ORTHOGONALITY AND LEAST SQUARES
Orthogonal Vectors Norm Unit Vector Orthonormal Vectors Orthogonal Complement Orthogonal Projection Cauchy-Schwarz Inequality Angle Between Two Vectors Gram-Schmidt Process QR Factorialization Transpose Symmetric and Skew-symmetric Matrices Orthogonal Matrices Least Squares Solutions Matrix of an Orthogonal Projection	Cauchy-Schwarz Inequality How to Examples Exercise How to: The Cauchy-Schwarz inequality comes in handy in Linear Algebra. It says that for any two vectors x and y, the absolute value of their dot product is less than or equal to the product of their norm. In MATLAB it would look something like this:      abs(dot(x, y)) <= norm(x)*norm(y)      Examples: Exercise: