algorithms-optimization / Convex optimization

Lower Bounds for Stepsize-Based Acceleration of Gradient Descent

Recorded as partial: the bound is Omega(T^-1.9319) against an achievable O(T^-1.2716), so it rules out reaching the optimal rate without pinning down the true one.

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algorithms-optimizationAug 11, 2026Significance 10/100Registry: unreviewed

Lower Bounds for Stepsize-Based Acceleration of Gradient Descent

Prior state unknown→proved

Recorded as partial: the bound is Omega(T^-1.9319) against an achievable O(T^-1.2716), so it rules out reaching the optimal rate without pinning down the true one.

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Recorded as partial: the bound is Omega(T^-1.9319) against an achievable O(T^-1.2716), so it rules out reaching the optimal rate without pinning down the true one.

Recorded as partial: the bound is Omega(T^-1.9319) against an achievable O(T^-1.2716), so it rules out reaching the optimal rate without pinning down the true one.

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Connected research record

Lower Bounds for Stepsize-Based Acceleration of Gradient Descent — Mathematical Frontier Network