Unverified50% confidenceFactExact time
There 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.
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10/15/2026
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UnverifiedInformation-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.79% similarUnverifiedHigher sample complexity means the concept is harder for the learning algorithm and is regarded as a signal of higher semantic complexity.75% similarUnverified内点法在理论上具有多项式时间复杂度保证，而单纯形法最坏情况下呈指数级复杂度74% 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.74% similarUnverifiedHigh-dimensional structure learning of Gaussian DAGs faces a statistical-computational gap: methods with sharp sample complexity rely on computationally expensive subset searches and require a pre-specified in-degree upper bound, while polynomial-time algorithms exhibit comparatively inferior sample complexity.72% similar
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