Im going to meander on a theme of the possibility of fractal computational algorithms that do not require heuristic parametric adjustment, which, according to Louis Figo, are best applied to machine learning and Big Data partition-clustering applications. Of course, we are, eventually, applying such fractal modeling techniques into process here that operate through non-stationary damping of propagation between a diffusion-impeded sheaf of semi-heterogeneous and pseudo-parallel, decaying parameters that are themselves non-trivially sequentially dependent, and certainly non-abelian. We can numerically-produce structures that are algebraically-primitive, perhaps, but one must engage the phenomenology of the problem to optimize simulations of macro-combinatorial emergence to map non-subjective/non-mechanistic finite-invariant groupoid thresholds to empirical observations of non-theoretical or non-ideal topological cohomologies. It is useful to recognize real-world instantiations of these processes to recognize latent parameters, although we find a hermeneutic circle at this approach. Pragmatically, there will be no sense to overcome the necessity and sufficiency of infinitely asynchronic stopping times for interactive sub-routines, which are inherently stochastic, although self-similar, and are themselves vital for evolving reflexive pseudo-memory emulation, as non-periodic encodings of differential arrival for subsystem feedback peaks, which would not always themselves bear the greatest preference for continuity, are often error-prone, with respect to their neighborhoods, and may exceed their control matrices, requiring piecemeal truncation, which can be accomplished as a functional self-corrective process by an improved algorithm. These systems are never truly closed, nor are they monotonic. I see how we lack computing power for dynamic elegance in expanding processing to a greater multiplicity of time scales and forcing parameters with realistic asymptotic behavior and higher-order fluctuating critical moments fractionally propelling non power-law preserving initial jump conditions for submetric manifolds. ...and then onto reciprocal entropy and mutual inter-perturbation qua self-intersection into non-stationary, taphonomic constructions.
Posted on: Tue, 27 Jan 2015 12:24:16 +0000
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