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Related Experiment Video

Updated: Sep 24, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Local convergence of tensor methods.

Nikita Doikov1, Yurii Nesterov2

  • 1Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain (UCL), Louvain-la-Neuve, Belgium.

Mathematical Programming
|May 10, 2022
PubMed
Summary
This summary is machine-generated.

This study analyzes high-order Tensor Methods for convex optimization. We prove local superlinear convergence for composite objectives, establishing convergence rates and global bounds.

Keywords:
Convex optimizationHigh-order methodsLocal convergenceProximal methodsTensor methodsUniform convexity

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Area of Science:

  • Optimization Theory
  • Numerical Analysis
  • Convex Analysis

Background:

  • Convex optimization problems with composite objectives are fundamental in many scientific and engineering fields.
  • High-order methods offer potential for faster convergence but require careful analysis.
  • Understanding local convergence properties is crucial for developing efficient algorithms.

Purpose of the Study:

  • To investigate the local convergence properties of high-order Tensor Methods for composite convex optimization.
  • To establish theoretical guarantees for the convergence rate and behavior of these methods.
  • To explore strategies for globalizing the convergence of Tensor Methods.

Main Methods:

  • Analysis of local convergence using concepts of uniform convexity and Lipschitz continuity of high-order derivatives.
  • Derivation of convergence rates in terms of function value and subgradient norm.
  • Development of global complexity bounds for the Composite Tensor Method.
  • Application of inexact proximal iterations for global convergence.

Main Results:

  • Justification of local superlinear convergence for high-order Tensor Methods under specific assumptions.
  • Establishment of convergence guarantees for both function value and minimal subgradient norm.
  • Discussion of global complexity bounds for convex and uniformly convex scenarios.
  • Demonstration of globalization strategies using inexact proximal iterations.

Conclusions:

  • High-order Tensor Methods exhibit promising local superlinear convergence for composite convex optimization.
  • The theoretical framework provides a solid foundation for algorithm design and analysis.
  • Globalization techniques effectively extend local convergence properties to a wider domain.