Graduate Understanding of conjugate directions

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The discussion centers on the concept of conjugate directions in the context of the conjugate gradient method, specifically referencing J. R. Shewchuk's paper. It clarifies that two vectors, d1 and d2, are considered A-orthogonal if their transformation results in orthogonal vectors, satisfying the condition d2T*A*d1 = 0. There is confusion regarding whether the transformed vectors should also satisfy d'2T*d'1 = 0, leading to the derived condition d2T*AT*A*d1=0, which appears different from the A-orthogonality condition. The discussion raises questions about the appropriate matrix to use in these conditions and explores the possibility of finding a matrix A that ensures the transformed vectors are orthogonal. Understanding these relationships is crucial for grasping the underlying principles of conjugate directions in optimization methods.
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Understanding of conjugate directions
I am reading a good paper of J. R. Shewchuk, titled "An introduction to the conjugate gradient method without the agonizing pain", however, I do not fully understand the idea of conjugate directions. As shown in Figure 22a, where the vectors d1 and d2 are not orthogonal. These vectors are transformed by a multiplication with the matrix A and after the transformation we have the corresponding vectors d'1 (=A*d1) and d'2 (=A*d2) as in Figure 22b. If the transformed vectors d'1 and d'2 are orthogonal now, the original vectors d1 and d2 satisfy the condition d2T*A*d1 = 0. The vectors d1 and d2 are then called A-orthogonal or conjuate. So far, so good!

However, I would expect a different condition. The transformed vectors d'1 and d'2 should satisfy the condition d'2T*d'1 = 0. Inserting there d'1 = A*d1 and d'2 = A*d2 would yield the condition d2T*AT*A*d1=0. This condition is different from the condition for the A-orthogonality. And I don't understand why ... ?
 
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Are you sure it should be the same matrix? Or just any matrix, like ##d_2^\tau B d_1## with ##B=A^\tau A##. Another way out is to search a matrix ##A## such that ##d_1'=d_1A## and ##d_2'=Ad_2## are orthogonal.
 
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