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An introduction to vectors in linear algebra, including their definition, addition, scalar multiplication, and norm. It also covers the concept of orthogonal vectors and the dot product.
Typology: Summaries
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𝑛
𝑛
A
벡터의 크기와 방향 𝑅 2 벡터 𝑥Ԧ = (𝑥 1 , 𝑥 2 ) 의 크기와 방향 ∥ 𝑥Ԧ ∥ = 𝑥 1 2
2
𝑥Ԧ ∥ 𝑥Ԧ∥
내적 (inner product) 정의 𝑥 Ԧ ∙ 𝑦Ԧ = 𝑥 1 , 𝑥 2 , ⋯ , 𝑥𝑛 ∙ 𝑦 1 , 𝑦 2 , ⋯ , 𝑦𝑛 = 𝑥 1 ∙ 𝑦 1 + 𝑥 2 ∙ 𝑦 2 + ⋯ + 𝑥𝑛 ∙ 𝑦𝑛 성질 크기를 내적으로 표현하기 ∥ 𝑥Ԧ ∥^2 = 𝑥 12 + 𝑥 22 + ⋯ + 𝑥𝑛^2 = 𝑥Ԧ ∙ 𝑥Ԧ ∥ 𝑥Ԧ ∥ = 𝑥Ԧ ∙ 𝑥Ԧ
성질 𝑥 Ԧ ∙ 𝑥Ԧ ≥ 0 𝑥 Ԧ ∙ 𝑥Ԧ = 0 ⟺ 𝑥Ԧ = 0 𝑥 Ԧ ∙ 𝑦Ԧ = 𝑦Ԧ ∙ 𝑥Ԧ 𝑥 Ԧ + 𝑦Ԧ ∙ 𝑧Ԧ = 𝑥Ԧ ∙ 𝑧Ԧ + 𝑦Ԧ ∙ 𝑧Ԧ (𝑘 𝑥Ԧ) ∙ 𝑦Ԧ = 𝑘( 𝑥Ԧ ∙ 𝑦Ԧ)
단위벡터 기본 단위 벡터 𝑒 Ԧ𝑖 = ( 0 , 0 , ⋯ , 1 , 0 , ⋯ , 0 ) ∥ 𝑒Ԧ𝑖 ∥= 1 𝑥 Ԧ = 𝑥 1 , 𝑥 2 , ⋯ , 𝑥𝑛 = 𝑥 1 𝑒Ԧ 1 + 𝑥 2 𝑒Ԧ 2 + ⋯ + 𝑥𝑛 𝑒Ԧ𝑛 단위벡터 ∥ 𝑥Ԧ ∥ = 1 인 벡터 𝑥Ԧ 를 단위벡터 (방향벡터)라고 부른다.
𝒙 위로 𝒚 의 정사영 벡터
3
1 42
1 42
1 6
1 6