Optimization problems with low SWaP tactical Computing
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyDecision MakingSimilar Papers 제목 키워드 기반
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of float…
Few-Shot LearningQuantizationFine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation
Large language models (LLM) have achieved remarkable outcomes in addressing complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet few works have explored the potential of LLM in…
MathQuantum Machine LearningPredicting Tactical Solutions to Operational Planning Problems under Imperfect Information
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In t…
BIG-bench Machine LearningManagementFinding Optimal Solutions to Token Swapping by Conflict-based Search and Reduction to SAT
We study practical approaches to solving the token swapping (TSWAP) problem optimally in this short paper. In TSWAP, we are given an undirected graph with colored vertices. A colored token is placed in each vertex. A pai…
Multi-Agent Path FindingPredicting Tactical Solutions to Operational Planning Problems under Imperfect Information
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactical descriptions of operational solutions…
BIG-bench Machine LearningStochastic Optimization