eigenvalue equation
finds nonzero vectors whose direction does not change in linear transformations.
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singular value decomposition
represents an arbitrary matrix with orthogonal directions and nonnegative stretch.
View formula explanationlinear least squares
Find the coefficient that minimizes the sum of the squared errors of the predicted value and the observed value.
View formula explanationAdjoint expression of matrix inverse
Use algebraic cofactors and determinants to express the inverse of a square matrix.
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Reference: MIT OpenCourseWare · Linear Algebra ↗
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