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
Source References
This lesson synthesizes and paraphrases concepts from the sources below. No copyrighted text, problem sets, or solutions are reproduced. Return to the originals for full depth.
- Calculus, Vol. II (Tom Apostol, 2nd ed., 1969) foundational - Ch. 8 - Vol. II, Ch. 8: functions of several variables, level sets, limits and continuity.
- Calculus, Vol. I (Tom Apostol, 2nd ed., 1967) foundational - Ch. 12 - Vol. I, Ch. 12: vectors in R^n, dot product, norm, orthogonality.