LU Decomposition Method in Matrix #LUDecomposition #linearalgebra
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LU Decomposition Method in Matrix #LUDecomposition #linearalgebra
In this video, we demonstrate how to perform LU Decomposition of a 3x3 matrix without pivoting. LU decomposition is a fundamental linear algebra technique used to solve systems of linear equations, invert matrices, and perform numerical analysis. We break the matrix into a lower triangular matrix (L) and an upper triangular matrix (U) by ...
1.4K views2 months ago
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Peter Math on Instagram: "Singular Value Decomposition (SVD) is one of the most powerful and versatile tools in linear algebra. It breaks down any matrix—even a non-square one—into three simpler matrices that reveal its fundamental structure: one that captures its directions (U), one that scales them (Σ), and one that rotates or reflects them (Vᵀ). At its core, SVD helps us understand how a matrix transforms space by identifying the principal components of that transformation. SVD is everywhere—
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Singular Value Decomposition (SVD) is one of the most powerful and versatile tools in linear algebra. It breaks down any matrix—even a non-square one—into three simpler matrices that reveal its fundamental structure: one that captures its directions (U), one that scales them (Σ), and one that rotates or reflects them (Vᵀ). At its core, SVD helps us understand how a matrix transforms space by identifying the principal components of that transformation. SVD is everywhere—from data science and mach
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Singular Value Decomposition (SVD) is one of the most powerful and versatile tools in linear algebra. It breaks down any matrix—even a non-square one—into three simpler matrices that reveal its fundamental structure: one that captures its directions (U), one that scales them (Σ), and one that rotates or reflects them (Vᵀ). At its core, SVD helps us understand how a matrix transforms space by identifying the principal components of that transformation. SVD is everywhere—from data science and mach
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Peter Math on Instagram: "Eigenvalue Decomposition is a foundational concept in linear algebra that reveals the internal structure of square matrices. It works by breaking a matrix into its eigenvalues (which scale vectors) and eigenvectors (which define directions that remain unchanged by the matrix transformation). In essence, it tells us how a transformation stretches or compresses space along specific directions—making it a key tool for understanding complex systems. This decomposition is de
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Singular Value Decomposition (SVD) is one of the most powerful and versatile tools in linear algebra. It breaks down any matrix—even a non-square one—into three simpler matrices that reveal its fundamental structure: one that captures its directions (U), one that scales them (Σ), and one that rotates or reflects them (Vᵀ). At its core, SVD helps us understand how a matrix transforms space by identifying the principal components of that transformation. SVD is everywhere—from data science and mach
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Peter Math on Instagram: "Eigenvalue Decomposition is a foundational concept in linear algebra that reveals the internal structure of square matrices. It works by breaking a matrix into its eigenvalues (which scale vectors) and eigenvectors (which define directions that remain unchanged by the matrix transformation). In essence, it tells us how a transformation stretches or compresses space along specific directions—making it a key tool for understanding complex systems. This decomposition is de
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Peter Math on Instagram: "Singular Value Decomposition (SVD) is one of the most powerful and versatile tools in linear algebra. It breaks down any matrix—even a non-square one—into three simpler matrices that reveal its fundamental structure: one that captures its directions (U), one that scales them (Σ), and one that rotates or reflects them (Vᵀ). At its core, SVD helps us understand how a matrix transforms space by identifying the principal components of that transformation. SVD is everywhere—
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