Complexity and mission computability of adaptive computing systems

Abstract

There is a subset of computational problems that are computable in polynomial time for which an existing algorithm may not complete due to a lack of high performance technology on a mission field. We define a subclass of deterministic polynomial time complexity class called mission class, as many polynomial problems are not computable in mission time. By focusing on such subclass of languages in the context for successful military applications, we also discuss their computational and communicational constraints. We investigate feasible (non)linear models that will minimize energy and maximize memory, efficiency, and computational power, and also provide an approximate solution obtained within a pre-determined length of computation time using limited resources so that an optimal solution to a language could be determined.

Optimization problems with low SWAP tactical computing

“…. , x n may be moved around and be SWaPped within the executable time, in particular, when some of the computations can be approximated and thus no longer need to use up the limited resources (see [18,Defn. 1]), or when technologies x i are removed or more are added to the SWaP platform.…”

“…In our definition of mission complexity, 18 we restrict to computations that are solvable in polynomial time. This imples that for each 1 ≤ i ≤ n, M i (t i ) is computable in the order of O(n k ), where k is some integer.…”