1.30 Convergence and continuity Flashcards
(38 cards)
Simple sandwich rule for limits
w_n ≤ x_n ≤y_n for all n
If w_n tends to 0 and y_n tends to 0 then x_n does
e.g. 0 ≤ d(x,y) can be used
Archimedean property of reals
for any two positive real numbers a and b, a < b there exists a positive integer n such that na > b
DEF 1.32 Convergence
We say x_n → x ( xn converges to x) if d(x_n, x) → 0 as n → ∞.
x_n → x if for any ε > 0 ∃N ∈ N st ∀n > N we
have d(x, x_ n) < ε.
THM 1.33 convergence equivalence
Let (x_n) be a sequence in a metric space (X, d). Then the following
are equivalent:
- x_n → x;
- for every open U with x ∈ U, ∃N > 0 s.t. (n > N) ⇒ x_n ∈ U;
- for every ε > 0 ∃N > 0 s.t. (n > N) ⇒ x_n ∈ B_ε(x).
PROOF of THM 1.33 convergence equivalence
1→2: If x_n → x and x ∈ U, then there is a ball Bε(x) ⊂ U, since U is open. x_n → x so d(xn, x) < ε for n sufficiently large, so., x_n ∈ U for n sufficiently large
(there is N= N( ε) st ∀n>N lie in nhd Bε(x))
2→3: As U is open for each x ∃ε>0 s.tBε(x) ⊂ U. Thus x_n ∈Bε(x) as d(x,x_n)<ε
3→1: For a given ε>0 and large n x_n∈Bε(x) implies d(x_n,x)< ε
THM 1.34 x ∈ s̅ when sequence…
Let S be a subset of the metric space X.
Then x ∈ s̅ IFF there is a sequence (xn) of points of S with x_n → x
Corollary 1.35 (Closedness under taking limits)
A subset Y ⊂ X of a metric space
(X, d) is closed if and only if for every sequence (xn) in Y that is convergent in X its
limit is also in Y .
the closure s̅ is obtained from S by adding all possible limit points of sequences in S
Def (f_n) converges pointwise
(f_n) converges pointwise to f if for each x in [a.b] (f_n(x)) converges to f(x) in R
f_n→ f pointwise as n tends to infitiy f_n(x) → f(x) for all x in [a,b]
Proof: thm1.35 sequence and s̅
→
If x ∈ s̅ then by thm 1.28 we have V∩S ≠Ø for all nhds V of x. For each n we choose B_{1/n}(x). Then B_{1/n} ∩S ≠Ø.
For a sequence (x_n)∈S choose such a sequence in x_n ∈ B_{1/n}∩S.
as n→∞ d(x_n,x)→0 so x_n →x
←
If x ∉s̅ then there is a NHD U of x with U ∩S =Ø → no sequence in S can get into U so it cannot converge to x.
Example:
Take (R^2, d1), where d_1(x, y) = |x_1−y_1|+|x_2−y_2|
Consider sequence
(1/n,{2n+1}/{n+1} ). Limit?
We guess its limit is (0, 2). To
see if this is right, look at
d_1((1/n,{2n+1}/{n+1} ),(0, 2))
= |1/n| + |* -2|
= (1/n) + (1/(n+1)) → 0 as n → ∞. So the limit is (0, 2).
In C[0, 1] let f_n(t) = t^n and f(t) = 0 for 0 ≤ t ≤ 1.
Does fn → f in d1
d1(fn, f) = ∫_[0,1] t^n dt =
1/{n + 1} → 0
as n → ∞. So f_n → f in d1
graph is 0 then suddenly 1, looks more and more lik right side of a square
In C[0, 1] let f_n(t) = t^n and f(t) = 0 for 0 ≤ t ≤ 1.
Does fn → f, in d∞?
d∞(fn, f) = max{t^n: 0 ≤ t ≤ 1} = 1 does not converge to 0 as n → ∞. So fn does not converge f in d∞
In C[0, 1] let f_n(t) = t^n and
f(x) =
{ 0 for 0 ≤ x < 1.
{1 for x=1
Does fn → f, pointwise on [0,1]
f_n converges to f pointwise on [0,1] but f is not in X[0,1] as it is not continuous at 1
Consider the discrete metric, when does a sequence converge in this metric?
d_0(x, y) =
{1 if x ≠ y,
{0 if x = y
Then x_n→ x ⇐⇒ d_0(x_n,x) → 0
d_0 is 1 or 0 so IFF d_0(x_n,x) =0 for sufficiently large n. If there is an n_0 st x_n=x for all n ≥ n_0
ie
All convergent sequences in this metric are eventually constant. So, for example
d0(1/n, 0) does not converge to 0.
EXAMPLE pointwise convergence
f_n = 1 + x + x^2/2 +…+x^n/(n!) to
to f(x) = e^x
Proposition 1.37. R^2 convergence with standard metrics a-n b_n
Take R^2 with any of the metrics d1, d2 and d∞. Then a sequence
xn = (an, bn) converges to x = (a, b) if and only if an → a and bn → b.
A similar result holds for R^m in general.
PROOF
Proposition 1.37. R^2 convergence with standard metrics
Proof
If a_n → a and b_n → b then for any
ε > 0 there are N_a and N_b such that for N > N_a we
have |a_n − a| < ε/2 and for n > N_b |bn − b| < ε/2.
Thus for any n > N = max(Na, Nb):
ε > |an − a| + |bn − b| = d1(xn, x) ≥ d2(xn, x) ≥ d∞(xn, x),
which shows the convergence in all three metrics
converse: assume contradiction a_n does not converge to a: ie assume ∃ε >0 st for any N ∃ n>N s.t. |an − a| >ε. Then
d_1(x_n,x) ≥ d2(xn, x),≥ d∞(xn, x), = max {|a_n − a|,|b_n − b|} > |a_n − a| >ε showing divergence in three metrics
For any x_n and x metrics inequality
d_1(x_n,x) ≥ d_2(x_n,x) ≥ d_∞(x_n,x)
Thm 1.39 strength of convergence d_1
If f_n → f in (C[a, b], d∞), then f_n → f in (C[a, b], d1).
Informally speaking, d∞ convergence is stronger than d1 convergence.
PROOF
Thm 1.38. strength of convergence d_1
PROOF
REMARK of thm 1.38 point wise
d_∞ m.s and also f_n→ f pointwise
DEF 1.40 continuity at a point
Let f : (X, dX) → (Y, dY ) be a map between metric
spaces. We say that f is continuous at x ∈ X if for each ε > 0 there is a δ_{ε,x} > 0 such
that d_Y (f(x′), f(x)) < ε for all x′ ∈ X whenever d_X(x′, x) < δ_{ε,x}.
Another def continuity 1.4
Balls
for every ε > 0 there exists a δ > 0 such that f(Bδ(x)) ⊂ Bε(f(x)).
The map f is continuous, if it is continuous at all points of X.
or if given any x_n converging to x, f(x_n) converges to f(x) (sequential continuity the 1.41)
THM. 1.41 SEQUENTIAL CONTINUITY
For f : (X,dX) → (Y,dY), f is continuous at a if and only if,
whenever a sequence x_n → a, then f(x_n) → f(a).
In short, f is continuous at a IFF f permutes with the limit:
f(lim_n→∞ ( x) )= lim_n→∞[ f(x_n )]
for any sequence x_n → a.