La factorización de Cholesky, también conocida como Cholesky Descomposición, is a mathematical technique used in álgebra lineal to factorize a positive-definite matrix into a product of a lower triangular matrix and its transpose. This factorization is particularly useful in various applications, including solving systems of linear equations, optimization problems, and in métodos estadísticos.
Específicamente, si A is a symmetric, positive-definite matrix, the Cholesky Factorization states that there exists a unique lower triangular matrix L tal que:
A = L * L^TT
where LT is the transpose of L. The process of obtaining L involves a series of calculations that eliminate variables step by step, ensuring that the resulting matrix is triangular.
Cholesky Factorization is computationally efficient, requiring approximately half the number of operations needed for other factorization methods such as LU decomposition. Its advantages make it favorable in algorithms requiring matrix inversion or solving linear systems, especially in the context of machine learning and numerical simulations. Additionally, it plays a critical role in Monte Carlo methods and algoritmos de optimización, where the efficiency of matrix computations is crucial.
En resumen, la factorización de Cholesky es una herramienta poderosa en álgebra lineal numérica, providing a means to simplify complex calculations involving positive-definite matrices.