The conjugate gradient method is an algorithm used to solve systems of linear equations, particularly those arising from the discretization of partial differential equations. It is especially useful in quantum mechanics and variational methods for minimizing energy functions due to its efficiency in handling large, sparse matrices, making it a valuable tool in computational physics.
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