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Piqin Shi, Chengjing Wang, Can Xiang, Peipei Tang, Numerical computation of entropy-regularized quadratic optimization problems

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DOI: 10.23952/jano.6.2024.1.03
Volume 6, Issue 1, 1 April 2024, Pages 59-70

 

Abstract. Entropy-regularized quadratic optimization problems are a special class of optimization problems with wide applications in various fields, such as transportation and machine learning. In this paper, we apply the augmented Lagrangian method to this problem with its subproblem solved by the block coordinate descent method. Under certain mild conditions, we analyze the global convergence of this algorithm. Numerical experiments demonstrate the effectiveness of this algorithm.

 

How to Cite this Article:
P. Shi, C. Wang, C. Xiang, P. Tang, Numerical computation of entropy-regularized quadratic optimization problems, J. Appl. Numer. Optim. 6 (2024), 59-70.