Eigenvalue Calculation

Eigenvalue calculation is a fundamental concept in linear algebra and is widely used in data science and artificial intelligence. It involves finding the scalar values that satisfy the equation Ax = λx, where A is a square matrix, λ is a scalar value known as the eigenvalue, and x is a non-zero vector known as the eigenvector. The eigenvectors and eigenvalues of a matrix are important in many applications, including principal component analysis, image compression, and machine learning algorithms such as PCA and LDA. The eigenvalues of a matrix can provide information about the matrix's properties, such as its determinant, trace, and rank.

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Eigenvalue Calculation

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