Def A set is a collection of objects called elements
DefA is a subset of B (A⊂B or A⊆B) if ∀x∈A,x∈B
Def If A, B are sets, the Cartesian productA×B is the set of ordered pairs ∀a∈A,∀b∈B,(a,b)
DefA, B are sets; a functionF:A→B is a subset F⊂A×B s.t. (a,b)∈F∧(a,b′)∈F⟹b=b′; ∀a∈A,∃b∈B,(a,b)∈F
Deff:A→B is injective if ∀a,a′∈A,f(a)=f(a′)⟹a=a′, i.e., if ∀b∈B at most 1 a∈A with f(a)=b
Deff:A→B is surjective if ∀b∈B,∃a∈A,F(a)=b, i.e., if ∀b∈B at least 1 a∈A with f(a)=b
Deff:A→B is bijective iff f is both injective and surjective
Fact When f:A→B is bijective, ∃f−1:B→A s.t. f−1(b)=the unique a∈A s.t. f(a)=b
Def Given f:A→B,g:B→C; the function composition is gf or g∘f:A→C s.t. ∀a∈A,g∘f(a)=g(f(a))
Fact Target of f must match source of g that not all pairs of functions can be composed
Fact Function composition is not commutative
Def A relation from A to B is a subset R⊂A×B
Def An ordering on S is a relation ≤ on S×S s.t. (comparability) ∀x,y∈Sexactly one of x<y, y<x, x=y is true; (transitivity) ∀x,y,z∈S if x≤y and y≤z, then x≤z
Def An ordered set is a set (S,≤) with an order ≤ on S
Fact If x≤y and y≤x, then x=y
DefS is an ordered set, E⊂S; if ∃s∈S s.t. ∀e∈E,s≥e, then E is bounded above in S, and s is an upper bound for E
Defs is a least upper bound, supE, or the supremum of E if s is an upper bound for E; t is an upper bound for E⟹t≥s
Def Similarly, greatest lower bound for E is called infimum or infE
Def A field is a set where we can do arithmetic (+, −, ×, ÷ except 0). We require the existence of 0, 1 and the rules 0+x=x, x+y=y+x, (x+y)+z=x+(y+z), x(y+z)=xy+xz, xy=yx
Def An ordered field is a field F with an ordering < s.t. for x,y,z∈F with y<z, x+y<x+z; if x,y∈F, x>0,y>0, xy>0
Fact In an ordered field, ∀x∈Fx2≥0
Fact A bounded ordered set e⊂S has at most one supremum
Fact{x∈Q>0:x2<2} has no supremum
Def A nonempty set S has the least upper bound property if every nonempty E⊂S which is bounded above has a least upper bound
Thm If S has the least upper bound property, then S has the greatest lower bound property
Def An ordered field is complete if it has the least upper bound property
RmkQ is not complete, a finite ordered set has the least upper bound property
Thm There is a complete ordered field R
Def If F,G are ordered fields, an isomorphism is a function f:F→G s.t. f is a bijection; f(x)>f(y) iff x>y; f(x+y)=f(x)+f(y); f(xy)=f(x)f(y) (F,G being "the same field with the elements labelled differently")
PropF=R has the Archimedean property: for any x,y>0, ∃n∈Z>0 s.t. nx>y
PropF=R,n∈F>0, then there is a unique y∈F with yn=x ("nth roots exist")
Lemmaa is an upper bound for {z:z≥0,zn≤x} iff an≥x
Def A Cauchy sequence of Q is a sequence a=a1,a2,a3,… s.t. ∀ϵ>0,∃N∈Z>0 s.t. ∀i,j>N,∣ai−aj∣<ϵ
DefQ is dense in R if: if x,y∈R and x<y, then ∃p∈Q s.t. x<p<y
Fact Irrational numbers R∖Q is dense in R
Def An equivalence relation on S is a relation ∼∈S×S s.t. ∀x,x∼x; ∀x,y,x∼y⟺y∼x; ∀x,y,z,x∼y∧y∼z⟹x∼z (which is a bijection)
Def An equation relation ∼ partitions S into disjoint subsets (i.e. equivalence classes) si, each one of the form {s∈S:s∼s0} for some s0
Def A real number is an equivalence class of Cauchy sequences under this relation
Prop If a,b,c are Cauchy sequences, a∼b and b∼c, then a∼c
Defa>b if ∃δ>0,N s.t. ai>bi+δ for all i>N
Prop If x,y∈R, exactly one of x<y,x>y,x=y is true
Def A Cauchy sequence of R is a sequence x=x1,x2,…xi∈R s.t. ∀ϵ>0,∃N s.t. ∣xi−y∣<ϵ
Def A sequence of reals x has limity if ∀ϵ>0,∃N s.t. ∀i>N,∣xi−y∣<ϵ
ThmR has the least upper bound property
Thm (Monotone Convergence Thm) A non-decreasing sequence of xi∈R which is bounded above is Cauchy and thus converges to a limit (everything that rises must converge)
DefR, the extended reals, is an ordered set R∪{−∞,∞}
Def(Limit) If x=x1,x2,… is a sequence in R, limx=∞ if ∀E,∃N,∀i>N,xi>E
FactR is not a field
Def The equivalence classes under ∼ are called cardinalities
Fact The equivalence classes of finite sets are N (by pigeonhole principle)
Def A set S is infinite if ∃f:S↪S (monomorphism) an injection which is not a bijection
Fact An infinite set cannot be equivalent to a finite set
DefS is countably infinite if S∼Z>0
PropZ is countable
Fact
If S,T are countable, so is S∪T
If S countable, any subset of S is countably infinite or finite
If S,T are countable, so is S×T
Def if S,T are sets, we denote by ST the set of functions from T to S
Fact
If S,T are finite sets, ∣ST∣=∣S∣∣T∣
{0,1}T= set of subsets of T
S∅=∅
Thm (Cantor’s Diagonal Argument) There are uncountable sets, i.e., {0,1}Z≥0
CorR is uncountable
Def A vector space over a field k (a k-vector space) is a set V, whose elements are called vectors with operations + (addition) and ⋅ (scalar multiplication)
DefV is a vector space over R; a norm on V is a function ∣∣⋅∣∣:V→R s.t. (nonnegativity) ∀v∈V,∣∣v∣∣≥0, and ∣∣v∣∣=0 iff v=0; (homogeneity) ∣∣λv∣∣=∣λ∣∣∣v∣∣∀λ∈R,v∈V; (triangle inequality) ∀v,w∈V,∣∣v+w∣∣≤∣∣v∣∣+∣∣w∣∣
Rmk the set of functions f:R→R bounded between −1 and 1 are not a vector space
Def The norm ballBv,∣∣⋅∣∣(a) is the set {v∈V:∣∣v∣∣≤a}
Def A subset S of a vector space V is convex if ∀v,w∈S, the line segment vw is continued in S
Fact Norm balls are always convex
Def
(Lp norm) ∣∣(x1,…,xn)∣∣p=(∣x1∣p+⋯+∣xn∣p)p1
(L∞ norm) ∣∣(x1,…,xn)∣∣∞=maxi∣xi∣
(sup norm)∣∣f∣∣sup=supx∣f(x)∣
Fact∣∣v∣∣1≥∣∣v∣∣2≥∣∣v∣∣∞, ∣∣v∣∣1≤2∣∣v∣∣∞, ∣∣v∣∣1≤d∣∣v∣∣2 for v∈Rd
Def An inner product space is a vector space V with a function <,>:V×V→R s.t. (symmetry) ⟨x,y⟩=⟨y,x⟩; ⟨x1+x2,y⟩=⟨x1,y⟩+⟨x2,y⟩; (linearity in first component) ⟨λx+y,z⟩=λ⟨x,z⟩+⟨y,z⟩; (positive-definiteness) ⟨x,x⟩≥0 and ⟨x,x⟩=0 iff x=0
Thm For every inner product space V, ∣∣v∣∣:=⟨v,v⟩21 is a norm, i.e., every inner product space is a normed space
Thm (Cauchy-Schwarz Inequality) ⟨x,y⟩≤∣∣x∣∣∣∣y∣∣
Cor If v,w∈Rn, Cauchy-Schwarz says cosθ=∣∣v∣∣∣∣w∣∣⟨v,w⟩≤1, where θ is the angle between v and w
Fact correlation coefficient ρ measures the relation between the variables, i.e., the angle between x and y if everything is normalized to the mean 0
ρ=1 iff ∀i,a>0,yi=axi iff θ=0
ρ>0 iff x,y are at an acute angle; ρ<0 for obtuse angle; ρ=0 for orthogonal/perpendicular
Def A metric space is a set X with function d:X×X→R s.t. (nonnegativity) d(x,y)≥0, with d(x,y)=0 iff x=y; (symmetry) d(x,y)=d(y,x); (triangle inequality) d(x,z)≤d(x,y)+d(y,z)
Fact If V is a normed vector space, then the function d(x,y)=∣∣x−y∣∣ is a metric
DefX is metric space with metric d; the open ball of radius r around x∈X is BX,d(x,r)={y∈X:d(x,y)<r}
DefX is a metric space, S⊂X; the interior of S with respect to X, denoted intX(S), is the set {s∈S:∃ϵ>0,BX(s,ϵ)⊂S}
Def A subset S⊂X is open if intX(S)=S
Thm∀S⊂X, if int(int(S))=int(S), then int(S) is open
Prop (Properties of open/closed sets)
Any union of an arbitrary collection of open sets is open
Any intersection of a finite collection of open sets is open
Any union of a finite collection of closed sets is closed
Any intersection of an arbitrary collection of closed sets is closed
Prop Every open set S⊂X is a union of some collection of open balls
DefX is a metric space, p∈X; a neighborhood of p is an open set U∋p
DefE is a subset of metric space X; x∈X is an accumulation point or limit point of E in X (i.e., x∈LX(E)) if for every neighborhood U∋x, U∩E contains a point not equal to x, i.e., ∀B(x,ϵ),ϵ>0
Def A point x is an isolated point of E in X if x∈E and x is not a limit point of E in X
Prop If X=Rn with Euclidean metric and E⊂X, then any point in int(E) is an accumulation point for E
Def Let x1,x2,… be a sequence of points in a metric space X; limi→∞xi=x if, for every neighborhood U of x, ∃NU s.t. ∀i>NU, xi∈U; i.e.; ∀ϵ>0,∃Nϵ s.t. ∀i>Nϵ, xi∈B(x,ϵ) i.e. d(x,xi)<ϵ
Thm Let E⊂X; x∈LX(E) iff ∃ a sequence x1,x2,⋯∈E∖{x} with limi→∞xi=x
DefE is closed in X if LX(E)⊂E
DefE⊂X is closed if its complement E (:=X∖E) is open
Def The closure of E in X is clos(E)=E∪LX(E); i.e.; clos(E)=int(E), or clos(E)= intersection of all closed subsets of X containing E
Rmkclos(E)=E iff E is closed
Defx is a subsequential limit of x1,x2,… if ∃ a sequence xi1,xi2,… (i1<i2<⋯) with limj→∞xij=x
Prop If limi→∞xi=x, then x is the only subsequential limit
Thm (Bolzano–Weierstrass Thm) Any bounded sequence of real numbers has a subsequential limit
Thm (Baby Bolzano–Weierstrass Thm) If K is a finite set of real numbers, then any sequence x1,x2,⋯∈K has a subsequential limit (by pigeonhole principle)
Prop Let x1,x2,… a sequence in X, E={x1,x2,…}⊂X; if x is an accumulation point of E, it is a subsequential limit of x1,x2,…
Def Let K be a subset of a metric space X; K is compact if, for any collection of open sets of X ({Us}s∈S) which coversK (i.e. K⊂∪s∈SUs), there is a finite subcollection U1,U2,…,UN which still covers K
Fact A finite set K is compact
Fact Any unbounded subset of R (including R,Z) is noncompact
Fact(0,1) is noncompact; [0,1] is compact
Thm (Heine-Borel Thm) ∀S⊂Rn, S is closed and bounded iff S is compact
Thm Let x1,x2,… be an infinite sequence in a compact subset K⊂X; then x1,x2,… has a convergent subsequence where limit is in K
Thm An infinite subset S of a compact set K⊂X has an accumulation point in K
Cor If x1,x2,… is a sequence of points in compact set K, then x1,x2,… has a subsequential limit in K
Fact Compact sets are always closed so all accumulation points/subsequential limits are in K
Prop A closed subset Y of a compact set K⊂X is compact
DefX is a metric space, E⊂X. E is bounded if ∃BX(x,r)⊃E
Def Let X be a metric space, E⊂X. E is dense in X if closX(E)=X
Prop If K⊂X is compact, then K is bounded
Def An n-cell/box in Rn is a set {a1≤x1≤b1,…,an≤xn≤bn}=[a1,b1]×⋯×[an,bn]
Prop (Nested Interval Theorem) If [a1,b1]⊃[a2,b2]⊃[a3,b3]⊃⋯ is a sequence of nested closed intervals in R, ∃x∈R s.t. ∀i,x∈[ai,bi]
Def A sequence x1,x2,… in a metric space X is a Cauchy sequence if ∀ϵ>0,∃Nϵ s.t. ∀i,j>Nϵ,d(xi,xj)<ϵ
Prop If x1,x2,… is a Cauchy sequence, it has at most one subsequential limit, limixi=x
Def If every Cauchy sequence in X converges, X is complete
Prop Every compact metric space is complete
Def The Cantor setE:=∩i=0∞Ei with E0=[0,1],E1=[0,31]∪[32,1],E2=[0,91]∪[92,31]∪[32,97]∪[98,1],…
Fact The Cantor set E is an intersection of closed sets, so it is closed, but (1) E has no isolated points; (2) E contains no closed interval
Def (Roughly Minkowski Dimension) Let X be a bounded metric space. Define ∀ϵ>0,C(x,ϵ)= smallest number of ϵ-balls that can cover X. The dimension of X is the inverse exponent of C(x,ϵ)
FactC(x,ϵ) for: (1) line segment ∼c⋅ϵ−1, (2) square ∼c⋅ϵ−2, (3) three points =3⋅ϵ0, (4) line circumscribing square ∼4cϵ−1, (5) circumference of circle ∼c′⋅ϵ−1
Prop The Cantor set is uncountable, closed, and contains no positive length interval
Def(Limit) Let X be a metric space, E⊂X be a subset, f:E→Y, and a an accumulation point in LX(E), b∈Y. limx→af(x)=b, if for every neighborhood U of b, ∃ a neighborhood V of a s.t. ∀x∈V∖a,f(x)∈U. Equivalently, ∀ϵ>0,∃δ s.t. if 0<d(x,a)<δ then d(f(x),b)<ϵ
Rmk In R, 0<∣x−a∣<δ,∣f(x)−b∣<ϵ
Def (Notion) For f:X→Y, S⊂X. f(S)={f(x):x∈S}
DefX,Y are metric spaces, f:X→Y is a function. f is continuous at a point p∈X if ∀ neighborhood V of f(p), ∃ a neighborhood U of p s.t. f(U)⊂V
Prop The following are equivalent: (1) f is continuous of p; (2) limx→pf(x)=f(p)
Deff:X→Y is continuous if:
(Analysts’ Definition) it is continuous at every point p of X, i.e., limx→pf(x)=f(p)
(Topologists’ Definition, Equivalent) ∀ open subset V⊂Y, f−1(V)={x∈X:f(x)∈V} is open in X
Fact about continuity
Constant functions are continuous
The identity map i:X→X is continuous
If f,s:X→R are continuous, so are f+g, f−g, fg (though not necessarily gf)
Any polynomial p:R→R is continuous
If f:X→Y and g:Y→Z are continuous, so is g∘f:X→Z
If E⊂Y, and g:Y→Z is continuous, then g∣E:E→Z is continuous (g∣E: g is restricted to E, i.e., ∀e∈E,g∣E(e)=g(e))
A function X→Rn(f1,f2,…,fn) is continuous iff ∀i, fi is constant
Thm(Weak Extreme Value Theorem) A function on a finite set has a maximum
Thm(Extreme Value Theorem) Let K be a compact metric space and f:K→R be a continuous function. Then ∃x∈K s.t. f(x)=supp∈Kf(p)
Def A function whose input is another function is functional (e.g., X be space of [0,1]→R, F:X→R functional)
Def A metric space X is connected if:
(Analysts’ Definition) ∃ surjective continuous function f:X→{0,1} (Note {0} is open)
(Topologists’ Definition, Equivalent) ∃ two nonempty open subsets U0,U1⊂X s.t. U0∩U1=∅ and U0∪U1=X (In this case, U1=U0c is closed, from where also clopen)
Fact about connectedness: (1) The Cantor set is totally disconnected: the only connected subsets are single points; yet the Cantor set has no isolated points. (2) Q is not connected
Def Let f1,f2,… be functions X→Y, and f(x)=limn→∞fn(x) when this limit exists. If ∀x∈X the limit exists, we say fi→fpointwise
Fact about sequences of functions
For bump function f1(x) a bump, f2(x)=f1(x−1), f3(x)=f1(x−2), …; pointwise limit f(n)=limn→∞fn(n)=0. Note that fi→0 is not true in the sup norm
The pointwise limit of continuous functions is not always continuous
Def Let f1,f2,… be functions X→R. We say f1,f2,… converges pointwise to f if, ∀x∈X, limi→∞fi(x)=f(x), i.e., ∀x∈X, ∀ϵ>0, ∃Nx,ϵ s.t. ∀n>Nx,ϵ, ∣fn(x)−f(x)∣<ϵ
Def We say f1,f2,… converges uniformly to f if, ∀ϵ>0, ∃Nϵ s.t. ∀x∈X, ∀n>Nϵ, ∣fn(x)−f(x)∣<ϵ
Rmk on uniform convergence
If f1,f2,… converges uniformly to f, it converges pointwise to f
If f1,f2,… are bounded functions in X, then f1,f2,⋯→f uniformly iff limi→∞fi=f if the metric space of bounded functions with sup norm
X=[0,c],c<1, fn(x)=xn does uniformly converge to 0
Def We say a function f:[a,b]→R is L2-null if ∫ab∣f(x)∣2dx=0
Def We say f and g are equivalent in L2 (f∼L2g) if f−g is L2-null
DefThe space of L2 functions in [a,b] is the set of convergence classes for ∼L2 (equivalence class)
Fact Denote f∼g if ∣∣f−g∣∣2=0. The set of equivalence classes for this relation forms a normed vector space called the space of L2 functions
Thm Let X be a set, Y be a complete metric space. B(X) is the normed vector space of bounded functions X→Y. Then B(X) is complete
Def A complete normed vector space is called a Banach space
Def A complete inner product space is called a Hilbert space
Thm (Uniform Limit Theorem) Let f1,f2,… be a subsequence of continuous functions uniformly converging to f. Then f is continuous
Rmk about Uniform Limit Theorem
As a consequence, if Bc(X)⊂B(X) is the subspace of continuous functions, then Bc is closed and complete
As a consequence, if Bcts(X)⊂bounded B(X) is the subspace of bounded continuous functions, then Bcts is closed and complete. f1,f2,… is a Cauchy sequence of cts (continuous) bounded functions. f1,f2,… uniformly converges to some f in B(X) because B(X) is complete. By Uniform Limit Theorem, f is continuous, f∈Bcts(X)
Def Let f:(a,b)→R. We write f(x+)=q with x∈(a,b) to mean: limn→∞f(xn)=q for every sequence x1,x2,⋯⊂(x,b), i.e., "limit as f goes to x from above". f(x−)=q means same thing but for sequences in (a,x)
Propf is continuous at x iff f(x+) exists, f(x−) exists, and f(x−)=f(x)=f(x+). In particular, this applies to sequences used to define f(x+) and f(x−)
Lemma If a subsequence can be partitioned into a finite union of subsequences, each converging to L, then the sequences converge to L
DefTypes of discontinuities
removable: f(x−)=f(x+) but f(x) does not equal to these
jump: f(x−) and f(x+) both exist but are not equal
essential: either f(x−) or f(x+) does not exist
Thm (Froda’s Theorem) Let f:R→R be a function, Discont(f)⊂R be the set {x∈R:f is discontinnuous at x}. A set S⊂R can be Discont(f) for some f:R→R iff it is the union of countably many closed sets (i.e., any closed set)
Def A function f:(a,b)→R is monotone nondecreasing if f(y)≥f(x) whenever y≥x and strictly increasing if f(y)>f(x) whenever y>x
Prop If f is monotone nondecreasing, then f(x−)=supy<xf(y) and f(x+)=infy>xf(y), i.e., f(x−)≤f(x) and f(x)≤f(x+). All discontinuities are jump discontinuities
Thm If f is monotone nondecreasing, Discont(f) is countable
Rmk (Intuition of Uniform Convergence) fn is contained in ϵ-band around f⟺∀x,fn(x)∈(f(x)−ϵ,f(x)+ϵ)⟺∀x,∣fn(x)−f(x)∣<ϵ
Def Let s1,s2,… be a sequence of real numbers. We define limsupn→∞sn=limn→∞supk≥nsk
Def Let s1,s2,… be a sequence of real numbers. We define liminfn→∞sn=limn→∞infk≥nsk
Proplimsup and liminf always exist in R∪{∞,−∞}, and exist in R iff the sequence is bounded
Rmklimsn=∞ is not the same thing as "sn unbounded above"
Proplimsupsn=∞⟺sn unbounded above. liminfsn=−∞⟺sn unbounded below. If sn is unbounded, so is every tail of sn, so supk≥nsk=∞ for every n
Proplimsup is the supremum of all subsequential limits. liminf is the infimum of all subsequential limits
If sn→s=limsn, then limsupsn=liminfsn=limsn
Conversely, if limsupsn=liminfsn, then limsn exists and equals to these
Def We define partial sumssn=∑i=1nai
Def Define ∑i=1∞ai=limn→∞∑i=1nai be a series with a sequence of partial sums
Def If the limit limn→∞∑i=1nai exists, in which case we say, the infinite series ∑i=1∞aiconverges
Rmk For this definition, make sure we are summing something with an ordering (e.g., no sum of all rational numbers)
Rmk For n>m, sn−sm=∑i=1nai−∑i=1mai=∑i=m+1nai. If ∑ai converges, limai exists and =0 (but not conversely)
Example (Geometric series)
x∈R, ∑i=0∞xi=1+x+x2+x3+⋯. If ∣x∣≥0, xi does not converge to 0, so the series does not converge; when ∣x∣<1, this converges to 1−x1
sn=∑i=1nxn=1+x+⋯+xn=1−x1−xn+1
Example (Harmonic series) ∑n=1∞n1=1+21+31+⋯=1+=42(21+31)+≥84(41+51+61+71)+≥168(81+⋯)
Prop (Comparison test) Suppose ∑cn be convergent series, ∑dn be divergent series. Let ∑an be some series we wish to analyze. If ∃N s.t. ∀n>N, ∣an∣≤cn, then ∑an converges. If ∃N s.t. ∀n>N, ∣an∣≥dn, then ∑an diverges.
Prop (Tail test) ∑i=1∞ai converges iff all its tails ∑i=m∞ai converge
Prop (Cauchy condensation test) Suppose an is non-negative and monotone non-increasing. Then ∑n=1∞an converges iff ∑k=0∞2ka2k converges
Prop∑n=1∞ns1 converges iff s>1
Prop (Root test) If limsupn→∞n∣an∣<1, then ∑k=1∞ak converges. If limsupn→∞n∣an∣>1, then ∑k=1∞ak diverges
Prop (Ratio test) If limsupn→∞anan+1<1, ∑an converges. If liminfn→∞anan+1>1, ∑an diverges
Prop (Alternating series test) Let an be a sequence with alternating signs and suppose ∣an∣ are monotone decreasing. Then ∑an converges iff liman=0
Thm Suppose a1,a2,… is a sequence s.t. ∑i=1∞∣ai∣ converges (absolute convergent), then ∑ai converges, and if ai′ is any arrangement of ai, then ∑ai′ converges to ∑ai. If ∑ai is convergent but not absolute convergent, I can find a rearrangement which diverges and a rearrangement which sums to x∀x∈R
Thm (Weierstrass M-test) Let M1,M2,… a sequence of non-negative reals s.t. ∑i=1∞Mi converges, and f1,f2,… a sequence of functions X→R s.t. ∀x∈X,∣fi(x)∣≤Mi. Then ∑i=0∞fi converges uniformly
Def Some functions can be expressed as infinite sums of trigonometric functions, i.e., Fourier series. There is Gibbs phenomenon, the oscillatory behavior around a jump discontinuity
Def We say f is differentiable of x∈X if for all sequences t1,t2,⋯→x, limt→xt−xf(t)−f(x) exists. In this case, the limit is always the same and we call it f′(x)
Rmk If the limit exists, limt→xf(t)−f(x)=0, so f differentiable at x⟹f continuous at x, but not ⟸ (e.g., corner)
Prop Let f:(a,b)→R, let x be an maximum, i.e., ∀y∈(a,b),f(x)≥f(y), and suppose f is differentiable at x. Then f′(x)=0
Lemma If f is continuous on [a,b] and f(a)=f(b), then f has an extremum in (a,b)
Thm (Mean Value Theorem) Let f:[a,b]→R be continuous, differentiable on (a,b). ∃x∗∈(a,b) s.t. f′(x∗)=b−af(b)−f(a) (Note h(x)=t(f(b)−f(a))x−(b−a)f(x))
Def A partition of [a,b] is a "finite set of real numbers": a=x0≤x1≤⋯≤xn=b, Δxi=xi+1−xi
Def Riemann upper integral of f on [a,b] is infPU(P,f), lower integral of f on [a,b] is supPL(P,F)
PropL(P1,f)≤U(P2,f) for every pair of partitions P1,P2
Def We say P∗ is a refinement of P if every breakpoint of P is a breakpoint of P∗
Lemma If P∗ a refinement of P, then L(P,f)≤L(P∗,f)≤U(P∗,f)≤U(P,f)
Fact If P1 and P2 are partitions, there is a partition Q which is a common refinement of both (simply take the union of breakpoints(P1)∪breakpoints(P2)), L(P1,f)≤L(Q,f)≤U(Q,f)≤U(P2,f)
Def If lower integral = upper integral (infPU(P,f)=supPL(P,f)), we say f is Riemann integrable on [a,b] and define ∫abf(x)dx to be the common value
Rmk (Dirichlet function) χQ(x)={10x∈Qx∈/Q with χQ:[a,b]→{0,1}, is non-integrable (U(P,χQ)=b−a,L(P,χQ)=0)
RmkχCantor set(x)={10x∈Cantor setx∈/Cantor set with χCantor set:[0,1]→{0,1}, is integrable with ∫01χCantor set(x)dx=0 (U(Pk,f)=(32)k+ϵ,infPU(P,f)≤infkU(Pk,f)=0)
Thm If f is continuous, then f is integrable
Thm If f is (bounded) monotone, then f is integrable
Fact
If c∈[a,b] and f integrable on [a,b] then ∫abf(x)dx−∫acf(x)dx=∫cbf(x)dx
If f is integrable, then ∣f∣ is integrable, and ∫ab∣f(x)∣dx≥∣∫abf(x)dx∣ (triangle inequality)
Def (Indefinite integral) Let f integrable on [a,b], then we define F:[a,b]→R by F(x)=∫axf(t)dt
ThmF is continuous and Lipschitz
Thm (Fundamental Theorem of Calculus, FToC) If f is continuous at x0∈[a,b] and integrable, then F is differentiable at x0, and F′(x0)=f(x0)