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
- Neither
- Odd
- 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 :
- is a lower bound, and
- 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
- If is bounded above, then converges to .
- If is not bounded above, then diverges to Similarly, let be a decreasing sequence
- If is bounded below, then converges to
- 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:
- Polynomials
- Proof: We can evaluate by substitution:
-
- Proof:
-
- 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
4.1 | Related Rates
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
- Domain and range
- x and y intercepts
- Limits at infinity and asymptotes
- Intervals of increase and decrease
- Intervals of concave up / down
4.10 | Optimization
Optimization
- Set the desired and find critical points