I was given the following question as part of a homework assignment. Any help would be greatly appreciated!
The following image shows the steps of a preliminary version of the conjugate gradient algorithm and was taken from the book Numerical Optimization by Jorge Nocedal and Stephen J. Wright.
We know from a previous theorem that $p_{k+1} \in \text{span}\{r_0, A r_0, \dots, A^{k+1} r_0\}$, $A \in \mathbb{R}^{n \times n}$ is symmetric positive definite and $r_k \in \mathbb{R}^{n \times 1}$, therefore $$ p_{k+1} = \xi_0 r_0 + \xi_1 A r_0 + \dots + \xi_{k+1} A^{k+1} r_0 $$ Show that $\xi_{k+1} = (-1)^{k+1}(\alpha_0 \times \alpha_1 \times \dots \times \alpha_k)$ using the preliminary version of the conjugate gradient method (Algorithm 5.1).
I have tried to solve this problem by Mathematical induction on $k$ since the base step is easy to prove, however, I haven't figured out a way to succesfully complete the induction step. I have tried using the following results:
- $r_{k+1} = r_k + \alpha_{k} A r_{k}$
- $r_k^T p_i = 0$ for $i = 0, 1, \dots, k-1$
- $p_k^T A p_i = 0$ for $i = 0, 1, \dots, k-1$
- $r_k^T r_i = 0$ for $i = 0, 1, \dots, k-1$
Note: the instructions never stated this, but I assumed that $\xi_0 = -1$ since it's essentially a definition in Algorithm 5.1.