Rank 1 matrices are important in some computer algorithms and several theoretical contexts, including the singular value decomposition in Chapter 7. It can be shown that an m × n matrix A has rank 1 if and only if it is an outer product; that is, A = uv T for some u in ℝ m and v in ℝ n . Exercises 31-33 suggest why this property is true. 33. Let A be any 2 × 3 matrix such that rank A = 1, let u be the first column of A , and suppose u ≠ 0 . Explain why there is a vector v in ℝ 3 such that A = uv T . How could this construction be modified if the first column of A were zero?
Rank 1 matrices are important in some computer algorithms and several theoretical contexts, including the singular value decomposition in Chapter 7. It can be shown that an m × n matrix A has rank 1 if and only if it is an outer product; that is, A = uv T for some u in ℝ m and v in ℝ n . Exercises 31-33 suggest why this property is true. 33. Let A be any 2 × 3 matrix such that rank A = 1, let u be the first column of A , and suppose u ≠ 0 . Explain why there is a vector v in ℝ 3 such that A = uv T . How could this construction be modified if the first column of A were zero?
Solution Summary: The author explains the reason behind the existence of a vector v in R3 such that A=uvT
Rank 1 matrices are important in some computer algorithms and several theoretical contexts, including the singular value decomposition in Chapter 7. It can be shown that an m × n matrix A has rank 1 if and only if it is an outer product; that is, A = uvT for some u in ℝm and v in ℝn. Exercises 31-33 suggest why this property is true.
33. Let A be any 2 × 3 matrix such that rank A = 1, let u be the first column of A, and suppose u ≠ 0. Explain why there is a vectorv in ℝ3 such that A = uvT. How could this construction be modified if the first column of A were zero?
Quantities that have magnitude and direction but not position. Some examples of vectors are velocity, displacement, acceleration, and force. They are sometimes called Euclidean or spatial vectors.
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