Complexity and mission computability of adaptive computing systems

TL;DR

This paper introduces the 'mission class', a subset of polynomial-time problems relevant to military applications, analyzing their computational constraints and proposing models for efficient approximate solutions within limited resources.

Contribution

It defines the mission class of problems, explores their computational and communication constraints, and proposes models for resource-efficient approximate solutions.

Findings

Identification of the mission class as a subset of polynomial problems

Analysis of computational and communication constraints in military contexts

Proposed models for energy-efficient and resource-limited approximate solutions

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.