This paper presents a simple unifying framework for a wide class of conjugate directions algorithms whose iterates minimize some quadratic functional over a subspace. Our approach is motivated by its advantages for nonlinear minimization, but the purpose of this paper is to present the greatly simplified convergence analysis that results for the linear case.