The conjugate gradient method is an iterative algorithm used to solve large systems of linear equations, especially those that are symmetric and positive-definite. This technique minimizes a quadratic function, allowing for efficient convergence to the solution by using conjugate directions in the search space. It's particularly valuable in computational chemistry for optimizing molecular structures and minimizing energy functions derived from empirical force fields.
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