A derandomization technique reduces random bits to O(log k) for k-server on HSTs, enabling the first poly-time randomized k-server algorithm on general metrics with polylog competitive ratio.
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Supervised learning on states collected from ERCSPP relaxations identifies dominant states effectively within instances but shows declining performance on unseen instances.
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Randomized $k$-server in polynomial time
A derandomization technique reduces random bits to O(log k) for k-server on HSTs, enabling the first poly-time randomized k-server algorithm on general metrics with polylog competitive ratio.
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Learning Dominant States in Elementary Resource Constrained Shortest Path Problems
Supervised learning on states collected from ERCSPP relaxations identifies dominant states effectively within instances but shows declining performance on unseen instances.