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SUMMARY OF NOTATION AND METHODS
THE CONJUGATE GRADIENT METHOD
COMPARISON OF METHODS
2 other sections not shown
A-matrix approximated bo Pi C. W. Merriam CM CM CM CM CM rH CM o rH CM rH rH CM to CM computation conjugate gradient method criterion Davidon Fletcher Powell derivative design function DFP method equations 12 equations 23 evaluation example problem flowchart Footnotes to Chapter function-gradient point-evaluations Gauss elimination H CM Hessian HUMAN OPERATOR MODEL I I I I I OOOOO rH inner product iteration linearly independent matrix Newton Raphson method Ol Ol one-dimensional minimization OOOOO OOOOO OOOOO Pi o H Pi rH positive definite positive definite matrix Powell and Steepest quadratic convergence quadratic function Raphson and Conjugate rH cd H rH CM CM rH O rH rH Ol rH rH rH rH rH XXXXX rH ro search direction st st st starting guess Starting Point 2,5 Starting Point 5,20 steepest descent method step symmetric matrix Theorem Trajectories trial X X X y(eb