Abstract
The Barzilai–Borwein (BB) gradient method is efficient for solving large-scale unconstrained problems to modest accuracy due to its ingenious stepsize which generally yields nonmonotone behavior. In this paper, we propose a new stepsize to accelerate the BB method by requiring finite termination for minimizing the two-dimensional strongly convex quadratic function. Based on this new stepsize, we develop an efficient gradient method for quadratic optimization which adaptively takes the nonmonotone BB stepsizes and certain monotone stepsizes. Two variants using retard stepsizes associated with the new stepsize are also presented. Numerical experiments show that our strategies of properly inserting monotone gradient steps into the nonmonotone BB method could significantly improve its performance and our new methods are competitive with the most successful gradient descent methods developed in the recent literature.
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Acknowledgements
The authors would like to thank the associate editor and the anonymous referees for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (Grant Nos. 11701137, 11631013, 12071108, 11671116, 11991021, 12021001), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA27000000), Beijing Academy of Artificial Intelligence (BAAI), China Scholarship Council (No. 201806705007), Natural Science Foundation of Hebei Province (Grant No. A2021202010), and USA National Science Foundation (Grant Nos. 2110722, 1819161).
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Huang, Y., Dai, YH., Liu, XW. et al. On the acceleration of the Barzilai–Borwein method. Comput Optim Appl 81, 717–740 (2022). https://doi.org/10.1007/s10589-022-00349-z
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DOI: https://doi.org/10.1007/s10589-022-00349-z