Globally Convergent BFGS Method for Nonsmooth Convex Optimization1

DOI

この論文をさがす

説明

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.

収録刊行物

詳細情報 詳細情報について

問題の指摘

ページトップへ