integrala_lebesgue

Section 4.3 The Lebesgue Integral of a Measurable Nonnegative Function 79

PROBLEMS9. Let E have measure zero. Show that if f is a bounded function on E, then f is measurable

and fE f = 0.10. Let f be a bounded measurable function on a set of finite measure E. For a measurable

subset A of E, show that fA f = fE f XA.11. Does the Bounded Convergence Theorem hold for the Riemann integral?12. Let f be a bounded measurable function on a set of finite measure E. Assume g is bounded

and f = g a.e. on E. Show that fE f = fE g13. Does the Bounded Convergence Theorem hold if m(E) < oo but we drop the assumption

that the sequence (I f I} is uniformly bounded on E?14. Show that Proposition 8 is a special case of the Bounded Convergence Theorem.15. Verify the assertions in the last Remark of this section.16. Let f be a nonnegative bounded measurable function on a set of finite measure E. Assume

fE f = 0. Show that f = 0 a.e. on E.

4.3 THE LEBESGUE INTEGRAL OF A MEASURABLENONNEGATIVE FUNCTION

A measurable function f on E is said to vanish outside a set of finite measure provided thereis a subset E0 of E for which m(Eo) < oo and f =0 on E' Eo. It is convenient to say that afunction that vanishes outside a set of finite measure has finite support and define its supportto be {x e E I f (x) # 0).6 In the preceding section, we defined the integral of a boundedmeasurable function f over a set of finite measure E. However, even if m(E) = oo, if f isbounded and measurable on E but has finite support, we can define its integral over E by

Lf =lEDf>where E0 has finite measure and f = 0 on E - Eo. This integral is properly defined, that is, itis independent of the choice of set of finite measure E0 outside of which f vanishes. This is aconsequence of the additivity over domains property of integration for bounded measurablefunctions over a set of finite measure.

Definition For f a nonnegative measurable function on E, we define the integral of f overE by7

f = sup h I h bounded, measurable, of finite support and 0 < h < f on Ej. (8)f If

6But care is needed here. In the study of continuous real-valued functions on a topological space, the support ofa function is defined to be the closure of the set of points at which the function is nonzero.

7This is a definition of the integral of a nonnegative extended real-valued measurable function; it is not adefinition of what it means for such a function to be integrable. The integral is defined regardless of whether thefunction is bounded or the domain has finite measure. Of course, the integral is nonnegative since it is defined tobe the supremum of a set of nonnegative numbers. But the integral may be equal to oo, as it is, for instance, fora nonnegative measurable function that takes a positive constant value of a subset of E of infinite measure or thevalue oo on a subset of E of positive measure.

80 Chapter 4 Lebesgue Integration

Chebychev's Inequality Let f be a nonnegative measurable function on E. Then for anyA>0,

m{xEEI f(x)>A}<1fE

f. (9)

Proof Define EA = {x E E I f (x) > A). First suppose m(Ex) = oo. Let n be a natural number.Define EA, n = EA fl [-n, n] and On = A XEA. -Then on is a bounded measurable functionof finite support,

dinand0<On < fon Efor all n.E

We infer from the continuity of measure that

rlim m(EA,n)= Jim f On<J f.n -CO n-+oo E E

Thus inequality (9) holds since both sides equal no. Now consider the case m(Ex) < oo.Define h = A A. XEA. Then h is a bounded measurable function of finite support and 0 <_ h fon E. By the definition of the integral of f over E,

h<JE

Divide both sides of this inequality by A to obtain Chebychev's Inequality.

Proposition 9 Let f be a nonnegative measurable function on E. Then

IE f = 0 if and only if f = 0 a. e. on E. (10)

Proof First assume fE f = 0. Then, by Chebychev's Inequality, for each natural num-ber n, m(xEXI f(x) > 1/n) = 0. By the countable additivity of Lebesgue measure,m (x E X I f (x) > 0) = 0. Conversely, suppose f = 0 a.e. on E. Let (p be a simple functionand h a bounded measurable function of finite support for which 0 < p < h < f on E. Then9 = 0 a.e. on E and hence fE 9 = 0. Since this holds for all such (p, we infer that fE h = 0.Since this holds for all such h, we infer that fE f = 0.

Theorem 10 (Linearity and Monotonicity of Integration) Let f and g be nonnegativemeasurable functions on E. Then for any a > 0 and p > 0,

f(af+/3g) = afEf+18f 9.

Moreover,

if f < g on E, then fE f :s fE g. (12)


Proof For a > 0, 0 < h < f on E if and only if 0 < ah < a f on E. Therefore, by the linearityof the integral of bounded functions of finite support, fE a f = a fE f . Thus, to prove linearitywe need only consider the case a = 0 = 1. Let h and g be bounded measurable functions offinite support for which 0 < h < f and 0 < k < g on E. We have 0 < h + k < f + g on E,and h + k also is a bounded measurable function of finite support. Thus, by the linearity ofintegration for bounded measurable functions of finite support,

fh+fk=f(h+k)f(f+g).The least upper bound for the sums of integrals on the left-hand side, as h and k varyamong bounded measurable functions of finite support for which h < f and k < g, equalsfE f + fE g. These inequalities tell us that fE (f + g) is an upper bound for these same sums.Therefore,

ff+fgJ(f+g).It remains to prove this inequality in the opposite direction, that is,

j(f+g)_<fE Ef+fE 9.

By the definition of fE (f + g) as the supremum of fE P as P ranges over all boundedmeasurable functions of finite support for which 0 < C < f + g on E, to verify this inequalityit is necessary and sufficient to show that for any such function t,

ftff+fg.E < (13)

For such a function f, define the functions h and k on E by

h=min{f, £}andk=t-h on E.Let x belong to E. If 2(x) < f(x), then k(x) = 0 < g(x); if e(x) > f(x), then h(x) _£ (x) - f (x) < g (x ). Therefore, h < g on E. Both h and k are bounded measurable functionsof finite support. We have

0 <h < f,0 <k <gand1=h+konE.Hence, again using the linearity of integration for bounded measurable functions of finitesupport and the definitions of fE f and fE g, we have

JEl= IE h + fEk < fE f + j g.

Thus (13) holds and the proof of linearity is complete.In view of the definition of fE f as a supremum, to prove the monotonicity inequality

(12) it is necessary and sufficient to show that if h is a bounded measurable function of finitesupport for which 0 < h < f on E, then

fE h < fE g. (14)


Let h be such a function. Then h < g on E. Therefore, by the definition of fE g as asupremum, fE h < f E g. This completes the proof of monotonicity.

Theorem 11 (Additivity Over Domains of Integration) Let f be a nonnegative measurablefunction on E. If A and B are disjoint measurable subsets of E, then

In particular, if E0 is a subset of E of measure zero, then

fEJEEO f. (15)

Proof Additivity over domains of integration follows from linearity as it did for boundedfunctions on sets of finite measure. The excision formula (15) follows from additivity overdomains and the observation that, by Proposition 9, the integral of a nonnegative functionover a set of measure zero is zero.

The following lemma will enable us to establish several criteria to justify passage of thelimit under the integral sign.

Fatou's Lemma Let [f,) be a sequence of nonnegative measurable functions on E.

If {fn}- f pointwise a.e. on E, then f f <liminf f fn. (16)E E

Proof In view of (15), by possibly excising from E a set of measure zero, we assume thepointwise convergence is on all of E. The function f is nonnegative and measurable sinceit is the pointwise limit of a sequence of such functions. To verify the inequality in (16) itis necessary and sufficient to show that if h is any bounded measurable function of finitesupport for which 0 < h < f on E, then

JELh < liminf fn (17)

Let h be such a function. Choose M > 0 for which Ih I < M on E. Define E0 = {x E E I h (x ):P60}.Then m(E0) < oc. Let n be a natural number. Define a function hn on E by

hn = min{h, fn} on E.

Observe that the function hn is measurable, that

0<hn <Mon E0and hn=0 on E' E0.

Furthermore, for each x in E, since h (x) < f (x) and { fn (x)) --) f (x), {hn (X)) -+ h (x ). Weinfer from the Bounded Convergence Theorem applied to the uniformly bounded sequenceof restrictions of hn to the set of finite measure E0, and the vanishing of each hn on E ^- E0, that

lim f hn = lim f0 hn = f h = f h.n-+00 E n ->oo EE0 E


However, for each n, hn < fn on E and therefore, by the definition of the integral of fn overE, fE hn < fE fn. Thus,

JE= f liminf

fEn-+OO E

The inequality in Fatou's Lemma may be strict.

Example Let E = (0, 1] and for a natural number n, define fn = n X(0. 1/n). Then {fn}converges pointwise on E to f ° 0 on E. However,

f f=0<1= lim ffn.E n -+oo

As another example of strict inequality in Fatou's Lemma, let E = R and for a naturalnumber n, define gn = X(n. +,). Then {gn} converges pointwise on E to g°0 on E. However,

J g=0<1= lim fan.E

However, the inequality in Fatou's Lemma is an equality if the sequence if,,) isincreasing.

The Monotone Convergence Theorem Let {fn} be an increasing sequence of nonnegativemeasurable functions on B.

If f fn) - f pointwise a.e. on E, then

Proof According to Fatou's Lemma,

lim f ,f =f f.n E E

'EJE fn.However, for each index n, f, < f a.e. on E, and so, by the monotonicity of integration fornonnegative measurable functions and (15), fE fn < fE f. Therefore

flim sup fE ji.ff=hm f .fn .

oo

Corollary 12 Let {un} be a sequence of nonnegative measurable functions on E.

If f = 1 un pointwise a. e. on E, then ff=f un .

1

Proof Apply the Monotone Convergence Theorem with fn = Ek=1 uk, for each index n,and then use the linearity of integration for nonnegative measurable functions.


Definition A nonnegative measurable function f on a measurable set E is said to be integrableover E provided

JEf<00.

Proposition 13 Let the nonnegative function f be integrable over E. Then f is finite a. e. on E.

Proof Let n be a natural number. Chebychev's Inequality and the monotonicity of measuretell us that

m{xEEI f(x)=oo}<m{xEEI f(x)>n}<1Lf.E

But fE f is finite and therefore m{x E E I f (x) = oo} = 0.

Beppo Levi's Lemma Let[ fn } bean increasing sequence of nonnegative measurable functionson E. If the sequence of integrals [fE fn} is bounded, then { fn} converges pointwise on E to ameasurable function f that is finite a.e. onffn=ff<00.E and

limn*o0 E E

Proof Every monotone sequence of extended real numbers converges to an extended realnumber.8 Since { fn} is an increasing sequence of extended real-valued functions on E, wemay define the extended real-valued nonnegative function f pointwise on E by

f(x)= lim f(x) for all XE E.->o0

According to the Monotone Convergence Theorem, { fE fn) -+ fE f. Therefore, since thesequence of real numbers { fE fn} is bounded, its limit is finite and so fE f < oo. We inferfrom the preceding proposition that f is finite a.e. on E.

PROBLEMS17. Let E be a set of measure zero and define f = 00 on E. Show that fE f = 0-18. Show that the integral of a bounded measurable function of finite support is properly defined.

19. For a number a, define f (x) = x' for 0 < x < 1, and f (0) = 0. Compute fo f.20. Let {f, } be a sequence of nonnegative measurable functions that converges to f pointwise on

E. Let M > 0 be such that fE fn < M for all n. Show that fE f < M. Verify that this propertyis equivalent to the statement of Fatou's Lemma.

21. Let the function f be nonnegative and integrable over E and c > 0. Show there is a simplefunction ,1 on E that has finite support, 0 < ri < f on E and fE If - ,qI < E. If E is a closed,bounded interval, show there is a step function h on E that has finite support and f E I f - h I < E.

22. Let I fn) be a sequence of nonnegative measurable functions on R that converges pointwiseon R to f and f be integrable over R. ShowShow that

ifif JR n-if = limooRJ fn, then

f E= limao

f Efor any measurable set E.

8See page 23.

Section 4.4 The General Lebesgue Integral 85

23. Let {an} be a sequence of nonnegative real numbers. Define the function f on E = [1, oo) bysetting f(x) =anifn < x < n + 1. Show that fEf = E001 an

24. Let f be a nonnegative measurable function on E.(i) Show there is an increasing sequence {cpn} of nonnegative simple functions on E, each of

finite support, which converges pointwise on E to f.(ii) Show that fE f = sup { fET I (p simple, of finite support and 0 < 1P <, f on E}.

25. Let { fn} be a sequence of nonnegative measurable functions on E that converges pointwiseon E to f . Suppose fn < f on E for each n. Show that

ni[fn=1 fE E

26. Show that the Monotone Convergence Theorem may not hold for decreasing sequences offunctions.

27. Prove the following generalization of Fatou's Lemma: If { fn} is a sequence of nonnegativemeasurable functions on E, then

r r

flim inf fn < lim inf

f EE E

4.4 THE GENERAL LEBESGUE INTEGRAL

For an extended real-valued function f on E, we have defined the positive part f+ and thenegative part f- of f, respectively, by

f+(x)=max{f(x),0}and f-(x)=max(-f(x),0)forallxEE.Then f+ and f- are nonnegative functions on E,

f=f+-fonEand

Ifl=f++f-onE.Observe that f is measurable if and only if both f+ and f- are measurable.

Proposition 14 Let f be a measurable function on E. Then f+and f - are integrable over Ei f and only i f I f I is integrable over E.

Proof Assume f + and f - are integrable nonnegative functions. By the linearity of integra-tion for nonnegative functions, I f I = f+ + f - is integrable over E. Conversely, supposeIf I is integrable over E. Since 0 < f+ < If I and 0 < f- < If I on E, we infer from themonotonicity of integration for nonnegative functions that both f+ and f- are integrableover E.

Definition A measurable function f on E is said to be integrable over E provided If I isintegrable over E. When this is so we define the integral off over E by

EE.f =JEf+-J1


Of course, for a nonnegative function f, since f = f+ and f - = 0 on E, this definitionof integral coincides with the one just considered. By the linearity of integration for boundedmeasurable functions of finite support, the above definition of integral also agrees with thedefinition of integral for this class of functions.

Proposition 15 Let f be integrable over E. Then f is finite a.e. on E and

(18)fEfEE0f

Proof Proposition 13, tells us that If I is finite a.e.on E. Thus f is finite a.e.on E. Moreover,(18) follows by applying (15) to the positive and negative parts of f.

The following criterion for integrability is the Lebesgue integral correspondent of thecomparison test for the convergence of series of real numbers.

Proposition 16 (the Integral Comparison Test) Let f be a measurable function on E.Suppose there is a nonnegative function g that is integrable over E and dominates f in thesense that

I f l < g on E.Then f is integrable over E and

fE f Ifl.JE

Proof By the monotonicity of integration f o r nonnegative functions, I f I, and hence f, isintegrable. By the triangle inequality for real numbers and the linearity of integration fornonnegative functions,

JE f JEJE J 1.fl-Lf++Lf= E 0We ha ve arrived at our final stage of generality for the Lebesgue integral for functions

of a single real variable. Before proving the linearity property for integration, we need toaddress, with respect to integration, a point already addressed with respect to measurability.The point is that for two functions f and g which are integrable over E, the sum f +g is notproperly defined at points in E where f and g take infinite values of opposite sign. However,by Proposition 15, if we define A to be the set of points in E at which both f and g are finite,then m (E - A) = 0. Once we show that f + g is integrable over A, we define

JE(.f +g) = J(f+g).

We infer from (18) that f E (f +g) is equal to the integral over E of any extension of (f +g) I Ato an extended real-valued function on all of E.


Theorem 17 (Linearity and Monotonicity of Integration) Let the functions f and g beintegrable over E. Then for any a and 0, the function a f + (3g is integrable over E and

f(af+/3g)=aff+f3jg.E

Moreover,

if f g on E, then f<fE g.E

Proof If a > 0, then [a f ]+ = a f + and [a f ]- = a f -, while if a < 0, [a f ]+ = -a fand [a f ]- = -a f+. Therefore fE a f = a fE f, since we established this for nonnegativefunctions f and a > 0. So it suffices to establish linearity in the case a = R = 1. Bythe linearity of integration for nonnegative functions, I f I + IgI is integrable over E. SinceIf + gI If I + ISI on E, by the integral comparison test, f + g also is integrable over E.Proposition 15 tells us that f and g are finite a.e. on E. According to the same proposition,by possibly excising from E a set of measure zero, we may assume that f and g are finite onE. To verify linearity is to show that

f[f+gft -fE[f+g] =Lf f+ -fEf J+[f 8+-

fE9 1.

E E E

But(f+g)+- (f+g) =f+g=(f+- f-)+ (g+ -g-) onE,

and therefore, since each of these six functions takes real values on E,

(f+g)++f +g =(f+g) +f++g+onE.We infer from linearity of integration for nonnegative functions that

f(f+g) + fEf + fEg = fE(f+g) + fEf++ fEg+

(19)

Since f, g and f + g are integrable over E, each of these six integrals is finite. Rearrangethese integrals to obtain (19). This completes the proof of linearity.

To establish monotonicity we again argue as above that we may assume g and f arefinite on E. Define h = g - f on E. Then h is a properly defined nonnegative measurablefunction on E. By linearity of integration for integrable functions and monotonicity ofintegration for nonnegative functions,

IEJEJEJEh>0.

Corollary 18 (Additivity Over Domains of Integration) Let f be integrable over E. AssumeA and B are disjoint measurable subsets of E. Then

JAUBf=fAf+lB f. (20)


Proof Observe that If XAI -< If I and If XBI If I on E. By the integral comparison test,the measurable functions f XA and f XB are integrable over E. Since A and B are disjoint

f XAUB=f XA+f XBonE.But for any measurable subset C of E (see Problem 28),

fC f= fE f - Xc

Thus (20) follows from (21) and the linearity of integration.

(21)

The following generalization of the Bounded Convergence Theorem provides anotherjustification for passage of the limit under the integral sign.

The Lebesgue Dominated Convergence Theorem Let If,,) be a sequence of measurablefunctions on E. Suppose there is a function g that is integrable over E and dominates { fn} onE in the sense that I fn I < g on E for all n.

If {f,,} -a f pointwise a.e. on E, then f is integrable over E andnlim f fn = fE .E E

Proof Since I fn I < g on E and I f I < g a.e. on E and g is integrable over E, by the integralcomparison test, f and each fn also are integrable over E. We infer from Proposition 15that, by possibly excising from E a countable collection of sets of measure zero and using thecountable additivity of Lebesgue measure, we may assume that f and each fn is finite on E.The function g - f and for each n, the function g - fn, are properly defined, nonnegativeand measurable. Moreover, the sequence {g - fn} converges pointwise a.e. on E to g - f.Fatou's Lemma tells us that

JE(g- f) 5liminfJE(g- M.

Thus, by the linearity of integration for integrable functions,

L!Ef= fE(8f)S{1m1nffE(g-fn)= fEg - limsupfEfn,

that is,lim sup fE fn < fE f.

Similarly, considering the sequence {rg + fn }, we obtain

JE f < lim inf fE

A .

The proof is complete.


The following generalization of the Lebesgue Dominated Convergence Theorem, theproof of which we leave as an exercise (see Problem 32), is often useful (see Problem 33).

Theorem 19 (General Lebesgue Dominated Convergence Theorem) Let { fn} be a sequenceof measurable functions on E that converges pointwise a.e. on E to f. Suppose there is asequence {gn } of nonnegative measurable functions on E that converges pointwise a.e. on E tog and dominates { f, } on E in the sense that

I A I < gn on E for all n.

If limJE gn = fE g < 00' then Urn J fnn oo = f fn+00 E E

Remark In Fatou's Lemma and the Lebesgue Dominated Convergence Theorem, theassumption of pointwise convergence a.e. on E rather than on all of E is not a decorationpinned on to honor generality. It is necessary for future applications of these results. Weprovide one illustration of this necessity. Suppose f is an increasing function on all of R. Aforthcoming theorem of Lebesgue (Lebesgue's Theorem of Chapter 6) tells us that

limf(x+1/n)- f(x) = f(x) for almost all x. (22)n->oo 1/n

From this and Fatou's Lemma we will show that for any closed, bounded interval [a, b],

f f b fi(x) dx < f(b) - f (a).

In general, given a nondegenerate closed, bounded interval [a, b] and a subset A of [a, b] thathas measure zero, there is an increasing function f on [a, b] for which the limit in (22) fails toexist at each point in A (see Problem 10 of Chapter 6).

PROBLEMS

28. Let f be integrable over E and C a measurable subset of E. Show that fc f = fE f Xc.

29. For a measurable function f on [1, oo) which is bounded on bounded sets, define an = 44+1 f

for each natural number n. Is it true that f is integrable over [1, oo) if and only if the series7,' 1 an converges? Is it true that f is integrable over [1, oo) if and only if the series 2'1 anconverges absolutely?

30. Let g be a nonnegative integrable function over E and suppose If,) is a sequence ofmeasurable functions

finons on E such that foreach n, fI

gg

a.e.on rE. Show that

-< Jim sup J in < J lim sup fn .E E

31. Let f be a measurable function on E which can be expressed as f = g + h on E, where g isfinite and integrable over E and h is nonnegative on E. Define fE f = fE g + fE h. Show thatthis is properly defined in the sense that it is independent of the particular choice of finiteintegrable function g and nonnegative function h whose sum is f.


32. Prove the General Lebesgue Dominated Convergence Theorem by following the proof ofthe Lebesgue Dominated Convergence Theorem, but replacing the sequences {g - fn} and{g + fn}, respectively, by {gn - ff} and {gn + fn}.

33. Let { fn} be a sequence of integrable functions on E for which fn -* f a.e. on E and f isintegrable over E. Show that fE If - fn I 0 if and only if limn fE I.fn I = fE I f I (Hint:Use the General Lebesgue Dominated Convergence Theorem.)

34. Let f be a nonnegative measurable function on R. Show thatn

f = f fn-oo fn R

35. Let f be a real-valued function of two variables (x, y) that is defined on the squareQ = {(x, y) 10 < x < 1, 0 < y < 1} and is a measurable function of x for each fixed valueof y. Suppose for each fixed value of x, limy .o f(x, y) = f(x) and that for all y, we haveIf (x, y) I < g(x), where g is integrable over [0, 1]. Show that

li o f 1 f(x, Y)dx = f1 f(x)dx.

Also show that if the function f (x, y) is continuous in y for each x, then

h(y) = f f(x, y)dx10

is a continuous function of y.36. Let f be a real-valued function of two variables (x, y) that is defined on the square

Q = {(x, y) 10 < x < 1, 0 < y < 1} and is a measurable function of x for each fixed value ofy. For each (x, y) E Q let the partial derivative a fl ay exist. Suppose there is a function g thatis integrable over [0, 1] and such that

af(x, Y) <g(x)forall (x, y)EQ.

Prove that

ddy

(x, y)dxforall y[0, 1].Jo

1

f(x, y)dx =Jlfay

4.5 COUNTABLE ADDITIVITY AND CONTINUITY OF INTEGRATION

The linearity and monotonicity properties of the Lebesgue integral, which we establishedin the preceding section, are extensions of familiar properties of the Riemann integral. Inthis brief section we establish two properties of the Lebesgue integral which have no coun-terpart for the Riemann integral. The following countable additivity property for Lebesgueintegration is a companion of the countable additivity property for Lebesgue measure.

Theorem 20 (the Countable Additivity of Integration) Let f be integrable over E and{En}n° 1 a disjoint countable collection of measurable subsets of E whose union is E. Then

00f= f (23)fE n=1 En

Section 4.5 Countable Additivity and Continuity of Integration 91

Proof Let n be a natural number. Define fn = f Xn where Xn is the characteristic functionof the measurable set Uk=1 Ek. Then fn is a measurable function on E and

Ifnl<If Ion E.Observe that {fn} -+ f pointwise on E. Thus, by the Lebesgue Dominated ConvergenceTheorem, f f = lnn f fnE n-+ oo E

On the other hand, since {En}" 1 is disjoint, it follows from the additivity over domainsproperty of the integral that for each n,

fn

fn = I f fE k=1 E,t

Thusn co

lim fn = lim E f.fE f- n > C'0 fE n --> oo n Ek f = n=111

We leave it to the reader to use the countable additivity of integration to provethe following result regarding the continuity of integration: use as a pattern the proof ofcontinuity of measure based on countable additivity of measure.

Theorem 21 (the Continuity of Integration) Let f be integrable over E.(i) If {En }n° 1 is an ascending countable collection of measurable subsets of E, then

Jf = nl f f (24)En f&

(ii) If (En}n°1 is a descending countable collection of measurable subsets of E, then

f. (25)f f = limfE.0o En n -a oonn-=l n

PROBLEMS

37. Let f be a integrable function on E. Show that for each E > 0, there is a natural number Nfor which if n > N, then fErt f < E where En = {x E E I IxI > n}.

38. For each of the two functions f on [1, oo) defined below, show that limn fl" f exists whilef is not integrable over [1, oo). Does this contradict the continuity of integration?(i) Define f (x) = (-1)n/n, for n < x < n + 1.(ii) Define f(x) _ (sinx)/x fort <x <oo.

39. Prove the theorem regarding the continuity of integration.

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