A CLASS OF DESCENT FOUR-TERM EXTENSION OF THE DAI-LIAO CONJUGATE GRADIENT METHOD BASED ON THE SCALED MEMORYLESS BFGS UPDATE

作者：Babaie Kafaki Saman^*; Ghanbari Reza

来源：Journal of Industrial and Management Optimization, 2017, 13(2): 649-658.

DOI：10.3934/jimo.2016038

• Unconstrained optimization
• large scale optimization
• conjugate gradient method
• sufficient descent property
• BFGS update
• global convergence

Hybridizing the three-term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Dai and Liao based on the scaled memoryless BFGS update, a one-parameter class of four-term conjugate gradient methods is proposed. It is shown that the suggested class of conjugate gradient methods possesses the sufficient descent property, without convexity assumption on the objective function. A brief global convergence analysis is made for uniformly convex objective functions. Results of numerical comparisons are reported. They demonstrate efficiency of a method of the proposed class in the sense of the Dolan-More performance profile.

• 出版日期2017-4