Week 12: Full Course Synthesis & Exam Revision Map

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Comprehensive course-wide compression layer: algebra, calculus, discrete math, formula cheat sheets, and high-frequency traps. # Mathematics I — Week 12: Full Course Synthesis & Exam Revision Map The IITM syllabus lists **Week 12 as revision**, so there is no new topic to introduce here.

Mathematics I — Week 12: Full Course Synthesis & Exam Revision Map

The IITM syllabus lists Week 12 as revision, so there is no new topic to introduce here.
This is the useful version of Week 12: a full-course compression layer for everything from sets to graph algorithms.

0. Entire course in one map

text
W1  Sets → Relations → Functions
        ↓
W2  Coordinates → Straight Lines
        ↓
W3  Quadratics
        ↓
W4  General Polynomials
        ↓
W5  Functions → Composition → Inverses → Exponentials
        ↓
W6  Logarithms
        ↓
W7  Sequences → Limits
        ↓
W8  Derivatives
        ↓
W9  Integrals
        ↓
W10 Graph Theory → BFS/DFS → DAGs
        ↓
W11 Weighted Graphs → Shortest/Longest Paths
There are really three large mathematical blocks:
  1. Algebra + functions: Weeks 1–6
  2. Calculus: Weeks 7–9
  3. Discrete maths + graph algorithms: Weeks 10–11

Part I — Weeks 1–6

Algebra, Functions & Their Graphs


Week 1 — Sets, Relations & Functions

Core set notation

Membership:
x∈Ax\in A
Subset:
A⊆BA\subseteq B
Empty set:
∅\varnothing
Cardinality:
∣A∣|A|
Power set:
P(A)\mathcal P(A)
If:
∣A∣=n,|A|=n,
then:
∣P(A)∣=2n\boxed{ |\mathcal P(A)|=2^n }

Set operations

Union:
A∪BA\cup B
Intersection:
A∩BA\cap B
Difference:
A−BA-B
Complement:
AcA^c
De Morgan:
(A∪B)c=Ac∩Bc\boxed{ (A\cup B)^c=A^c\cap B^c } (A∩B)c=Ac∪Bc\boxed{ (A\cap B)^c=A^c\cup B^c }

Cartesian product

A×B={(a,b):a∈A,b∈B}\boxed{ A\times B = \{(a,b):a\in A,b\in B\} }
If:
∣A∣=m,∣B∣=n,|A|=m,\qquad|B|=n,
then:
∣A×B∣=mn\boxed{ |A\times B|=mn }

Relation properties — must know cold

Reflexive

aRa\boxed{ aRa }
for every aa.

Symmetric

aRb⇒bRa\boxed{ aRb\Rightarrow bRa }

Antisymmetric

aRb∧bRa⇒a=b\boxed{ aRb\land bRa\Rightarrow a=b }

Transitive

aRb∧bRc⇒aRc\boxed{ aRb\land bRc\Rightarrow aRc }

Equivalence relation

reflexive + symmetric + transitive\boxed{ \text{reflexive + symmetric + transitive} }

Partial order

reflexive + antisymmetric + transitive\boxed{ \text{reflexive + antisymmetric + transitive} }

Highest-value trap

Antisymmetric does not mean "not symmetric."
A relation can be both.

Functions

For:
f:A→B,f:A\to B,
  • AA = domain
  • BB = codomain
  • actual outputs = range
Always:
Range⊆Codomain\boxed{ \text{Range}\subseteq\text{Codomain} }

Injective / one-one

f(a)=f(b)⇒a=bf(a)=f(b)\Rightarrow a=b

Surjective / onto

Range=Codomain\text{Range}=\text{Codomain}

Bijective

injective + surjective\boxed{ \text{injective + surjective} }
Bijective functions admit full inverses.

Week 2 — Coordinate Geometry & Straight Lines

Distance

d=(x2−x1)2+(y2−y1)2\boxed{ d= \sqrt{(x_2-x_1)^2+(y_2-y_1)^2} }

Midpoint

(x1+x22,y1+y22)\boxed{ \left( \frac{x_1+x_2}{2}, \frac{y_1+y_2}{2} \right) }

Slope

m=y2−y1x2−x1\boxed{ m= \frac{y_2-y_1}{x_2-x_1} }

Types of slopes

Positive:
m>0m>0
Negative:
m<0m<0
Horizontal:
m=0m=0
Vertical:
m undefinedm\text{ undefined}

Line equations

Slope-intercept

y=mx+c\boxed{ y=mx+c }

Point-slope

y−y1=m(x−x1)\boxed{ y-y_1=m(x-x_1) }

General form

Ax+By+C=0\boxed{ Ax+By+C=0 }
Slope from general form:
m=−AB\boxed{ m=-\frac AB }

Parallel lines

m1=m2\boxed{ m_1=m_2 }

Perpendicular lines

m1m2=−1\boxed{ m_1m_2=-1 }
except horizontal/vertical edge case.

Collinearity

Three points are collinear if their slopes agree appropriately, or equivalently:
x1(y2−y3)+x2(y3−y1)+x3(y1−y2)=0\boxed{ x_1(y_2-y_3)+ x_2(y_3-y_1)+ x_3(y_1-y_2)=0 }

Week 3 — Quadratics

General form:
f(x)=ax2+bx+c\boxed{ f(x)=ax^2+bx+c }
with:
a≠0.a\neq0.

Direction

If:
a>0,a>0,
opens upward → minimum.
If:
a<0,a<0,
opens downward → maximum.

Axis of symmetry

x=−b2a\boxed{ x=-\frac b{2a} }

Vertex

(−b2a,f(−b2a))\boxed{ \left( -\frac b{2a}, f\left(-\frac b{2a}\right) \right) }

Quadratic formula

x=−b±b2−4ac2a\boxed{ x= \frac{-b\pm\sqrt{b^2-4ac}}{2a} }
Discriminant:
Δ=b2−4ac\boxed{ \Delta=b^2-4ac }

Nature of roots

If:
Δ>0\Delta>0
→ two distinct real roots.
If:
Δ=0\Delta=0
→ repeated real root.
If:
Δ<0\Delta<0
→ no real roots.

Root relationships

If roots are:
α,β,\alpha,\beta,
then:
α+β=−ba\boxed{ \alpha+\beta=-\frac ba } αβ=ca\boxed{ \alpha\beta=\frac ca }

Three useful quadratic forms

Standard:
ax2+bx+cax^2+bx+c
Factored:
a(x−r1)(x−r2)a(x-r_1)(x-r_2)
Vertex:
a(x−h)2+ka(x-h)^2+k
Each exposes different information.

Week 4 — Polynomials

General polynomial:
P(x)=anxn+⋯+a0.P(x)=a_nx^n+\cdots+a_0.
Degree:
n.n.
Leading coefficient:
an.a_n.

Division algorithm

P(x)=D(x)Q(x)+R(x)\boxed{ P(x)=D(x)Q(x)+R(x) }
with:
deg⁡R<deg⁡D.\deg R<\deg D.

Remainder theorem

When dividing by:
x−a,x-a,
remainder is:
P(a)\boxed{ P(a) }

Factor theorem

x−a factor  ⟺  P(a)=0\boxed{ x-a\text{ factor} \iff P(a)=0 }
Thus:
root  ⟺  factor  ⟺  x-intercept\boxed{ \text{root} \iff \text{factor} \iff x\text{-intercept} }

Multiplicity

For:
(x−r)m,(x-r)^m,
rr is a root of multiplicity mm.
Odd multiplicity:
crosses the axis\boxed{\text{crosses the axis}}
Even multiplicity:
touches/bounces\boxed{\text{touches/bounces}}

End behavior

Determined by:
  • parity of degree
  • sign of leading coefficient
DegreeLeading coefficientLeftRight
even+upup
even-downdown
odd+downup
odd-updown
Shortcut:
even degree → same-direction ends
odd degree → opposite-direction ends

Week 5 — Composition, Inverses & Exponentials

Vertical line test

Tests whether graph is a function.

Horizontal line test

Tests whether function is one-one.

Composition

(f∘g)(x)=f(g(x))\boxed{ (f\circ g)(x)=f(g(x)) }
Order matters:
f∘g≠g∘ff\circ g \neq g\circ f
in general.

Inverse

If:
f(a)=b,f(a)=b,
then:
f−1(b)=a.f^{-1}(b)=a.
Core identities:
f−1(f(x))=x\boxed{ f^{-1}(f(x))=x } f(f−1(x))=x\boxed{ f(f^{-1}(x))=x }

Inverse trap

f−1(x)≠1f(x)\boxed{ f^{-1}(x)\neq\frac1{f(x)} }

Finding inverse

  1. Write: y=f(x)y=f(x)
  2. Swap x,yx,y.
  3. Solve for yy.
  4. Check domain/range.

Exponential function

f(x)=ax\boxed{ f(x)=a^x }
where:
a>0,a≠1.a>0,\qquad a\neq1.
Domain:
R\mathbb R
Range:
(0,∞)(0,\infty)
If:
a>1a>1
→ growth.
If:
0<a<10<a<1
→ decay.

Week 6 — Logarithms

Fundamental equivalence:
ax=y  ⟺  log⁡ay=x\boxed{ a^x=y \iff \log_a y=x }
Restrictions:
a>0,a≠1,y>0.a>0,\quad a\neq1,\quad y>0.

Log laws

Product:
log⁡a(xy)=log⁡ax+log⁡ay\boxed{ \log_a(xy) = \log_a x+\log_a y }
Quotient:
log⁡a(xy)=log⁡ax−log⁡ay\boxed{ \log_a\left(\frac xy\right) = \log_a x-\log_a y }
Power:
log⁡a(xr)=rlog⁡ax\boxed{ \log_a(x^r)=r\log_a x }
Change of base:
log⁡ax=log⁡bxlog⁡ba\boxed{ \log_a x = \frac{\log_bx}{\log_ba} }

Inverse identities

log⁡a(ax)=x\boxed{ \log_a(a^x)=x } alog⁡ax=x\boxed{ a^{\log_a x}=x }

Common log trap

Wrong:
log⁡(x+y)=log⁡x+log⁡y.\log(x+y)=\log x+\log y.
There is no such law.

Part II — Weeks 7–9

Calculus


Week 7 — Sequences & Limits

Sequence

A sequence is essentially:
a:N→R.a:\mathbb N\to\mathbb R.

Sequence limit

lim⁡n→∞an=L\boxed{ \lim_{n\to\infty}a_n=L }
means terms approach LL as nn becomes large.

Basic limit

lim⁡n→∞1np=0\boxed{ \lim_{n\to\infty}\frac1{n^p}=0 }
for:
p>0.p>0.

Rational degree rule

For:
P(n)Q(n),\frac{P(n)}{Q(n)},
if numerator degree < denominator degree:
0\boxed{0}
If equal:
leading coefficients\boxed{ \frac{\text{leading coefficients}} {} }
If numerator degree > denominator degree:
usually unbounded or requires sign analysis.

Function limits

lim⁡x→af(x)=L\boxed{ \lim_{x\to a}f(x)=L }
means nearby values of f(x)f(x) approach LL.
The exact value:
f(a)f(a)
may differ.

Left and right limits

lim⁡x→a−f(x)\lim_{x\to a^-}f(x)
and:
lim⁡x→a+f(x).\lim_{x\to a^+}f(x).
Two-sided limit exists iff:
lim⁡x→a−f(x)=lim⁡x→a+f(x)\boxed{ \lim_{x\to a^-}f(x) = \lim_{x\to a^+}f(x) }

Continuity

At x=ax=a:
lim⁡x→af(x)=f(a)\boxed{ \lim_{x\to a}f(x)=f(a) }
with both sides existing.

0/00/0 trap

00\frac00
is not the answer.
It is an indeterminate form.
Typical remedies:
  • factor
  • cancel
  • rationalize

Tangent preview

Secant slope:
f(x+h)−f(x)h.\frac{f(x+h)-f(x)}h.
Tangent slope:
lim⁡h→0f(x+h)−f(x)h\boxed{ \lim_{h\to0} \frac{f(x+h)-f(x)}h }

Week 8 — Derivatives

Derivative definition:
f′(x)=lim⁡h→0f(x+h)−f(x)h\boxed{ f'(x) = \lim_{h\to0} \frac{f(x+h)-f(x)}h }
Interpretations:
  • tangent slope
  • instantaneous rate of change
  • local behavior

Differentiability relationship

differentiable⇒continuous\boxed{ \text{differentiable} \Rightarrow \text{continuous} }
But:
continuous⇏differentiable.\text{continuous} \not\Rightarrow \text{differentiable}.
Example:
∣x∣|x|
at 00.

Derivative rules

Constant:
ddxc=0\boxed{ \frac d{dx}c=0 }
Power:
ddxxn=nxn−1\boxed{ \frac d{dx}x^n=nx^{n-1} }
Product:
(fg)′=f′g+fg′\boxed{ (fg)'=f'g+fg' }
Quotient:
(fg)′=f′g−fg′g2\boxed{ \left(\frac fg\right)' = \frac{f'g-fg'}{g^2} }
Chain:
(f∘g)′(x)=f′(g(x))g′(x)\boxed{ (f\circ g)'(x) = f'(g(x))g'(x) }

Common derivatives

ddxex=ex\boxed{ \frac d{dx}e^x=e^x } ddxax=axln⁡a\boxed{ \frac d{dx}a^x=a^x\ln a } ddxln⁡x=1x\boxed{ \frac d{dx}\ln x=\frac1x }

Tangent line

At:
x=a,x=a, y−f(a)=f′(a)(x−a)\boxed{ y-f(a)=f'(a)(x-a) }

Increasing/decreasing

If:
f′(x)>0f'(x)>0
→ increasing.
If:
f′(x)<0f'(x)<0
→ decreasing.

Critical points

Candidates occur when:
f′(x)=0\boxed{ f'(x)=0 }
or derivative is undefined while function exists.

First derivative test

+→−+\to-
→ local maximum.
−→+-\to+
→ local minimum.

L’Hôpital

For suitable:
00\frac00
or:
∞∞,\frac{\infty}{\infty}, lim⁡fg=lim⁡f′g′\boxed{ \lim\frac fg = \lim\frac{f'}{g'} }
when conditions are satisfied.
Do not confuse this with the quotient rule.

Week 9 — Integration

Indefinite integral:
∫f(x) dx=F(x)+C\boxed{ \int f(x)\,dx=F(x)+C }
where:
F′(x)=f(x).F'(x)=f(x).

Power rule

For:
n≠−1,n\neq-1, ∫xn dx=xn+1n+1+C\boxed{ \int x^n\,dx = \frac{x^{n+1}}{n+1}+C }
Special case:
∫1x dx=ln⁡∣x∣+C\boxed{ \int\frac1x\,dx = \ln|x|+C }

Exponential integral

∫ex dx=ex+C\boxed{ \int e^x\,dx=e^x+C }

Fundamental Theorem of Calculus

If:
F′=f,F'=f,
then:
∫abf(x) dx=F(b)−F(a)\boxed{ \int_a^b f(x)\,dx = F(b)-F(a) }

Signed area

Above xx-axis:
++
Below xx-axis:
−-
Therefore definite integral is net signed area.
For total geometric area, split intervals or use:
∣f(x)∣.|f(x)|.

Area between curves

If:
f(x)≥g(x),f(x)\geq g(x),
then:
A=∫ab[f(x)−g(x)] dx\boxed{ A= \int_a^b [f(x)-g(x)]\,dx }
Think:
top minus bottom.

Substitution

Reverse chain rule:
∫F′(g(x))g′(x) dx=F(g(x))+C\boxed{ \int F'(g(x))g'(x)\,dx = F(g(x))+C }

Calculus in one picture

text
LIMIT
"What happens nearby?"
   ↓
DERIVATIVE
"How fast is it changing?"
   ↓
INTEGRAL
"How much accumulated?"
And:
derivative↔integral\boxed{ \text{derivative} \leftrightarrow \text{integral} }
through the Fundamental Theorem.

Part III — Weeks 10–11

Graph Theory & Algorithms


Week 10 — Graph Fundamentals

Graph:
G=(V,E)\boxed{ G=(V,E) }
where:
  • VV = vertices
  • EE = edges

Degree

Undirected:
deg⁡(v)\deg(v)
Handshaking lemma:
∑vdeg⁡(v)=2∣E∣\boxed{ \sum_v\deg(v)=2|E| }

Directed graphs

In-degree:
deg⁡−(v)\deg^-(v)
Out-degree:
deg⁡+(v)\deg^+(v)
and:
∑vdeg⁡−(v)=∑vdeg⁡+(v)=∣E∣\boxed{ \sum_v\deg^-(v) = \sum_v\deg^+(v) = |E| }

Complete graph

For:
Kn,K_n, ∣E∣=n(n−1)2\boxed{ |E|=\frac{n(n-1)}2 }

Representations

Adjacency matrix:
Aij=1A_{ij}=1
if edge exists.
Adjacency list:
text
A: B, C
B: A
C: A
Adjacency list space:
O(V+E)O(V+E)
Adjacency matrix space:
O(V2).O(V^2).

BFS

Breadth-First Search:
  • queue
  • level-by-level exploration
  • shortest paths in unweighted graphs
Complexity:
O(V+E)\boxed{ O(V+E) }

DFS

Depth-First Search:
  • stack/recursion
  • explores deeply
  • backtracking
Complexity:
O(V+E)\boxed{ O(V+E) }

Tree

A tree is:
  • connected
  • acyclic
and for nn vertices:
∣E∣=n−1\boxed{ |E|=n-1 }

DAG

DAG:
Directed Acyclic Graph\boxed{ \text{Directed Acyclic Graph} }
No directed cycles.
Useful for:
  • dependencies
  • scheduling
  • prerequisites
A topological ordering exists iff graph is a DAG.

Week 11 — Weighted Graph Algorithms

Path weight

∑w(e)\boxed{ \sum w(e) }
along the path.

Relaxation

Core shortest-path operation:
d[v]←min⁡(d[v],d[u]+w(u,v))\boxed{ d[v] \leftarrow \min(d[v],d[u]+w(u,v)) }

Algorithm choice

Unweighted

BFS\boxed{\text{BFS}}

Weighted, nonnegative

Dijkstra\boxed{\text{Dijkstra}}

Negative weights

Bellman–Ford\boxed{\text{Bellman–Ford}}

Dijkstra

Condition:
w(e)≥0\boxed{ w(e)\geq0 }
Greedy strategy:
repeatedly finalize nearest unprocessed vertex.
Typical efficient complexity:
O((V+E)log⁡V).O((V+E)\log V).

Bellman–Ford

Handles negative edges.
Relax all edges:
∣V∣−1|V|-1
times.
Complexity:
O(VE)\boxed{ O(VE) }
One additional successful relaxation indicates a reachable negative cycle.

Negative cycle

If total cycle weight is negative, you can loop repeatedly and reduce cost indefinitely.
Therefore finite shortest path may not exist.

Longest path in DAG

On DAGs:
d[v]=max⁡u→v(d[u]+w(u,v))\boxed{ d[v] = \max_{u\to v} (d[u]+w(u,v)) }
Process vertices in topological order.

Transitive closure

Answers:
Is vv reachable from uu?
It adds:
u→vu\to v
whenever a path already exists.

Adjacency matrix powers

If AA is adjacency matrix:
(Ak)ij\boxed{ (A^k)_{ij} }
counts walks of length:
kk
from ii to jj.
Important:
walks, not necessarily simple paths.

1. Highest-value formula sheet

If I had to compress Maths 1 to one page, these are the formulas I'd keep.

Sets

∣P(A)∣=2∣A∣|\mathcal P(A)|=2^{|A|} ∣A×B∣=∣A∣∣B∣|A\times B|=|A||B|

Line

m=y2−y1x2−x1m=\frac{y_2-y_1}{x_2-x_1} y−y1=m(x−x1)y-y_1=m(x-x_1) m1m2=−1m_1m_2=-1

Quadratic

xv=−b2ax_v=-\frac b{2a} x=−b±b2−4ac2ax= \frac{-b\pm\sqrt{b^2-4ac}}{2a} Δ=b2−4ac\Delta=b^2-4ac

Polynomial

P(a)=remainder on division by x−aP(a)=\text{remainder on division by }x-a P(a)=0  ⟺  x−a factorP(a)=0 \iff x-a\text{ factor}

Composition/inverse

(f∘g)(x)=f(g(x))(f\circ g)(x)=f(g(x)) f−1(f(x))=xf^{-1}(f(x))=x

Logs

ax=y  ⟺  log⁡ay=xa^x=y \iff \log_a y=x log⁡(xy)=log⁡x+log⁡y\log(xy)=\log x+\log y log⁡(xr)=rlog⁡x\log(x^r)=r\log x

Limits

lim⁡x→a−f(x)=lim⁡x→a+f(x)\lim_{x\to a^-}f(x) = \lim_{x\to a^+}f(x)
for two-sided existence.

Derivative

f′(x)=lim⁡h→0f(x+h)−f(x)hf'(x) = \lim_{h\to0} \frac{f(x+h)-f(x)}h (xn)′=nxn−1(x^n)'=nx^{n-1} (fg)′=f′g+fg′(fg)'=f'g+fg' (fg)′=f′g−fg′g2\left(\frac fg\right)' = \frac{f'g-fg'}{g^2}

Integral

∫xn dx=xn+1n+1+C\int x^n\,dx = \frac{x^{n+1}}{n+1}+C ∫abf=F(b)−F(a)\int_a^b f = F(b)-F(a)

Graphs

∑vdeg⁡(v)=2∣E∣\sum_v\deg(v)=2|E| BFS/DFS=O(V+E)\text{BFS/DFS}=O(V+E) tree: ∣E∣=∣V∣−1\text{tree: }|E|=|V|-1

2. Most dangerous conceptual traps

These are more important than memorizing fifty extra formulas.

Trap 1

Symmetric:
aRb⇒bRaaRb\Rightarrow bRa
is not the same as antisymmetric:
aRb∧bRa⇒a=b.aRb\land bRa\Rightarrow a=b.

Trap 2

Vertical line test:
Is it a function?
Horizontal line test:
Is it one-one?

Trap 3

f−1(x)f^{-1}(x)
does not mean:
1f(x).\frac1{f(x)}.

Trap 4

log⁡(x+y)≠log⁡x+log⁡y.\log(x+y) \neq \log x+\log y.

Trap 5

00\frac00
is not zero.
It means indeterminate.

Trap 6

Continuity does not guarantee differentiability.

Trap 7

f′(x)=0f'(x)=0
does not automatically mean minimum or maximum.

Trap 8

Definite integral is signed area.

Trap 9

DFS does not generally give shortest paths.

Trap 10

Dijkstra cannot safely handle negative edge weights.

3. Dependency map

Some topics depend heavily on earlier ones.
text
Functions
├── composition
│   └── chain rule
│
├── inverse
│   └── logarithms
│
└── graphs
    ├── limits
    ├── derivatives
    └── integrals

Relations
└── graph theory
    ├── adjacency
    ├── reachability
    └── transitive closure

Matrices
└── adjacency matrix powers
The syllabus isn't really eleven isolated chapters.
It is a connected dependency graph.

4. Suggested exam-priority hierarchy

If prep time becomes constrained, I would prioritize roughly:

Tier A — must be reliable

  • relation properties
  • functions/injectivity/surjectivity
  • straight-line equations and slope
  • quadratic roots/vertex/discriminant
  • polynomial roots/factor/remainder theorem
  • composition/inverse
  • logarithm rules
  • limits
  • derivative rules
  • critical points
  • basic integration
  • BFS vs DFS
  • Dijkstra vs Bellman–Ford

Tier B — important

  • multiplicity/end behavior
  • continuity
  • tangent equations
  • L’Hôpital
  • area between curves
  • DAG/topological ordering
  • transitive closure
  • adjacency matrix powers

Tier C — polish after fundamentals

  • deeper proofs
  • rarer graph identities
  • unusual transformations
  • more elaborate limit manipulations

5. The course as mental models

If formulas disappear from memory, these intuitions can reconstruct a lot.

Sets

Which objects belong?

Relation

Which pairs are connected?

Function

Each input gets exactly one output.

Slope

change in yy per change in xx.

Quadratic

symmetric curved function with one turning point.

Polynomial

degree + roots + multiplicities + leading term determine much of the graph.

Exponential

repeated multiplicative growth.

Logarithm

what exponent produced this value?

Limit

what value is behavior approaching?

Derivative

instantaneous local change.

Integral

accumulated change.

Graph

objects connected by relationships.

BFS

explore outward in layers.

DFS

go deep, then backtrack.

Dijkstra

repeatedly lock in nearest safe vertex.

Bellman–Ford

repeatedly propagate improvements everywhere.

6. Final mastery checklist

You should be able to answer yes to these:
  • Can I distinguish all four relation properties?
  • Can I classify injective/surjective/bijective functions?
  • Can I construct a line from two points?
  • Can I analyze a quadratic completely?
  • Can I infer polynomial graph behavior from factors?
  • Can I use remainder/factor theorem quickly?
  • Can I compose functions without reversing order?
  • Can I find an inverse and respect its domain?
  • Can I solve exponential/log equations?
  • Can I evaluate basic limits and recognize 0/00/0?
  • Can I differentiate standard functions correctly?
  • Can I identify extrema from f′f'?
  • Can I integrate basic polynomials and compute definite integrals?
  • Can I distinguish signed area from actual area?
  • Can I trace BFS and DFS?
  • Can I recognize trees and DAGs?
  • Can I choose BFS vs Dijkstra vs Bellman–Ford?
  • Can I interpret adjacency-matrix powers?
If most of those are solid, you have the core conceptual coverage of Maths 1.

Quick Recall & Flashcards

Test your active recall before proceeding to practice problems. Format:
Question :: Answer.
  • Equivalence Relation vs Partial Order distinction :: Equivalence is Symmetric; Partial Order is Antisymmetric
  • Vertex of parabola vs Critical point of derivative :: Vertex x=−b/(2a)x = -b/(2a) is where f′(x)=2ax+b=0f'(x) = 2ax + b = 0
  • Shortest path algorithm decision: Unweighted graph :: Breadth-First Search (BFS) in O(∣V∣+∣E∣)O(|V| + |E|)
  • Shortest path algorithm decision: Nonnegative weights :: Dijkstra's Algorithm in O((∣V∣+∣E∣)log⁡∣V∣)O((|V| + |E|)\log |V|)
  • Shortest path algorithm decision: General weights (negative edges allowed) :: Bellman–Ford Algorithm in O(∣V∣⋅∣E∣)O(|V| \cdot |E|)
  • Fundamental theorem linking derivatives and integrals :: Fundamental Theorem of Calculus (FTC): accumulation vs rate of change

Practice & Self-Check

Attempt these diagnostic questions to verify your operational understanding.
1

Question 1

If a directed graph GG has a topological ordering, which of the following MUST be true?
MCQ
3 Unit Assessment
A. GG has no directed cycles (it is a DAG)
B. GG is an undirected tree
C. GG is strongly connected
D. Every vertex has indegree equal to outdegree
2

Question 2

A critical point of a differentiable function f(x)f(x) occurs where:
MCQ
3 Unit Assessment
A. f(x)=0f(x) = 0
B. f′(x)=0f'(x) = 0 or f′(x)f'(x) is undefined
C. f′′(x)>0f''(x) > 0
D. f(x)=f(−x)f(x) = f(-x)

Active Recall Session

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