期刊
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
卷 104, 期 3, 页码 539-558出版社
KLUWER ACADEMIC/PLENUM PUBL
DOI: 10.1023/A:1004633524446
关键词
nonsmooth convex optimization; Moreau-Yosida regularization; strong convexity; inexact function and gradient evaluations; BFGS method
We propose an implementable BFGS method for solving a nonsmooth convex optimization problem by converting the original objective function into a once continuously differentiable function by way of the Moreau-Yosida regularization. The proposed method makes use of approximate function and gradient values of the Moreau-Yosida regularization instead of the corresponding exact values. We prove the global convergence of the proposed method under the assumption of strong convexity of the objective function.
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