Matrix Addition : C = A + B C = A + B
Matrix Multiplication : C = A B C = A B
Determinant of a 2x2 Matrix : det ( A ) = a d − b c det ( A ) = a d − b c
Determinant of a 3x3 Matrix : det ( A ) = a ( e i − f h ) − b ( d i − f g ) + c ( d h − e g ) det ( A ) = a ( e i − f h ) − b ( d i − f g ) + c ( d h − e g )
Inverse of a 2x2 Matrix : A − 1 = 1 det ( A ) ( d − b − c a ) A − 1 = d e t ( A ) 1 ( d − c − b a )
Eigenvalues : det ( A − λ I ) = 0 det ( A − λ I ) = 0
Eigenvectors : ( A − λ I ) v = 0 ( A − λ I ) v = 0
Dot Product : a ⋅ b = a 1 b 1 + a 2 b 2 + ⋯ + a n b n a ⋅ b = a 1 b 1 + a 2 b 2 + ⋯ + a n b n
Cross Product : a × b = ∣ i j k a 1 a 2 a 3 b 1 b 2 b 3 ∣ a × b = i a 1 b 1 j a 2 b 2 k a 3 b 3
Vector Magnitude : ∥ a ∥ = a 1 2 + a 2 2 + ⋯ + a n 2 ∥ a ∥ = a 1 2 + a 2 2 + ⋯ + a n 2
Vector Projection : proj b a = a ⋅ b ∥ b ∥ 2 b proj b a = ∥ b ∥ 2 a ⋅ b b
Matrix Transpose : A T A T
Trace of a Matrix : tr ( A ) = ∑ a i i tr ( A ) = ∑ a ii
Rank of a Matrix : Number of linearly independent rows/columns.
Null Space : Null ( A ) = { x ∣ A x = 0 } Null ( A ) = { x ∣ A x = 0 }
Column Space : Col ( A ) = Span ( columns of A ) Col ( A ) = Span ( columns of A )
Row Space : Row ( A ) = Span ( rows of A ) Row ( A ) = Span ( rows of A )
Orthogonal Vectors : a ⋅ b = 0 a ⋅ b = 0
Orthogonal Matrix : A T A = I A T A = I
Singular Value Decomposition : A = U Σ V T A = U Σ V T
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