The conjugate gradient method can be derived from several different perspectives, including specialization of the conjugate direction method for optimization, and variation of the Lanczos iteration for eigenvalue problems.
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This gives the following method for solving the equation "'b "'} } : find a sequence of " n " conjugate directions, and then compute the coefficients.
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His mathematical contributions include quasi-Newton methods, particularly the Davidon-Fletcher-Powell formula and the Powell's Symmetric Broyden formula, conjugate direction method ( also called Powell's method ), and radial basis function.
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The conjugate direction method is imprecise in the sense that no formulae are given for selection of the directions \ boldsymbol { p } _ 0, \ boldsymbol { p } _ 1, \ boldsymbol { p } _ 2, \ ldots.
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