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Matrix decomposition is a mathematical process that breaks down a matrix into simpler, constituent components, making complex matrix operations more manageable and computationally efficient. It is fundamental in various applications such as solving linear equations, eigenvalue problems, and in machine learning algorithms for dimensionality reduction.
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