# Optimization problems with low SWaP tactical Computing

## TL;DR

This paper explores optimization strategies for low SWaP tactical computing environments, emphasizing the importance of efficient algorithms and decision tradeoffs under strict resource constraints.

## Contribution

It analyzes the complexity of optimization methods suitable for resource-limited, tactical computing scenarios, highlighting the need for fast, simple algorithms.

## Key findings

- Limited set of algorithms suitable for low SWaP environments
- Tradeoffs between decision speed and computational efficiency
- Complexity analysis of optimization strategies

## Abstract

In a resource-constrained, contested environment, computing resources need to be aware of possible size, weight, and power (SWaP) restrictions. SWaP-aware computational efficiency depends upon optimization of computational resources and intelligent time versus efficiency tradeoffs in decision making. In this paper we address the complexity of various optimization strategies related to low SWaP computing. Due to these restrictions, only a small subset of less complicated and fast computable algorithms can be used for tactical, adaptive computing.