The goal of this problem is to prove that, when the conjugate-gradient method is used to solve Ax = b, the norms of the errors decrease monotonically. %3D (i) Prove that the conjugate directions satisfy pPj >0 for all i and j. (ii) Prove that the estimates of the solution satisfy ||x; || > ||xi-1|| for all i. (Here the 2-norm is used.) (iii) Let x, solve Ax b. Prove that ||x; – x || < ||X;-1 – x || for all i.

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**Problem 2.12: Conjugate Gradient Method**

The aim of this problem is to prove that, when the conjugate-gradient method is used to solve the equation \( Ax = b \), the norms of the errors decrease monotonically. The problem is divided into the following parts:

(i) **Conjugate Directions:** Prove that the conjugate directions satisfy \( p_i^T p_j \geq 0 \) for all \( i \) and \( j \).

(ii) **Estimates of the Solution:** Prove that the estimates of the solution satisfy \( \|x_i\| \geq \|x_{i-1}\| \) for all \( i \). (Here, the 2-norm is used.)

(iii) **Solution Error Norms:** Let \( x_* \) solve \( Ax = b \). Prove that \( \|x_i - x_*\| \leq \|x_{i-1} - x_*\| \) for all \( i \).
Transcribed Image Text:**Problem 2.12: Conjugate Gradient Method** The aim of this problem is to prove that, when the conjugate-gradient method is used to solve the equation \( Ax = b \), the norms of the errors decrease monotonically. The problem is divided into the following parts: (i) **Conjugate Directions:** Prove that the conjugate directions satisfy \( p_i^T p_j \geq 0 \) for all \( i \) and \( j \). (ii) **Estimates of the Solution:** Prove that the estimates of the solution satisfy \( \|x_i\| \geq \|x_{i-1}\| \) for all \( i \). (Here, the 2-norm is used.) (iii) **Solution Error Norms:** Let \( x_* \) solve \( Ax = b \). Prove that \( \|x_i - x_*\| \leq \|x_{i-1} - x_*\| \) for all \( i \).
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