On the Optimal Combination of Tensor Optimization Methods
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Abstract (arXiv)
We consider the minimization problem of a sum of a number of functions having Lipshitz p-th order derivatives with different Lipschitz constants. In this case, to accelerate optimization, we propose a general framework allowing to obtain near-optimal oracle complexity for each function in the sum separately, meaning, in particular, that the oracle for a function with lower Lipschitz constant is called a smaller number of times. As a building block, we extend the current theory of tensor methods and show how to generalize near-optimal tensor methods to work with inexact tensor step. Further, we investigate the situation when the functions in the sum have Lipschitz derivatives of a different order. For this situation, we propose a generic way to separate the oracle complexity between the parts of the sum. Our method is not optimal, which leads to an open problem of the optimal combination of oracles of a different order.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Feb 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2023Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysiscited by 11
- 2022Optimal Gradient Sliding and its Application to Distributed Optimization Under Similaritycited by 10
- 2026Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problemscited by 0
- 2026Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems13 Jan 2026
- 2023Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis15 Apr 2023
- 2022Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity30 May 2022