This paper defines and proves the safety properties of non‑commutative objective matrices within the LPOS framework.It formalizes four essential conditions—boundedness, stability, monotonic improvement, and robustness—each expressed through spectral and Lipschitz constraints.The study demonstrates that LPOS ensures convergence without divergence, maintaining structural consistency while reducing computational redundancy.This formulation provides a mathematical foundation for safe optimization in distributed AI systems and energy‑efficient computation.