0 | Pre-Calculus Review

0.1 | Real-Valued Functions

Function:

  • Vertical Line Test
  • Let X and Y be sets. A function f is a mapping that assigns to each exactly one We use the notation

Domain and Range

  • Domain: List of possible inputs
  • Range: List of possible outputs

Examples

Odd and Even Functions

  • is called even if for all
  • is called odd if for all
  • Suppose is a function, and suppose that so that . Then we call x a root of f

Function Composition

Examples

Functional Inverses (only works with bijective/passes vertical line test)

Given find

Examples

Find the Domain

Even or Odd

  1. Neither
  2. Odd
  3. Even

Find the Inverse

0.2 | Polynomials

Def: Polynomials are functions of the form

Number of Roots

  • Fundamental Theorem of Arithmetic: Polynomials with degree n have n complex roots

Ex: Long Division

Ex: When is divided by , the remainder is -7. What is m?

1 | Sequence Limits

1.1 | Absolute Values

Triangle Inequality 1

Ex: Triangle Inequality 1: > For all

Proof: Assume WLOG that Case 1: Case 2: Case 3:

Triangle Inequality 2

Triangle Inequality 2: For all

Proof:

Absolute Value Problems

Ex 1: Single Bound

Simplifying:

Ex 4: Double Bound

Ex 5: Double Absolute

Ex 3: Double Absolute

1.2 | Sequences and Limits

Uniqueness of Sequence Limit

(Uniqueness of Sequence Limit) Let be a sequence. If has a limit L, then the limit L is unique.

1.3 | Rules for Limits

Epsilon-N

Epsilon-Delta definition of a limit

Let be a sequence and . We say that L is the limit of if for all there exists a real number N such that if , then

If such an L exists, we say that CONVERGES to L and write

If no such L exists, we say that DIVERGES

Ex: Prove that converges to 0

Proof: Let be given. We determine N so that by solving

Thus, for every , let . Then, for all ,

More Epsilon-N

Epsilon-Delta: every function - its limit will always be less than the smallest real number

Show that for all arbitrary

USING

AKA: the bound is too big for all . The bound should approach infinity.

USING

1.4 | Squeeze Theorem

Squeeze Theorem

(Squeeze Theorem for Sequences) If for all (for some ) and , then as well

Use the Squeeze Theorem to prove convergence:

Solution:

Hence, by the squeeze theorem.

1.5 | Recursive Sequences

Explicit vs Recursive

Explicit Sequence

Recursive Sequences

GLB and LUB

Let . Then is called the GREATEST LOWER BOUND of if :

  1. is a lower bound, and
  2. is the largest lower bound, that is, if is another lower bound of S, then Greatest Lower Bound of S = = infimum =

Greatest Lower Bound and Least Upper Bound

Ex: is bounded

  • NOTE: and might be in S, but do not have to be

Ex:

Ex:

Monotone Convergence Theorem:

MONOTONE = Strictly increasing or decreasing

(Theorem 1.5.7 in notes) Let be an increasing sequence

  1. If is bounded above, then converges to .
  2. If is not bounded above, then diverges to Similarly, let be a decreasing sequence
  3. If is bounded below, then converges to
  4. If is not bounded below, then diverges to

Ex: We have If a converges, then

We will show that the sequence converges using MCT To do this, we use induction to show that it is decreasing and bounded below. Base case: . Then Induction Step: Assume for some . We need to show, using , that . Since , , so . Dividing by 5 gives Therefore, By induction, for all n.

Now, we prove that is decreasing, or that for all n. Base case: so when Induction step: Assume for some Starting with , we have that Therefore, for all by induction. Since is decreasing and bounded below, it converges by the MCT.

Let and for all . Prove that

2 | Function Limits and Continuity

2.1 | Introduction to Function Limits

Epsilon-Delta

Example: Consider

  • -1 is not in the domain
  • has nothing to do with . In fact, it can exist even when is undefined.

Informal definition of a limit: The limit as approaches of is if is very close to when x is very close to

(Definition) Formal Limit Definition Let be a function and . We say that the limit as approaches a from the left is L and write

If for all there exists such that

Epsilon-Delta Examples

Example: Show that

Aside:

Proof: Let be given and choose . If then But so So implies , so

Example: Prove that

Rough Work: By making small we want to make small.

Proof must ensure that

Proof: Let be given. Choose . Thus and . Assume Note that

Now show that

Ex: Show that does not exist.

Proof; Assume towards a contradiction that for some Consider . Since L exists, there is such that

Let So , so this is equivalent to Similarly, So since

Contradiction!

2.2 | Sequential Characterization of Limits

2.3 | One-Sided Limits

Example: show that does not exist.

PROOF: It suffices to prove that the limits from both directions are not equal. Show Let and choose Then 0<x<7 so

Similarly, so the one sided limits are not equal.

2.4 | Fundamental Trig Limit

(Theorem) Fundamental Trig Limit)

Example: Show that

for all If then So Similarly, So

Example; Compute

2.5 | Limits at Infinity and Horizontal Asymptotes

(Definition) Limits as x approaches infinity Let be a function. We say that approaches L as x approaches and write if for there exists such that if , then

(Definition) Horizontal Asymptotes Let be a function and . We say that the line with equation is a horizontal asymptote of if wither or

2.6 | Fundamental Log Limit

(Definition) Fundamental Log Limit

2.7 | Infinite Limits and Vertical Asymptotes

(Definition) Limits to Infinity We say that as from the right if for all there exists such that if then and are all defined

Similarly, if or , we say that has a VERTICAL ASYMPTOTE at .

Example: So the limit is

2.8 | Continuity

Fact about continuity: is continuous at if and only if

(Theorems 2.8.7, 2.8.8, 2.8.9) The following functions are continuous everywhere in their domain:

  1. Polynomials
    1. Proof: We can evaluate by substitution:
    1. Proof:
    1. Proof:

(Theorem) Let be continuous at . The following are also continuous at .

(Theorem): If is continuous at , so it it’s inverse.

(Theorem): Suppose are continuous at , then is continuous at

(Proof) Sequential Characterization

is continuous at , by sequential characterization Now is a sequence converging to By continuity of at and the sequential characterization . By the sequential characterization, is continuous at

(Definition) We say is continuous on if and

2.9 | Types of Discontinuities

(Definition) Discontinuities has a removable discontinuity at if exists but does not equal has a jump continuity at if and both exist but are not equal has an infinite discontinuity at if has a vertical asymptote at has an oscillatory discontinuity if it oscillates

2.10 | Intermediate Value Theorem

(Theorem) IVT Suppose is continuous on and that .is between and . Then there exists such that

Ex: show that there is a real number such that .

PROOF: Let . This is a difference of continuous functions, thus it is continuous. So and is continuous on . Therefore,

Ex: Show that has a real root

PROOF: in . There is a root in by IVT

root in

root in

etc.

3 | Derivatives

3.1 | Average and Instantaneous Velocity

3.2 | Definition of the Derivative

Definition of the Derivative:

Continuity: A function is continuous at if

But continuity does not imply differentiability - counterexample:

Symmetric Difference Quotient:

3.3 | Derivatives of Common Functions

L’Hôpital’s Rule

3.4 | Derivative Rules

Power Rule:

Constant Rule: Let

Constant Multiple Rule: Let

Product / Sum Rule: Let

Power Rule:

Definition of

Product Rule: Let

Quotient Rule: Let

Logs and Exponents

Trig Functions

Inverse Trig Functions

3.5 | Linear Approximation

Local Linear Approximation

3.6 | Newton’s Method

Newton’s Method

3.7 | Derivatives of Inverse Functions

Inverse Function Theorem: For

Example:

3.8 | Implicit and Logarithmic Differentiation

Implicit Differentiation: Differentiate with respect to

  • Explicitly Defined:
  • Implicitly Defined:

Logarithmic Differentiation: everything

4 | Applications of the Derivative

Just relate the rates

4.2 | Extrema

Theorem (Extreme Value Theorem) Let be continuous on . Then, has a global maximum and a global minimum on .

4.3 | MVT

Theorem (Rolle’s Theorem) If is continuous on and differentiable on , and for some , then there exists some point such that

Theorem (MVT) If f is continuous on and differentiable on , there exists a such that

Theorem (Bounded Derivative Theorem) If , then

Ex: Prove that

is decreasing, so

4.4 | Antiderivatives

4.5 | First Derivatives and Direction

Derivative Tests

  • First derivative = increase / decrease
  • Second derivative = concave up / down
  • Critical points = when
  • Inflection points = when

4.6 | Second Derivatives and Concavity

See above

4.7 | Classifying Critical Points

First Derivative Test (FDT) and Second Derivative Test (SDT)

4.8 | L’Hopital’s Rule

Theorem (L’Hôpital’s Rule)

4.9 | Curve Sketching

Order of steps

  1. Domain and range
  2. x and y intercepts
  3. Limits at infinity and asymptotes
  4. Intervals of increase and decrease
  5. Intervals of concave up / down

4.10 | Optimization

Optimization

  • Set the desired and find critical points