Understanding of conjugate directions

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In summary, the conversation discusses the concept of conjugate directions in relation to the conjugate gradient method. The transformed vectors d'1 and d'2 are expected to be orthogonal, but the condition for A-orthogonality is different from the condition for orthogonality of d'1 and d'2. The conversation suggests using a matrix B=A^T*A or finding a matrix A that will make d'1 and d'2 orthogonal.
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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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1. What are conjugate directions in mathematics?

Conjugate directions in mathematics refer to a pair of vectors that are perpendicular to each other and have the property that any linear combination of them is also perpendicular to each other. This concept is commonly used in optimization and linear algebra.

2. How are conjugate directions used in optimization?

In optimization, conjugate directions are used to efficiently find the minimum or maximum value of a function. By choosing conjugate directions as search directions, the optimization algorithm can avoid getting stuck in local minima or maxima and converge to the global optimum.

3. What is the significance of conjugate directions in linear algebra?

In linear algebra, conjugate directions are important in solving systems of linear equations. By choosing conjugate directions as search directions, the conjugate gradient method can efficiently solve large systems of equations, which would be computationally expensive using other methods.

4. How do you determine if two vectors are conjugate directions?

Two vectors are considered conjugate directions if their dot product is equal to zero, meaning they are perpendicular to each other. Additionally, any linear combination of the two vectors should also result in a dot product of zero.

5. Can conjugate directions be applied in other fields besides mathematics?

Yes, the concept of conjugate directions can also be applied in other fields such as physics and engineering. In physics, conjugate directions can be used to solve problems involving energy and force. In engineering, conjugate directions can be used in the design and optimization of structures and systems.

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