Unverified50% confidenceFactExact time
Information-theoretic lower bounds characterize the best performance achievable by any algorithm (even exponential-time) without computational resource constraints, corresponding in this problem to Õ(md+k); computational lower bounds, by contrast, assume the algorithm must run in polynomial time, and the resulting bounds are usually larger.
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10/15/2026
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UnverifiedThere is an essential separation between the information-theoretically optimal regret Õ(md+k) and the regret achievable in polynomial time; this gap is inherent to computational complexity rather than a shortcoming of algorithm design.79% similarUnverifiedIn a continuous convex action space, discretizing the action space and applying the discrete algorithm can yield Õ(md+k) regret, but the number of grid points required grows exponentially with the dimension d, making the algorithm infeasible in polynomial time.77% similarUnverifiedComputing power constraints may lead to a divergence in technical approaches: regions with sufficient computing power pursue larger-scale models, while regions with constrained computing power focus on model compression and efficient inference.74% similarUnverifiedMatrix multiplication is typically compute-bound in high-batch inference scenarios, while autoregressive decoding with a batch size of 1 is almost invariably bandwidth-bound.74% similarUnverifiedMany asymptotically superior algorithms, due to large hidden constant factors or implementation complexity, actually perform worse than highly optimized Dijkstra or A* at real-world data scales.74% similar
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