On three-term conjugate gradient algorithms for unconstrained optimization

摘要

This paper presents a project for three-term conjugate gradient algorithms development. The search direction of the algorithms from this class has three terms and is computed as modifications of the classical conjugate gradient algorithms to satisfy both the descent and the conjugacy conditions. An example of three-term conjugate gradient algorithm from this class, as modifications of the classical and well known Hestenes and Stiefel or of the CG_DESCENT by Hager and Zhang conjugate gradient algorithms, satisfying both the descent and the conjugacy conditions is presented. These properties are independent of the line search. Also, the algorithm could be considered as a modification of the memoryless BFGS quasi-Newton method. The new approximation of the minimum is obtained by the general Wolfe line search and using by now a standard acceleration technique developed by Andrei. The proposed three-term conjugate gradient algorithm substantially outperforms the well known Hestenes and Stiefel conjugate gradient algorithm, as well as the more elaborated CG_DESCENT algorithm. A number of 5 applications from MINPACK-2 test problems collection, with 106 variables, prove that the suggested three-term conjugate gradient algorithm is top performer versus CG_DESCENT.