Abstract
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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Rauf, A.I., Fukushima, M. Globally Convergent BFGS Method for Nonsmooth Convex Optimization1. Journal of Optimization Theory and Applications 104, 539–558 (2000). https://doi.org/10.1023/A:1004633524446
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DOI: https://doi.org/10.1023/A:1004633524446