New conjugate gradient algorithms based on self-scaling memoryless Broyden–Fletcher–Goldfarb–Shanno method

Three new procedures for computation the scaling parameter in the self-scaling memoryless Broyden–Fletcher–Goldfarb–Shanno search direction with a parameter are presented. The first two are based on clustering the eigenvalues of the self-scaling memoryless Broyden–Fletcher–Goldfarb–Shanno iteration matrix with a parameter by using the determinant or the trace of this matrix. The third one is based on minimizing the measure function of Byrd and Nocedal (SIAM J Numer Anal 26:727–739, 1989). For all these three algorithms the sufficient descent condition is established. The stepsize is computed using the standard Wolfe line search. Under the standard Wolfe line search the global convergence of these algorithms is established. By using 80 unconstrained optimization test problems, with different structures and complexities, it is shown that the performances of the self-scaling memoryless algorithms based on the determinant or on the trace of the iteration matrix or on minimizing the measure function are better than those of CG_DESCENT (version 1.4) with Wolfe line search (Hager and Zhang in SIAM J Optim 16:170–192, 2005), the self-scaling memoryless BFGS algorithms with scaling parameter proposed by Oren and Spedicato (Math Program 10:70–90, 1976) and by Oren and Luenberger (Manag Sci 20:845–862, 1974), LBFGS by Liu and Nocedal (Math Program 45:503–528, 1989) and the standard BFGS. The self-scaling memoryless algorithm based on minimizing the measure function of Byrd and Nocedal is slightly top performer versus the same algorithms based on the determinant or on the trace of the iteration matrix.