spaces.