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A Lower Bound for the Heavy-Ball Method on Smooth Convex Functions

arXiv 2609.08656 ↗First posted 8 Sep 2026math.OC stat.MLcited by 0

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Abstract (arXiv)

Can the classical Heavy-Ball method, with arbitrary horizon-dependent parameters chosen in advance, achieve Nesterov's O(T-2) last-iterate rate on every smooth convex objective? We provide a negative answer. For every horizon T≥2 and every predetermined schedule with nonnegative step sizes and momenta in [0,1), there exists a convex 1-smooth objective, with initialization distance at most one and zero initial velocity, for which the last iterate of the Heavy-Ball method satisfies [ f(xT)-f^star=Omega!left(frac1Tαlog Tright), qquad α=frac1+√52. ] Thus even fully nonstationary, horizon-dependent tuning cannot give the classical Heavy-Ball method a Nesterov-rate guarantee on the smooth convex class.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 8 Sep 2026.

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References · 11

  1. 2014Analysis and Design of Optimization Algorithms via Integral Quadratic Constraintscited by 105
  2. 2014Global convergence of the Heavy-ball method for convex optimizationcited by 60
  3. 2023Provably Faster Gradient Descent via Long Stepscited by 20
  4. 2023Acceleration by Stepsize Hedging II: Silver Stepsize Schedule for Smooth Convex Optimizationcited by 20
  5. 2018Non-ergodic Convergence Analysis of Heavy-Ball Algorithmscited by 13
  6. 2023Provable non-accelerations of the heavy-ball methodcited by 11
  7. 2024Accelerated Objective Gap and Gradient Norm Convergence for Gradient Descent via Long Stepscited by 11
  8. 2023Accelerated Gradient Descent via Long Stepscited by 9
  9. 2026A lower bound for stepsize-based acceleration of gradient descentcited by 4
  10. 2026Improved Gradient Descent Lower Bounds Beyond Nesterovcited by 2
  11. 2026Stronger Lower Bounds for (Non-)Anytime Acceleration of Gradient Descentcited by 2

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