The conjugate gradient method is an algorithm for solving systems of linear equations with a symmetric positive-definite matrix. It is particularly efficient for large, sparse systems because it avoids the direct computation of the matrix inverse, instead iteratively refining an approximate solution. The method leverages properties of orthogonality and minimizes quadratic functions, making it applicable in various fields, including optimization and numerical analysis.
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