An optimization derivation of the method of conjugate gradients

We give a derivation of the method of conjugate gradients based on the requirement that each iterate minimizes a strictly convex quadratic on the space spanned by the previously observed gradients. Rather than verifying that the search direction has the correct properties, we show that generation of such iterates is equivalent to generation of orthogonal gradients which gives the description of the direction and the step length. Our approach gives a straightforward way to see that the search direction of the method of conjugate gradients is a negative scalar times the gradient of minimum Euclidean norm on the subspace generated so far.