# Succession of such steps the cg iteration attempts to

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Unformatted text preview: ng a distance αn (the step length) in the direction pn−1 (the search direction) By a succession of such steps, the CG iteration attempts to ﬁnd a minimum of a nonlinear equation Which function to minimize? 7 / 25 Conjugate gradients as an optimization algorithm (cont’d) Cannot use e knowing x∗ A or e 2 A as neither can be evaluated without On the other hand, given A and b and x ∈ IRm , the quantity 1 φ(x) = x Ax − x b 2 can certainly be evaluated as en Like e 2, A 2 A = = = = en Aen = (x∗ − xn ) A(x∗ − xn ) xn Axn − 2xn Ax∗ + x∗ Ax∗ xn Axn − 2xn b + x∗ b 2φ(xn ) + constant it must achieve its minimum uniquely at x = x∗ 8 / 25 Conjugate gradients as an optimization algorithm (cont’d) The CG iteration can be interpreted as an iterative process for minimizing the quadratic function φ(x) of x ∈ IRm At each step, an iterate xn = xn−1 + αn pn−1 is computed that minimizes φ(x) over all x in the one dimensional space xn−1 + pn−1 It can be readily conﬁrmed that the formula αn = rn −1 rn −1 pn−1 Apn−1 ensures αn is optimal in the sense among all step lengths α What makes the CG iteration remarkable is the choice of the search direction pn−1 , which has the special property that minimizing φ(x) over xn−1 + pn−1 actually minimizes it over all of Kn 9 / 25 Analogy between CG iteration and Lanczos iteration A close analogy between CG iteration for solving Ax = b and the Lanczos iteration for ﬁnding eigenvalu...
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