The conjugate gradient method is an efficient algorithm for solving large systems of linear equations, particularly those that are symmetric and positive definite. This method is iterative, which means it approaches the solution gradually, making it especially suitable for problems where the matrix involved is sparse. By combining gradient descent techniques with the concept of conjugate directions, this method can achieve convergence faster than traditional methods, making it a favorite in numerical analysis.
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