A regularized limited memory BFGS method for nonconvex unconstrained minimization |
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Authors: | Tao-Wen Liu |
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Institution: | 1. College of Mathematics and Econometrics, Hunan University, 410082, Changsha, China
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Abstract: | The limited memory BFGS method (L-BFGS) is an adaptation of the BFGS method for large-scale unconstrained optimization. However, The L-BFGS method need not converge for nonconvex objective functions and it is inefficient on highly ill-conditioned problems. In this paper, we proposed a regularization strategy on the L-BFGS method, where the used regularization parameter may play a compensation role in some sense when the condition number of Hessian approximation tends to become ill-conditioned. Then we proposed a regularized L-BFGS method and established its global convergence even when the objective function is nonconvex. Numerical results show that the proposed method is efficient. |
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