Research map / Learning theory and algorithms / Sample complexity
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Choose the acceleration parameters in advance and use Tensor Extragradient Method for the inner gradient-norm reduction | Optimal Tensor Method · Smooth convex optimization, p-th order | p-th order oracle complexity: \mathcal{O} \left( \epsilon^{-2/(3p+1)} \log (1/ \epsilon)\right) -> 5D_p\cdot\left( {L_pR^{p+1}}/{\epsilon} \right)^{\f | Helped |
| Use binary search to select the stepsize at each iteration | Near-optimal tensor methods · Smooth convex optimization, p-th order | p-th order oracle complexity: \mathcal{O} \left( \epsilon^{-2/(3p+1)} \log (1/ \epsilon)\right) | Hurt |
Closest papers by meaning
- Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness (NeurIPS 2021 · similarity 0.91)
- Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values (NeurIPS 2024 · similarity 0.91)
- Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity (ICLR 2025 · similarity 0.90)
- On The Complexity of First-Order Methods in Stochastic Bilevel Optimization (ICML 2024 · similarity 0.88)
- Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints (NeurIPS 2024 · similarity 0.88)
- Second-Order Min-Max Optimization with Lazy Hessians (ICLR 2025 · similarity 0.88)
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