A Modified Self-Scaling Memoryless Broyden–Fletcher–Goldfarb–Shanno Method for Unconstrained Optimization

C. X. Kou () and Y. H. Dai ()
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C. X. Kou: Beijing University of Posts and Telecommunications
Y. H. Dai: Chinese Academy of Sciences

Journal of Optimization Theory and Applications, 2015, vol. 165, issue 1, No 10, 209-224

Abstract: Abstract The introduction of quasi-Newton and nonlinear conjugate gradient methods revolutionized the field of nonlinear optimization. The self-scaling memoryless Broyden–Fletcher–Goldfarb–Shanno (SSML-BFGS) method by Perry (Disscussion Paper 269, 1977) and Shanno (SIAM J Numer Anal, 15, 1247–1257, 1978) provided a good understanding about the relationship between the two classes of methods. Based on the SSML-BFGS method, new conjugate gradient algorithms, called CG_DESCENT and CGOPT, have been proposed by Hager and Zhang (SIAM J Optim, 16, 170–192, 2005) and Dai and Kou (SIAM J Optim, 23, 296–320, 2013), respectively. It is somewhat surprising that the two conjugate gradient methods perform more efficiently than the SSML-BFGS method. In this paper, we aim at proposing some suitable modifications of the SSML-BFGS method such that the sufficient descent condition holds. Convergence analysis of the modified method is made for convex and nonconvex functions, respectively. The numerical experiments for the testing problems from the Constrained and Unconstrained Test Environment collection demonstrate that the modified SSML-BFGS method yields a desirable improvement over CGOPT and the original SSML-BFGS method.

Keywords: Unconstrained optimization; Self-scaling; Conjugate gradient method; Quasi-Newton method; Global convergence; Improved Wolfe line search; 49M37; 90C30 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10957-014-0528-4

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