A metric-routing version of continual learning task ordering is NP-hard, and a Christofides-style polynomial algorithm achieves 3/2 and 3/2+r^{1-T} approximation ratios against an upper-bound objective.
Topology-aware em bedding memory for continual learning on expanding networks,
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Algorithm Design for Continual Learning in IoT Networks
A metric-routing version of continual learning task ordering is NP-hard, and a Christofides-style polynomial algorithm achieves 3/2 and 3/2+r^{1-T} approximation ratios against an upper-bound objective.