Vector Projection Calculator

Compute the vector projection (projₓᵥ) and scalar projection (compₓᵥ) of vector v onto vector u. Visualize orthogonal decomposition, dot product, and angle.

Enter real components for both vectors. Zero vector detection for u is applied. Vectors originate from origin (0,0) in graph.
? v=(4,3), u=(2,1)
➡️ Horizontal proj: v=(5,2), u=(1,0)
⬆️ Vertical proj: v=(3,4), u=(0,1)
✖️ Perpendicular: v=(1,2), u=(2,-1)
? Same direction: v=(3,1), u=(6,2)
Privacy first: All computations run locally in your browser. No data is uploaded or stored.

The Mathematics of Vector Projection: Theory & Intuition

The vector projection of v onto u (non-zero) is the orthogonal projection of v onto the line spanned by u. It represents the component of v that lies in the direction of u. Denoted proju v = ((v·u)/(u·u)) u. The scalar projection (or component) is compu v = (v·u)/|u|, which gives the signed length of the projection. This concept is fundamental in linear algebra, physics (force decomposition), and data science (dimension reduction).

proju v = \frac{v·u}{u·u} u    ,    compu v = \frac{v·u}{|u|}

where dot product v·u = vₓuₓ + vᵧuᵧ, and |u| = √(uₓ² + uᵧ²).

Why Use an Interactive Projection Tool?

  • Physics & Engineering: Resolve forces into tangential and normal components, inclined plane problems.
  • Computer Graphics: Shadow calculations, vector reflections, and collision response.
  • Machine Learning: Projection onto principal components (PCA) and feature mapping.
  • Educational Clarity: Visualize orthogonal decomposition instantly.

Step-by-Step Derivation

Given vectors v and u, we want a scalar λ such that λu is the projection of v onto u, with residual vector (v - λu) perpendicular to u. Orthogonality condition: (v - λu)·u = 0 ⇒ v·u - λ (u·u) = 0 ⇒ λ = (v·u)/(u·u). Therefore proj = λu, and the perpendicular component = v - λu. The scalar projection length equals |proj| = |λ|·|u| = |v·u|/|u| if considering absolute length; but signed value preserves direction sign. This decomposition is unique and widely used in Gram–Schmidt process.

Applications across Disciplines

Case Study: Robotics Arm Force Analysis
A robotic arm applies force vector v = (30, 20) N at end-effector. The movement direction constraint is along u = (2, 1) (unit not necessary). Effective force contributing to motion is the projection proju v. The tool computes proj = (16,8) N, while perpendicular force (14,12) N is wasted/stresses joints. Engineers optimize by aligning v and u. Our calculator reveals real-time insights.

Computational Methodology & Accuracy

All calculations are performed with double-precision floating-point arithmetic using standard vector formulas. The interactive graph uses canvas coordinate mapping from world coordinates to screen pixels with dynamic padding and scaling. Vector arrows are drawn with triangle heads. Orthogonal decomposition is shown via a dashed segment connecting the tip of projection to the tip of vector v, highlighting the residual component. The tool treats u ≈ 0 as an error; the user is notified.

Vector v Vector u Vector Projection (proj) Scalar Projection Angle (°)
(4, 3) (2, 1) (4.4, 2.2) 4.919 10.30°
(5, 2) (1, 0) (5, 0) 5.00 21.80°
(3, 4) (0, 1) (0, 4) 4.00 53.13°
(1, 2) (2, -1) (0, 0) 0.00 90.0°

Frequently Asked Questions (FAQ)

Projection onto a zero vector is undefined. The calculator shows a warning and disables the projection result until you enter a non-zero u.

No. The scalar projection (signed) is (v·u)/|u|, which can be negative if the angle exceeds 90°, indicating projection opposite to direction of u. The absolute value gives length.

v_perp = v - proju v. It is orthogonal to u and completes the vector decomposition.

Conceptually yes, the same formula applies. For interactive graph we focus on 2D for clarity. A 3D version is coming soon.
This tool implements rigorous vector algebra verified against multiple sources (Strang, Linear Algebra; Anton & Rorres). Updated June 2026 to include dynamic orthographic decomposition. Backed by GetZenQuery tech team.
References: Strang, G. "Introduction to Linear Algebra"; Wolfram MathWorld: Projection; MIT OpenCourseWare 18.06.