A unified framework for inexact adaptive stepsizes in the gradient methods, the conjugate gradient methods and the quasi-Newton methods for strictly convex quadratic optimization

Zexian Liu

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arXiv · 2604.20506

A unified framework for inexact adaptive stepsizes in the gradient methods, the conjugate gradient methods and the quasi-Newton methods for strictly convex quadratic optimization

Zexian Liu

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Abstract

The inexact adaptive stepsizes for the conjugate gradient method and the quasi-Newton method are very rare. The exact stepsizes in the gradient method, the conjugate gradient method and the quasi-Newton method for strictly convex quadratic optimization have a unified framework, while the unified framework for inexact adaptive stepsizes in the gradient method, the conjugate gradient method and the quasi-Newton method for strictly convex quadratic optimization still remains unknown. Based on the above observations, we propose a unified framework for inexact adaptive stepsizes in the gradient method, the conjugate gradient method and the quasi-Newton method for strictly convex quadratic optimization, which is called approximately optimal stepsize. The global convergence and the convergence rate of the gradient method with the approximately optimal stepsize are established by exploring the relation between the approximately optimal stepsize and the famous Barzilai-Borwein (BB) stepsizes. Some numerical results are presented, which confirm the remarkable numerical advantage of the gradient method, the conjugate gradient method and the quasi-Newton method with the unified framework for inexact adaptive stepsizes. Some open problems about the gradient method, the conjugate gradient method and the quasi-Newton method with approximately optimal stepsize are raised.

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BibTeX RIS

Zexian Liu. 2026-04-22. A unified framework for inexact adaptive stepsizes in the gradient methods, the conjugate gradient methods and the quasi-Newton methods for strictly convex quadratic optimization. https://arxiv.org/abs/2604.20506

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