A Mixed Hybrid Conjugate Gradient Method for Unconstrained Engineering Optimization Problems
A new hybrid of the conjugate gradient (CG) method that combines the features of five different CG methods is proposed. The corresponding CG algorithm generated descent directions independent of line search procedures. With the standard Wolfe line search conditions, the algorithm was shown to be globally convergent. Based on numerical experiments with selected large-scale benchmark test functions and comparison with classical methods, the method is very promising and competitive.
KeywordsUnconstrained optimization problems Hybrid nonlinear conjugate gradient method Descent direction Global convergence Standard Wolfe line search conditions Numerical experiment
The support of the DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the CoE. The authors also wish to appreciate Covenant University Centre for Research, Innovation and Discovery (CUCRID) for funding the publication of this research output.
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