The information-theoretic complexity of differentiable functions
Matthijs Ruijgrok

TL;DR
This paper introduces the V-complexity, an information-theoretic measure for differentiable functions based on piecewise constant approximations, linking it to compression and applying it to diffusion models.
Contribution
It formalizes a new complexity measure for differentiable functions and connects it to compression algorithms and effective complexity in complex systems.
Findings
V-complexity formalizes intuitions about function simplicity.
V-complexity correlates with compression measures like RLE and LZ77.
Application to diffusion shows V-complexity dynamics in physical models.
Abstract
A measure for the complexity of a differentiable function f(x) on an interval is introduced. It is based on approximations of the function by piecewise constant functions. The measure takes into account the quality of the approximation and the number of intervals in the approximating function. This measure, called the V-complexity of f(x), is shown to formalize some intuitions about the simplicity or complexity of f(x). The V-complexity is then compared to another measure of complexity, namely how compressible an approximation of f(x) is. It is hypothesized that V-complexity is equivalent to the compression measure, in the case of the Run Length Encoding and the Lempel Ziv 77 algorithms. V-complexity can be used as an ingredient in the definition of the Effective Complexity (EC) of a Complex System. When the perceived regularities of such a system are described by a differentiable…
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