Vectors, Multivariable Functions, and Level Sets
From points in R^n to scalar fields: how to picture a function of several variables through its graph, level sets, and sections.
Leads to: Partial derivatives (3.2) differentiate the scalar fields introduced here.
Learning Objectives
Click a status chip to cycle: Not started → In progress → Studied → Practiced → Needs review → Mastered.
- Define R^n as a vector space and compute norms, dot products, and angles between vectors.
- Explain the difference between a scalar field, a vector field, and a curve in R^n.
- Construct and interpret level sets and vertical sections of a function of two variables.
- Compute limits and test continuity of a function of several variables along paths.
Key Vocabulary
- Euclidean space
- The set \(\R^n\) of ordered n-tuples with vector addition, scalar multiplication, and the dot product.
- Scalar field
- A function \(f:\R^n\to\R\) assigning a number to each point, e.g. temperature or a portfolio’s loss.
- Vector field
- A function \(F:\R^n\to\R^m\) assigning a vector to each point, e.g. a gradient or a force.
- Level set
- The set \(\{x: f(x)=c\}\); for \(n=2\) a level curve, for \(n=3\) a level surface.
- Norm
- The length \(\lVert x\rVert=\sqrt{x\cdot x}\); measures distance and induces the metric on \(\R^n\).
- Path limit
- The value approached by \(f\) as \(x\) tends to a point along a chosen curve; all paths must agree for a limit to exist.
Intuition & Motivation
The dot product is the one tool that turns geometry into algebra: it measures length, angle, and projection all at once. Almost every idea in the rest of this phase - gradients, orthogonality of constraints, least squares - is the dot product wearing a different hat.
Vectors and the dot product
A point \(x=(x_1,\dots,x_n)\) in \(\R^n\) is simultaneously a location and a displacement. The two operations that matter are the norm and the dot product:
The last identity defines the angle \(\theta\) between vectors and gives the Cauchy–Schwarz bound \(|x\cdot y|\le \lVert x\rVert\,\lVert y\rVert\). Two vectors are orthogonal exactly when \(x\cdot y=0\).
Graphs, level sets, and sections
For \(f:\R^2\to\R\) the graph is the surface \(z=f(x,y)\) in \(\R^3\). Two lower-dimensional shadows make it readable:
- Level curve \(f(x,y)=c\): the set of inputs mapping to a fixed height \(c\).
- Vertical section: freeze one variable (say \(y=y_0\)) and read the single-variable curve \(x\mapsto f(x,y_0)\).
Limits and continuity in several variables
We say \(\lim_{x\to a}f(x)=L\) if \(f(x)\) can be forced within any tolerance of \(L\) by taking \(x\) close enough to \(a\) - along every path. Unlike one dimension there are infinitely many approach directions, so a limit fails if two paths disagree.
- Concluding a two-variable limit exists after checking only straight-line paths - a parabolic path can still break it (classic: \(f=xy^2/(x^2+y^4)\)).
- Confusing the graph (a surface in \(\R^3\)) with a level set (a curve in \(\R^2\)).
- Writing \(\lVert x\rVert^2=x^2+y^2+\dots\) but then forgetting the square root when a distance is needed.
- Assuming orthogonal means ‘perpendicular on the page’ only; algebraically it is exactly \(x\cdot y=0\) in any dimension.
- Read any risk report as a contour map: nested ellipses are a quadratic loss, and their axes are the principal risk directions (Phase 4 makes this precise).
- Polar substitution \(x=r\cos\theta,\ y=r\sin\theta\) turns many 2-D limits into a single-variable limit in \(r\).
- The dot product is your Swiss-army knife: length, angle, projection, and later the gradient’s directional derivative all reduce to it.
Practice this in the Euler Lab
Computational problems that exercise exactly this technique. Each opens in the Euler Lab with a Python workbench, a progressive hint ladder, and answer checking. Tier A/B run at full scale in the browser.
Warm-up:
#6 Sum Square Difference (1%, tier A) #9 Special Pythagorean Triplet (1%, tier A) #28 Number Spiral Diagonals (2%, tier A) #39 Integer Right Triangles (2%, tier A)
Applied:
#504 Square on the Inside (16%, tier B) #265 Binary Circles (17%, tier B) #679 Freefarea (17%, tier B)
Challenge:
#147 Rectangles in Cross-hatched Grids (41%, tier C) #184 Triangles Containing the Origin (41%, tier C)
273 Project Euler problems in total are mapped to this lesson. Open the Euler Lab to filter them all.
Knowledge Check
Practical Exercise
Let \(f(x,y)=\dfrac{xy}{x^2+y^2}\) for \((x,y)\neq(0,0)\). (a) Compute the limit as \((x,y)\to(0,0)\) along the line \(y=mx\). (b) Does \(\lim_{(x,y)\to 0}f\) exist? Justify.
(a) Substitute \(y=mx\):
which is independent of \(x\), so the limit along \(y=mx\) is \(m/(1+m^2)\).
(b) The path limit depends on the slope \(m\): it is \(0\) along \(y=0\) (m=0) but \(\tfrac12\) along \(y=x\) (m=1). Two paths disagree, so the two-variable limit does not exist, even though every straight-line restriction is finite.
Lesson Summary
Formula Sheet Additions
Retrieval Practice
Close the lesson and answer from memory before checking. This is deliberate, effortful recall - the single highest-yield study action.
A: The set of inputs mapping to a fixed value c; here a family of ellipses with semi-axes sqrt(c) and (1/2)sqrt(c).
A: Exhibit two approach paths (e.g. y=0 and y=x) along which f tends to different values.
Completion Checklist
- I can explain the core ideas in my own words
- I worked the derivations/examples by hand
- I completed the interactive workbench(es)
- I passed the knowledge check