I attached the questions with answers but Can you give me each x_i as a linear combination of the p_i with matlab code

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I attached the questions with answers but Can you give me
each x_i as a linear combination of the p_i with matlab code?

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1. Use the linear conjugate gradient method to solve Ax=b where A = [5-20-1 -2] [-2 3-3 41] [0-35-42] [-14-4113] [-21 238] and b = [1 2 3 4 5]^T. Start with x_0 = [1 1 1 1 1]^T. After completing the algorithm, verify that the p_i are linearly independent and express each x_i as a linear combination of the p_i. 2. Show that the second strong Wolfe condition (3.7b) implies the curvature condition (6.7).

1. The linear conjugate gradient method is used to solve the equation Ax=b, where A is a matrix and b is a vector. The method starts with an initial guess for the solution vector x, and then iteratively improves the guess by taking linear combinations of the columns of A. The p_i are the columns of A, and the x_i are the iterations of the algorithm. The algorithm converges when the difference between successive iterations is less than a tolerance. i.e Find the eigenvalues and eigenvectors of A= [3-2] [-25] The eigenvalues of A are 3 and 5. The eigenvectors are [1, 0] and [0, 1]. The p_i are linearly independent if they are not linearly dependent on each other. This means that they are not proportional to each other, and they span the space of A. In other words, any vector in the space of A can be expressed as a linear

combination of the p_i. The x i can be expressed as a linear combination of the p_i by solving the equation Ax=b for x. This can be done using Gaussian elimination or by using the linear conjugate gradient method. 2. The second strong Wolfe condition states that the objective function must decrease by a certain amount on each iteration. This condition is necessary to guarantee that the algorithm will converge. The curvature condition is a necessary condition for the objective function to have a unique minimum. It states that the objective function must be bounded below and that the gradient must be Lipschitz continuous. The second strong Wolfe condition implies the curvature condition because it is a necessary condition for the objective function to decrease on each iteration. If the objective function did not decrease on each iteration, then it would not have a unique minimum. Therefore, the second strong Wolfe condition implies the curvature condition.

Explanation: 1. The linear conjugate gradient method is used to solve the equation Ax=b, where A is a matrix and b is a vector. The method starts with an initial guess for the solution vector x, and then iteratively improves the guess by taking linear combinations of the columns of A. The p_i are the columns of A, and the x_i are the iterations of the algorithm. The algorithm converges when the difference between successive iterations is less than a tolerance. The p_i are linearly independent if they are not linearly dependent on each other. This means that they are not proportional to each other, and they span the space of A. In other any vector in the space of A can be expressed as a linear combination of the p_i. Wo The x i can be expressed as a linear combination of the p_i by solving the equation Ax=b for x. This can be done using Gaussian elimination or by using the linear conjugate gradient method.

2. The second strong Wolfe condition implies the curvature condition because it is a necessary condition for the objective function to decrease on each iteration. If the objective function did not decrease on each iteration, then it would not have a unique minimum. Therefore, the second strong Wolfe condition implies the curvature condition. This condition is necessary to guarantee that the algorithm will converge. The curvature condition is a necessary condition for the objective function to have a unique minimum. It states that the objective function must be bounded below and that the gradient must be Lipschitz continuous.