#L01 · Linear Algebra & Manifolds · Lv.1View in 3D Mind-Map
Vector Operations, L1/L2 Norms & Normalization
Hermann Grassmann & J. W. Gibbs · 1844 CE
Measures vector magnitude via L1/L2 norms and scales vectors to unit length v̂ = v/‖v‖—essential for 3D game movement direction and L1/L2 regularization in machine learning.
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유클리드 노름 ||v||₂
5.0000
단위벡터 v̂ (x, y, z)
(0.60, 0.80, 0.00)
맨해튼 노름 ||v||₁
7.00
Prerequisite Path (6 STEP)
Full Path AnalyzerClick steps in order to follow the 3D neon path from basics to target.
STEP 1#A01Linear Equation & Balance Axiom
STEP 2#A02Polynomial Expansion & Factoring
STEP 3#A03Laws of Exponents & Radicals
STEP 4#G01Pythagorean Theorem
STEP 5#G022D/3D Euclidean Distance Formula
STEP 6#L01Vector Operations, L1/L2 Norms & Normalization
🚀 Next Unlocks (Derived Formulas) (3)
Geometric & Intuitive Insight
게임에서 위쪽 키와 오른쪽 키를 동시에 누르면 이동 벡터가 (1, 1)이 되어 속도가 √2 ≈ 1.414배 빨라지는 버그가 생깁니다. 이를 L2 노름 ‖v‖로 나누어 정규화(v̂)하면 어느 방향이든 속도가 1로 일정해집니다!
Step-by-Step Derivation
- Step 1.1단계: n차원 벡터 v = (v₁, v₂, …, v_n) 의 원점 거리는 피타고라스 정리에 의해 ‖v‖₂ = √(v₁² + v₂² + … + v_n²)
- Step 2.2단계: 각 성분을 ‖v‖₂ 로 나눈 벡터 v̂ = v / ‖v‖₂ 의 길이를 재면 ‖v̂‖₂ = ‖v‖₂ / ‖v‖₂ = 1 이 됩니다.
- Step 3.3단계: 절댓값 합 ‖v‖₁ = ∑|v_i| 는 격자 도로를 따라 이동하는 맨해튼 거리(L1 Norm)를 나타냅니다.
AI & Coding Real-World Use
Deep learning L1/L2 Weight Decay regularization, LayerNorm, and 3D game movement vector normalization
#L1/L2 Regularization#3D Vector Normalize#Embedding Norm
const norm2 = Math.hypot(vx, vy, vz); const unit = [vx/norm2, vy/norm2, vz/norm2];Linear Algebra & Manifolds — Related Formulas in This Cluster
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