William Arveson Department of Mathematics University of California Berkeley, CA USA

Size: px
Start display at page:

Download "William Arveson Department of Mathematics University of California Berkeley, CA USA"

Transcription

1 NOTES ON MEASURE AND INTEGRATION IN LOCALLY COMPACT SPACES William Arveso Departmet of Mathematics Uiversity of Califoria Berkeley, CA USA 25 March 1996 Abstract. This is a set of lecture otes which preset a ecoomical developmet of measure theory ad itegratio i locally compact Hausdorff spaces. We have tried to illumiate the more difficult parts of the subject. The Riesz-Markov theorem is established i a form coveiet for applicatios i moder aalysis, icludig Haar measure o locally compact groups or weights o C -algebras...though applicatios are ot take up here. The reader should have some kowledge of basic measure theory, through outer measures ad Carathéodory s extesio theorem. Cotets Itroductio 1. The trouble with Borel sets 2. How to costruct Rado measures 3. Measures ad liear fuctioals 4. Baire meets Borel 5. The dual of C 0 () The preparatio of these otes was supported i o-egligible ways by Schitzel ad Pretzel. 1 Typeset by AMS-TE

2 2 WILLIAM ARVESON Itroductio At Berkeley the material of the title is taught i Mathematics 202B, ad that discussio ormally culmiates i some form of the Riesz-Markov theorem. The proof of the latter ca be fairly straightforward or fairly difficult, depedig o the geerality i which it is formulated. Oe ca elimiate the most serious difficulties by limitig the discussio to spaces which are compact or σ-compact, but the oe must still deal with differeces betwee Baire sets ad Borel sets; oe ca elimiate all of the difficulty by limitig the discussio to secod coutable spaces. I have take both shortcuts myself, but have ot bee satisfied with the result. These otes preset a approach to the geeral theory of itegratio o locally compact spaces that is based o Rado measures. My ow experiece i presetig alterate approaches has coviced me that Rado measures are the most sesible way to reduce the arbitrariess ad the bother ivolved with doig measure theory i these spaces. We prove the Riesz-Markov theorem i geeral, i a form appropriate for costructig Haar measure o locally compact groups or for dealig with weights o commutative C -algebras. If I have eglected to metio sigificat refereces i the bibliography it is partly because these lecture otes have bee dashed off i haste. I apologize to ay of my colleagues who may have bee abused or offeded, i that order. Fially, I wat to thak Cal Moore for poitig out a error i the proof of Propositio 2.1 (the preset versio has bee fixed) ad Bob Solovay for supplyig the idea behid the example precedig Propositio The trouble with Borel sets Throughout these otes, will deote a locally compact Hausdorff space. A Borel set is a subset of belogig to the σ-algebra geerated by the closed sets of. A Baire set is a elemet belogig to the σ-algebra geerated by the compact G δ s...that is, compact sets K havig the form K = U 1 U 2..., where U 1, U 2,... is a sequece of ope sets i. We will write B (resp. B 0 ) for the σ-algebra of all Borel (resp. Baire) sets. If the topology of has a coutable base the B = B 0. It is a good exercise to prove that assertio. I geeral, however, there is a differece betwee these two σ-algebras, eve whe is compact. At the same time, each of them is more or less ievitable: B is associated with the topology of ad B 0 is associated with the space C 0 () of cotiuous real-valued fuctios o which vaish at (see Propositio 4.1). I these otes we deal maily with Borel sets ad Borel measures. The correspodig results for Baire sets ad Baire measures are treated i 4. The poit we wat to make is that the trouble with measure ad itegratio i locally compact spaces has little to do with the fact that B ad B 0 are differet, ad a lot to do with the fact that ca be very large...i.e., very o-compact. Ad oe eeds the result i geeral if oe wishes to discuss Haar measure o locally compact groups (eve commutative oes), or weights o C -algebras (especially commutative oes). I these otes I have take the approach that I have come to prefer, i which measure meas Rado measure. I have attempted to cast light o the pitfalls that

3 MEASURE AND INTEGRATION 3 ca occur, to avoid verbosity i the mathematics, ad especially I have tried to avoid the pitfalls I have stumbled through i the past. A Rado measure is a positive Borel measure µ : B [0, + ] which is fiite o compact sets ad is ier regular i the sese that for every Borel set E we have µ(e) = sup{µ(k) : K E, K K} K deotig the family of all compact sets. There is a correspodig otio of outer regularity: a Borel measure µ is outer regular o a family F of Borel sets if for every E F we have µ(e) = if{µ(o) : O E, O O}, O deotig the family of all ope sets. The followig result implies that whe is compact (or eve σ-compact) oe has the best of it, i that ier ad outer regularity are equivalet properties. A set is called bouded if it is cotaied i some compact set, ad σ-bouded if it is cotaied i a coutable uio of compact sets. Every σ-bouded Borel set ca obviously be writte as a coutable uio of bouded Borel sets. Propositio 1.1. Let µ be a Borel measure which is fiite o compact sets. The the followig are equivalet. (1) µ is outer regular o σ-bouded sets. (2) µ is ier regular o σ-bouded sets. proof. (1) = (2) Suppose first that E is a bouded Borel set, say E L where L is compact, ad fix ɛ > 0. We have to show that there is a compact set K E with µ(k) µ(e) ɛ. But sice the relative complemet L \ E is bouded, we see by outer regularity that there is a ope set O L \ E such that µ(o) µ(l \ E) + ɛ. It follows that K = L \ O = L O c is a compact subset of E satisfyig µ(k) = µ(l) µ(l O) µ(l) µ(o) µ(l) µ(l \ E) ɛ = µ(e) ɛ, as required. More geerally, suppose that E = E 1 E 2... is a coutable uio of bouded Borel sets E. Without loss of geerality, we may assume that the sets E are disjoit. If some E has ifiite measure, the by the precedig paragraph we have sup{µ(k) : K E, K K} = µ(e ) = +. Hece sup{µ(k) : K E, K K} = µ(e) = +,

4 4 WILLIAM ARVESON ad we are doe. If, o the other had, µ(e ) < for every, the fixig ɛ > 0 we may fid a sequece of compact sets K E with µ(e ) µ(k ) + ɛ/2. Puttig L = K 1 K 2 K, it is clear that L is a compact subset of E for which µ(l ) = µ(k k ) (µ(e k ) ɛ/2 k ) µ(e k ) ɛ. Takig the supremum over all we obtai sup µ(l ) µ(e) ɛ, from which ier regularity follows. (2) = (1) Suppose first that E is a bouded Borel set. The the closure E of E is compact, ad a simple coverig argumet implies that there is a bouded ope set U such that E U. Set L = U, ad fix ɛ > 0. The L \ E is a bouded Borel set, hece by ier regularity there is a compact set K L \ E with µ(k) µ(l \ E) ɛ. Put V = U \ K = U K c. V is a bouded ope set which cotais E, ad we have µ(v ) = µ(u K c ) µ(l K c ) = µ(l) µ(k) µ(l) (µ(l \ E) ɛ) = µ(e) ɛ. Sice ɛ is arbitrary, this shows that µ is outer regular o bouded sets. I geeral, suppose E = E, where each E is a bouded Borel set. Agai, we may assume that the sets E are mutually disjoit. Sice the assertio 1.1 (1) is trivial whe µ(e) = + we may assume µ(e) < +, ad hece µ(e ) < + for every. Fix ɛ > 0. By the precedig paragraph we may fid a sequece of ope sets O E such that µ(o ) µ(e ) + ɛ/2. The E is cotaied i the uio O = O, ad we have µ(o) µ(o ) µ(e ) + ɛ = µ(e) + ɛ as required. We emphasize that ier ad outer regularity are ot equivalet properties whe fails to be σ-compact. I order to discuss this pheomeo, we cosider the family R of all σ-bouded Borel sets. Notice that R cotais if ad oly if is σ-compact; ad i that case we have R = B. But if is ot σ-compact the R is ot a σ-algebra but merely a σ-rig of subsets of. More explicitly, a σ-rig is a ovoid family S of subsets of satisfyig the coditios E, F S = E \ F S E 1, E 2, S = E S.

5 MEASURE AND INTEGRATION 5 There is a theory of measures defied o σ-rigs that is parallel to ad geeralizes the theory of measures defied o σ-algebras. The σ-rig approach to Baire measures was emphasized ad popularized by Paul Halmos [1], who co-iveted the ame itself. Remarks. We are ow able to make some cocrete observatios about the degree of arbitrariess that accompaies measure theory i humogous spaces. Assume is ot σ-compact, let R be the σ-rig of all σ-bouded Borel sets ad let R deote the set of all complemets of sets i R, R = {E c : E R}. The R R = ad R R is the σ-algebra geerated by R. This is a σ-algebra of Borel sets, but it is ot B sice it does ot ecessarily cotai ope sets or closed sets. I ay evet, we have a coveiet partitio of this σ-algebra ito Borel sets which are either σ-bouded or co-σ-bouded. It is atural to ask if a reasoable measure that is iitially defied o R ca be exteded uiquely to a measure o the σ-algebra geerated by R. The aswer is o. Ideed [2, Exercise 9, pp ] shows that a measure o R always has a extesio but that extesios are ot uique. Actually, there is a oe-parameter family of extesios of ay reasoable measure o R. There is a smallest oe (the ier regular extesio) ad a largest oe (the outer regular extesio), ad there is a arbitrary positive costat ivolved with each of the others. A bad apple. Big locally compact spaces ca be pathological i subtle ways. For example, let S be a ucoutable discrete space, let R be the Euclidea real lie, ad let = S R. is a locally compact Hausdorff space, beig the cartesia product of two such. For every subset E ad every s S, let E s R be the sectio of E defied by E s = {x R : (s, x) E}. It is easy to show that that E is ope iff every sectio E s is a ope set i R. Similarly, E is compact iff all but a fiite umber of sectios of E are empty ad all the remaiig sectios are compact subsets of R. It would seem reasoable to guess that if a set E is locally Borel i the sese that its itersectio with every compact set is a Borel set, the it must be a Borel set (may of us have bee so fooled: see [2, Lemma 9, p. 334]). That guess is wrog, as the followig example shows. Sice a complete discussio of the example would require more iformatio about the Baire hierarchy tha we have at had, we merely give eough details for a persistet reader to complete the argumet. We may as well take S to be the set of all coutable ordials. It follows from the above remarks that E K is a Borel set for every compact set K iff every sectio of E is a Borel set i R. Here is a example of a o-borel set E havig the latter property. For every coutable ordial ω S, let E ω be a Borel set i R which belogs to the ω th Baire class but to o properly smaller Baire class. Defie E = {(ω, x) S R : x E ω }.

6 6 WILLIAM ARVESON Clearly every sectio of E is a Borel set. To see that E is ot a Borel set, suppose that it did belog to the σ-algebra B geerated by the family O of all ope sets of. The E would have to belog to some Baire class over O, say to the ω0 th Baire class. It is easy to see that this implies that every sectio E λ must belog to the ω0 th Baire class i the real lie, cotradictig our costructio of the sectios of E. Fially, we poit out that fiite Rado measures behave as well as possible, eve whe the uderlyig space is huge: Propositio 1.2. Every fiite Rado measure is both ier ad outer regular. sketch of proof. Outer regularity of the measure o ay Borel set follows from the ier regularity of the measure o the complemet of the set, because the measure of ay set is fiite. 2. How to costruct Rado measures I this sectio we show how Rado measures ca be costructed from certai simpler etities defied o the family O of all ope subsets of. Let m : O [0, + ] be a fuctio havig the followig properties (A) (B) (C) U K = m(u) < + U V = m(u) m(v ) U 1, U 2, U = m( U ) m(u ), (D) (E) U V = = m(u V ) = m(u) + m(v ) m(u) = sup{m(v ) : V O, V U, V K}. Notice that A ad D together imply that m( ) = 0. If we start with a Rado measure µ o B ad defie m to be the restrictio of µ to O, the such a m obviously has properties A through D, ad a simple argumet establishes E as well. Coversely, we have Propositio 2.1. Ay fuctio m defied o the ope sets which has properties A through E ca be exteded uiquely to a Rado measure defied o all Borel sets. proof. For uiqueess, let µ be a Rado measure which agrees with m o O. Sice Rado measures are obviously determied by their values o compact sets, it suffices to observe that for every compact set K, we have µ(k) = if{m(u) : U K, U O}. Ideed, µ is ier regular by defiitio of Rado measures, ad every compact set is obviously a bouded Borel set. Thus the assertio is a immediate cosequece of the equivalece of coditios (1) ad (2) i propositio 1.1. Turig ow to existece, we cosider the set fuctio µ defied o arbitrary subsets A by µ (A) = if{m(u) : U A, U O}.

7 We claim first that µ is a outer measure, that is MEASURE AND INTEGRATION 7 µ ( ) = 0 A B = µ (A) µ (B) A 1, A 2, = µ ( A ) µ (A ). The first two properties are obvious. To prove the third, it is clear that we eed oly cosider the case i which µ (A ) is fiite for every = 1, 2,.... I that case, fix ɛ > 0 ad choose ope sets U A with the property that µ (U ) µ (A ) + ɛ/2 for every. above, The U is a ope set cotaiig A, hece by property C µ ( A ) m( U ) m(u ) µ (A ) + ɛ, ad the claim follows from the fact that ɛ is arbitrary. It is apparet from the defiitio of µ that µ (U) = m(u) if U is a ope set. We claim ext that every ope set is measurable; that is, for each ope set O we have (2.2) µ (A) = µ (A O) + µ (A O c ), for every subset A. To prove 2.2 it suffices to prove the iequality, sice the opposite oe follows from the subadditivity of µ. For that, fix A ad O. If µ (A) = + the there is othig to prove, so we may assume that µ (A) (ad hece both µ (A O) ad µ (A O c )) is fiite. Fix ɛ > 0 ad choose a ope set U A so that m(u) µ (A) + ɛ. We will prove that (2.3) m(u) m(u O) + µ (U O c ). Note that it suffices to prove 2.3, sice µ (A O) + µ (A O c ) m(u O) + µ (U O c ) m(u) µ (A) + ɛ, ad ɛ is arbitrary. I order to prove 2.3, we use property E above to fid a ope set V whose closure V is a compact subset of U O ad m(u O) m(v ) + ɛ. Notice that V ad U V c are disjoit ope sets which are both cotaied i U. Thus we have by properties B ad D m(v ) + m(u V c ) = m(v (U V c )) m(u).

8 8 WILLIAM ARVESON Fially, sice U O c U V c, m(u O) + µ (U O c ) m(v ) + ɛ + µ (U O c ) m(v ) + m(u V c ) + ɛ m(u) + ɛ, ad 2.3 follows because ɛ is arbitrary. By Carathéodory s extesio theorem [2, Chapter 12, 2] the restrictio of µ to the σ-algebra of µ -measurable sets is a measure; hece the restrictio of µ to the σ-algebra of Borel sets is a measure µ satisfyig µ(o) = m(o) for every ope set O. Notice that µ is fiite o bouded sets by property A, ad µ is outer regular by the defiitio of µ. So if is σ-compact, the µ is already a Rado measure by propositio 1.1. If is ot σ-compact the µ is ot ecessarily a Rado measure, ad we must modify it as follows. Let M deote the σ-rig of all σ-bouded Borel sets. We defie a ew set fuctio µ o Borel sets as follows: µ(e) = sup{µ(b) : B M, B E}. It is a fact that µ is coutably additive. Gratig that for a momet, otice that µ is a Rado measure. Ideed, Propositio 1.1 implies that µ(b) = µ(b) for every B M, that µ is ier regular o M, ad thus by its defiitio µ must be ier regular o all Borel sets. We also have µ(o) = m(o) for every ope set O. To see that, fix O ad choose a ope set V whose closure is a compact subset of O. The we have m(o) = µ(o) µ(o) µ(v ). Sice V is a σ-bouded ope set we have µ(v ) = µ(v ) = m(v ), ad hece m(o) µ(o) m(v ) for all such V. After takig the sup over V ad usig property (E) above, we fid that m(o) = µ(o). We may coclude i this case that µ is a Rado measure which agrees with m o ope sets. It remais to check that µ is coutably additive. For that, let E 1, E 2,... be a sequece of mutually disjoit Borel sets, ad let B be a σ-bouded subset of E. Set B = B E. The B 1, B 2,... are mutually disjoit σ-bouded Borel sets, hece µ(b) = µ(b ) µ(e ). =1 =1 By takig the supremum over all such B we obtai µ( E ) µ(e ). To prove the opposite iequality it suffices to cosider the case i which µ(e ) is fiite for every. I this case, for each positive umber ɛ we ca fid a σ-bouded Borel set B E such that =1 µ(b ) µ(e ) ɛ/2.

9 MEASURE AND INTEGRATION 9 By summig o we obtai µ(b) µ(e ) ɛ where B = B. Sice B is a σ-bouded subset of E we fid that µ( E ) µ(b) µ(e ) ɛ, =1 ad the coutable additivity of µ follows because ɛ is arbitrary. Remark. There are other routes to the costructio of Borel measures which begi with a set fuctio m defied o the family K of all compact sets. Such etities m are called cotets, ad are i a sese dual to set fuctios obeyig the properties A E that we have used. The iterested reader is referred to [1, sectios 53 54] ad [2, chapter 13, sectio 3]. 3. Measures ad liear fuctioals Let C c () be the space of all cotiuous fuctios f : R which have compact support i the sese that the set supp(f) = {x : f(x) 0} is compact. supp(f) is called the support of the fuctio f. C c () is a algebra of fuctios, ad is i fact a ideal i the algebra C() of all cotiuous fuctios f : R, i the sese that f C c (), g C() = fg C c (). If is compact the C c () = C(). If is ot compact, the C c () is sup-orm dese i the algebra C 0 () of all cotiuous real fuctios which vaish at. The Riesz-Markov theorem gives a useful ad cocrete descriptio of positive liear fuctioals Λ : C c () R, that is, liear fuctioals Λ which are positive i the sese that f 0 = Λ(f) 0, f C c (). For example, let µ be a Rado measure o B. Sice compact sets have fiite µ-measure, it follows that every fuctio i C c () belogs to L 1 (, B, µ) ad we ca defie Λ : C c () R by (3.1) Λ(f) = f dµ, f C c (). Λ is a positive liear fuctioal o C c (). The followig lemma shows how certai values of the measure µ ca be recovered directly from Λ. Lemma 3.2. Suppose that µ ad Λ are related by 3.1. The for every ope set U we have µ(u) = sup{λ(f) : 0 f 1, f C c (), supp(f) U}. proof. The iequality is clear from the fact that if 0 f 1 ad supp(f) U the χ U (x) f(x) for every x,

10 10 WILLIAM ARVESON ad after itegratig this iequality we obtai µ(u) f dµ = Λ(f). For the opposite iequality, let K be a arbitrary compact subset of U. We may fid a bouded ope set V satisfyig K V V U. By Tietze s extesio theorem, there is a cotiuous fuctio f satisfyig 0 f 1, f = 1 o K, ad f = 0 o the complemet of V. Thus the support of f is cotaied i V U, ad sice χ K f we may itegrate the latter iequality to obtai µ(k) Λ(f) sup{λ(f) : 0 f 1, f C c (), supp(f) U}. The desired iequality follows from ier regularity after takig the sup over K. The followig theorem of Riesz ad Markov asserts that 3.1 gives the the most geeral example of a positive liear fuctioal o C c (). Theorem 3.3 (Riesz-Markov). Let Λ be a positive liear fuctioal o C c (). The there is a uique Rado measure µ such that Λ(f) = f dµ, f C c (). Remarks. I should poit out that i spite of the fact that this formulatio of the Riesz-Markov theorem is the oe I happe to prefer, it is ot the oly reasoable oe. See [1],[2] for others. The coectio betwee liear fuctioals ad Baire measures will be described i sectio 4 below. proof of Theorem 3.3. The uiqueess of µ is a direct cosequece of Lemma 3.2 ad the results of sectio 1. Ideed, Lemma 3.2 implies that the values of µ o ope sets are determied by the liear fuctioal Λ, ad by propositio 1.1 the value of µ o ay compact set K obeys µ(k) = if{µ(u) : U K, U O}. Fially, sice for a arbitrary Borel set E we have µ(e) = sup{µ(k) : K E, K K}, it follows that the µ(e) is uiquely determied by the liear fuctioal Λ. For existece, we defie a umber m(u) [0, + ] for every ope set U by m(u) = sup{λ(f) : f C c (), 0 f 1, supp(f) U}. We will show first that m satisfies the hypostheses A E of propositio 2.1, ad hece defies a Rado measure µ : B [0, + ] by way of µ(u) = m(u), U O. We the show that Λ is truly itegratio agaist this Rado measure µ. We will require the followig result o the existece of partitios of uity.

11 MEASURE AND INTEGRATION 11 Lemma 3.4. Let {O α : α I} be a ope cover of a compact subset K. The there is a fiite set φ 1, φ 2,..., φ of real cotiuous fuctios o ad there is a fiite subset α 1, α 2..., α I satisfyig (i) (ii) (iii) 0 φ k 1 supp(φ k ) O αk φ k = 1 o K. k This is a stadard result whose proof ca be foud i [2, propositio 9.16]. Let us establish the properties A E of sectio 2. For A, let U be a bouded ope set. Sice the closure of U is compact, a simple coverig argumet shows that we may fid aother bouded ope set V which cotais the closure of U. By Tietze s extesio theorem, there is a cotiuous fuctio g : R such that 0 g 1, g = 1 o U g = 0 o the complemet of V. Ay fuctio f C c () satisfyig 0 f 1 ad supp(f) U must also satisfy 0 f g ad hece Λ(f) Λ(g). It follows that m(u) = sup{λ(f) : 0 f 1, f C c ()} Λ(g) < + ad property A follows. Property B is obvious. For property C, choose ope sets U 1, U 2,..., put U = U, ad choose f C c () with 0 f 1 ad such that f is supported i U. By Lemma 3.4, we may fid a iteger ad cotiuous fuctios φ 1, φ 2,..., φ takig values i the uit iterval, such that supp(φ k ) U k, φ 1 + φ φ = 1 It follows that f = k φ kf, ad hece Λ(f) = Λ(φ k f) ad o supp(f). m(u k ) m(u k ). Property C follows by takig the supremum over all f i the precedig lie. To establish D we prove oly the iequality, sice the opposite oe is a cosequece of C. Let U ad V be disjoit ope sets ad let f ad g be two fuctios i C c () satisfyig 0 f, g 1, supp(f) U, ad supp(g) V. Sice U V = we have 0 f + g 1 ad supp(f + g) U V. Hece Λ(f) + Λ(g) = Λ(f + g) m(u V ), ad the iequality m(u) + m(v ) m(u V ) follows after takig the supremum over f ad g.

12 12 WILLIAM ARVESON For property E, choose f C c () satisfig 0 f 1 ad supp(f) U. Aother simple coverig argumet o the compact subset supp(f) U shows that we ca fid a ope set V havig compact closure such that Sice supp(f) V we have supp(f) V V U. Λ(f) m(v ) sup{m(v ) : V U, V K} ad ow property E follows after takig the sup over f. Usig propositio 2.1 we may coclude that there is a Rado measure µ o B which agrees with m o ope sets. It remais to show that (3.4) Λ(f) = f dµ for every f C c (). Sice both sides of 3.4 are liear i f ad sice C c () is spaed by its oegative fuctios, it suffices to establish 3.4 for the case where 0 f 1. To this ed, fix f ad choose a positive umber ɛ. We will exhibit a pair of simple Borel fuctios u, v havig the followig properties. (3.5) (3.6) (3.7) u f v (v u) dµ ɛ u dµ Λ(f) v dµ + ɛ Note that this will complete the proof. Ideed, we may itegrate 3.5 to obtai u dµ f dµ v dµ ad sice the itegrals of u ad v are withi ɛ of each other by 3.6, we see from 3.7 ad the precedig iequality that Λ(f) f dµ 2ɛ. The result follows sice ɛ is arbitrary. I order to costruct u ad v, fix a positive iteger ad defie a sequece of bouded ope sets O 0 O 1 O by O k = {x : f(x) > k/}, for k = 1, 2,...,, ad let O 0 be ay bouded ope set which cotais supp(f). Notice that O is empty ad that the closure of O k is cotaied i O k 1 for k = 1, 2,...,. Defie u ad v as follows u = 1 v = 1 c k c k 1,

13 MEASURE AND INTEGRATION 13 where c k deotes the characteristic fuctio of O k. We have c = 0 because O is empty, ad it apparet that 0 u v. We will show that if is sufficietly large the the coditios are satisfied. For 3.5, otice that f, u, v all vaish outside O 0, ad that if x O k 1 \ O k for k = 1, 2,..., the u(x) = 1 k 1 1 = k 1 i=1 < f(x) k = 1 k 1 1 = v(x), i=0 which proves 3.5. For 3.6 we have v u = 1 c 0; hece (v u) dµ = 1 µ(o 0) which is smaller tha ɛ provided is sufficietly large. To prove 3.7 we employ a device from [1, 56, p. 246]. Defie a sequece φ 1, φ 2,..., φ of cotiuous fuctios by φ k = [(f k 1 ) 0] 1 = [(f k 1 ) 1 ] 0. Each φ k vaishes o the complemet of O 0, hece φ k C c (). Moreover, we have 0 if x / O k 1 (3.8) φ k (x) = f(x) k 1 if x O k 1 \ O k 1 if x O k. Clearly, 0 φ k 1. Notig that for x O k 1 \ O k, φ 1 (x) = φ 2 (x) = = φ k 1 (x) = 1 φ k (x) = f(x) k 1 φ k+1 (x) = φ k+2 (x) = = φ (x) = 0 it follows that f = φ 1 + φ φ. For coveiece, we defie O 1 = O 0. We claim that for every k = 1, 2,..., we have the iequalities (3.9) 1 µ(o k) Λ(φ k ) 1 µ(o k 2), To prove the first iequality, choose ay fuctio g C c () satisfyig 0 g 1 ad supp(g) O k. Sice φ k (x) = 1 for every x O k we have 0 g φ k, ad hece Λ(g) Λ(φ k ).

14 14 WILLIAM ARVESON Takig the supremum over all such g gives µ(o k ) Λ(φ k ) = Λ(φ k ), ad the iequality follows after divisio by. For the secod iequality, otice that for k 2, the closed support of φ k is cotaied i O k 1 O k 2. So by defiitio of µ(o k 2 ) = m(o k 2 ) we have Λ(φ k ) = Λ(φ k ) µ(o k 2 ) ad we obtai the secod iequality after dividig by. The case k = 1 follows similarly from the fact that the closed support of φ 1 is cotaied i O 0. Notig that u dµ = 1 v dµ = 1 µ(o k ) = 1 1 µ(o k ), µ(o k 1 ) = 1 1 µ(o k ) we may sum the iequalities 3.9 from k = 1 to ad use Λ(f) = k Λ(φ k) to obtai u dµ Λ(f) v dµ + 1 µ(o 0). k=0 ad Sice the last term o the right is less tha ɛ whe is large, 3.7 follows. 4. Baire meets Borel We have already poited out that the Borel σ-algebra B is atural because it is the σ-algebra geerated by the topology of. The followig result implies that the Baire σ algebra B 0 has just as strog a claim to ievitability. Propositio 4.1. B 0 is the smallest σ-algebra with respect to which the fuctios i C c () are measurable. proof. We show first that every fuctio f i C c () is B 0 -measurable. Sice the B 0 -measurable fuctios are a vector space ad sice every fuctio i C c () is a differece of oegatives oes, we may assume that f 0. Fix t R ad cosider the set F t = {x : f(x) t}. If t 0 the F t = belogs to B 0. If t > 0 the F t = =2{x : f(x) > t t/} is exhibited as a compact G δ, hece F t B 0. Coversely, if A is ay σ-algebra with the property that every fuctio i C c () is A-measurable, the we claim that A cotais every compact G δ, ad hece A cotais B 0. For that, let K be a compact set havig the form K = U 1 U 2...

15 MEASURE AND INTEGRATION 15 where the sets U are ope. By replacig each U with a smaller ope set if ecessary, we ca assume that each U is bouded, ad hece has compact closure. By Tietze s extesio theorem there are cotiuous fuctios f : [0, 1] with the properties f = 1 o K ad f = 0 o the complemet of U. f belogs to C c () ad is therefore A-measurable. Fially, sice for each x we have { 1 if x K lim f (x) = 0 if x / K, it follows that the characteristic fuctio of K, beig a poitwise limit of a sequece of A-measurable fuctios, is A-measurable. Hece K A. Remark. Sice the closure of C c () i the sup orm f = sup{ f(x) : x } is the algebra C 0 () of all cotiuous real-valued fuctios which vaish at, we see that B 0 could also have bee defied as the smallest σ-algebra with respect to which the fuctios i C 0 () are measurable. Perhaps the most compellig feature of Baire measures is that regularity comes for free o σ-bouded sets. I order to discuss this result it will be coveiet to itroduce some otatio. K 0 will deote the family of all compact G δ s, ad O 0 will deote the family of all ope Baire sets. A Baire measure µ : B 0 [0, + ] is called ier regular o a family F of Baire sets if for every F F we have µ(f ) = sup{µ(k) : K F, K K 0 }. Similarly, µ is outer regular o F if for every F F we have µ(f ) = if{µ(o) : O F, O O 0 }. 4.2 Separatio properties of K 0 ad O 0. There are eough ope Baire sets to form a base for the topology o ; more geerally, give ay compact set K ad a ope set U cotaiig K, there exist sets K 0 K 0 ad U 0 O 0 such that K K 0 U 0 U, see [1, Theorem D, 50]. Ideed, by replacig U with a smaller ope set havig compact closure, if ecessary, we see that oe ca eve choose U 0 to be a bouded ope Baire set. That is about all oe ca say about ope Baire sets i geeral. O the other had, the oly compact Baire sets are the obvious oes, amely the compact G δ s. The proof of the latter is ot so easy, ad we shall ot require the result the sequel. The reader is referred to [1, Theorem A, 51] for a proof. Propositio 4.3. Let µ be a Baire measure which is fiite o compact G δ s. The µ is both ier regular ad outer regular o σ-bouded Baire sets. proof. We will show that for every σ-bouded Baire set E oe has both properties (4.3.A) (4.3.B) µ(e) = sup{µ(k) : K E, K K 0 }, µ(e) = if{µ(u) : U E, U O 0 }.

16 16 WILLIAM ARVESON To this ed, we claim that for every Baire set E ad every K K 0, the itersectio K E satisfies both 4.3.A ad 4.3.B. Ideed, let A deote the family of all Baire sets E for which this assertio is true. It suffices to show that A cotais K 0 ad is a σ-algebra. To see that A cotais K 0, choose E K 0. The assertio 4.3.A is trivial because E itself belogs to K 0. I order to prove 4.3.B, let K K 0. Sice K E is a compact G δ we may fid bouded ope Baire sets U such that K E = U 1 U By replacig U with U 1 U 2 U we ca assume that U 1 U For each we have µ(u ) < because U is bouded, ad thus 4.3.B follows from upper cotiuity of µ: µ(k E) = lim µ(u ). I order to show that A is a σ-algebra we have to show that A is closed uder complemetatio ad coutable uios. We show first that A is closed uder complemetatio. Choose E A, fix K K 0 ad ɛ > 0. Sice E A there are sets L K 0 ad U, V O 0 such that L K E U, K V ad such that both µ(u \L) ad µ(v \K) are smaller tha ɛ. Usig the remarks 4.2 we may assume that both U ad V are bouded, by replacig them with smaller oes if ecessary. Defie sets A, B by A = K U c, B = V L c. A belogs to K 0, B belogs to O 0, ad we have A K E B. Both A ad B are bouded sets, ad therefore have fiite measure. Moreover, sice µ(b) = µ(v \ L) = µ(v \ K) + µ(k \ L) ad sice we have µ(a) = µ(k \ U) = µ(k) µ(k U) µ(k) µ(u) µ(b) µ(a) µ(v \K)+µ(K \L) µ(k)+µ(u) = µ(v \K)+(µ(U) µ(l)) 2ɛ. Sice ɛ is arbitrary, it follows that E c belogs to A. We claim ow that A is closed uder coutable uios. Choose E 1, E 2, A, fix K K 0 ad ɛ > 0. Because 4.3.A ad 4.3.B are valid for K E for every, we may fid K K 0, ad bouded sets U O 0 such that K K E U

17 ad for which MEASURE AND INTEGRATION 17 µ(u ) µ(k ) ɛ/2. Put U = U, L = K 1 K 2 K. The L K 0, U O 0 ad we have L K E U. Moreover, µ(u) µ( L ) (µ(u ) µ(k )) ɛ. =1 Sice the sets L icrease to L as ad sice L K E has fiite measure, it follows that µ(l ) µ( L ). Hece the differece µ(u) µ(l ) is smaller tha 2ɛ whe is sufficietly large. Sice ɛ is arbitrary, we coclude that E A. Thus, A cotais all Baire sets. Now let E be ay σ-bouded Baire set, say E K where K is compact. The by the remarks 4.2 we may assume that K K 0 by slightly elargig each K. Hece E is itself a coutable uio of sets E = (K E) each of which is, by what has already bee proved, a Baire set of fiite measure which is both ier ad outer regular. It is easy to see that this implies E is both ier ad outer regular. Ideed, choose ɛ > 0. For each we fid L K 0 ad a bouded set U O 0 such that ad for which L K E U µ(u ) µ(k ) ɛ/2. Puttig L = L, ad U = U we have L E U ad by estimatig as we have doe above we also have µ(u \ L) ɛ. If µ(e) is ifiite the so is µ(u) ad the precedig iequality implies µ(l) = +. By lower cotiuity of µ, lim µ(l 1 L 2 L ) = µ(l) = +. Sice L 1 L 2 L is a compact G δ subset of E, this establishes ier ad outer regularity at E i this case. If µ(e) < +, the the precedig iequality implies that both µ(l) ad µ(u) are withi ɛ of µ(e). Sice µ(l) = lim µ(l 1 L 2 L ) ad sice L 1 L 2 L is a K 0 -subset of E for each, we coclude that E is both ier ad outer regular i this case as well.

18 18 WILLIAM ARVESON Corollary. Every fiite Baire measure o a compact Hausdorff space is both ier regular ad outer regular. Fially, it is sigificat that the correspodece betwee Rado measures (defied o B) ad Baire measures (defied o B 0 ) is bijective. However, eve here oe must be careful i the formulatio if is ot σ-compact. More precisely, if oe restricts a Rado measure µ to B 0 the oe obtais a Baire measure which is fiite o compact sets. However, the ier regularity of Rado measures o B does ot immediately imply that their restrictios to B 0 are ier regular Baire measures as defied i the paragraphs precedig Propositio 4.3. The problem is that a give Baire set may have may more compact subsets tha it has compact G δ subsets. Nevertheless, Propositio 4.3 implies that the restrictio of a Rado measure o B to the σ-rig R 0 of σ-bouded Baire sets is ier regular. If is σ-compact, the this restrictio is already a regular Baire measure i both the ier ad outer seses. But if is ot σ-compact the i order to obtai a ier regular Baire measure we must first restrict the Rado measure to the σ-rig R 0 ad the use the latter to defie a ier regular Baire measure o the full Baire σ-algebra much as we did i the proof of Propositio 2.1. After these sheaigas oe ca say that every Rado measure restricts to a ier regular Baire measure i geeral. The followig asserts that this map is a bijectio. Propositio 4.4. Let µ be a ier regular Baire measure which is fiite o compact Baire sets. The µ exteds uiquely to a Rado measure o B. proof. For the existece of a Rado extesio of µ we ote that sice fuctios i C c () are Baire measurable ad µ-itegerable, we may defie a positive liear fuctioal Λ o C c () by Λ(f) = f dµ. By Theorem 3.3 there is a Rado measure ν such that f dν = Λ(f) = f dµ, f C c (). I order to show that µ is the restrictio of ν as described i the precedig discussio, let K be ay compact G δ. Notice that there is a sequece of fuctios f C c () such that { 1 if x K f (x) 0 if x / K. Ideed, we ca write K = U where each U is a ope set havig compact closure. Choosig a cotiuous fuctio g takig values i [0, 1] such that g = 1 o K ad g = 0 o the complemet of U, we may take f = g 1 g 2 g. By the mootoe covergece theorem we have ν(k) = lim f dν = lim f dµ = µ(k), ad the desired coclusio follows from the ier regularity of µ o B 0.

19 MEASURE AND INTEGRATION 19 For uiqueess, suppose that ν 1 ad ν 2 are two Rado measures which exted µ i the sese described above. Notice that for every f C c () satisfyig f 0, the value of the itegral f dν 1 is etirely determied by the values of the fuctio F (t) = ν 1 ({x : f(x) t}) for t > 0; i.e., by the values of ν 1 K0 = µ K0. The same applies to ν 2, hece f dν 1 = f dµ = f dν 2. Hece ν 1 = ν 2 by the uiqueess assertio of 3.3. Corollary. If is compact, the every fiite Baire measure exteds uiquely to a measure o B which is both ier ad outer regular. There are two possible reformulatios of the Riesz-Markov theorem i terms of Baire measures; the proofs follow from the precedig discussio. Theorem 4.5. For every positive liear fuctioal Λ defied o C c () there is a uique measure µ defied o the σ-rig of all σ-bouded Baire sets (resp. a uique ier regular Baire measure µ) such that Λ(f) = f dµ, f C c (). 5. The dual of C 0 () We ow discuss oe of the useful cosequeces of the Riesz-Markov theorem. C 0 () will deote the space of all real-valued cotiuous fuctios f o which vaish at i the sese that for every ɛ > 0 the set {x : f(x) ɛ} is compact. C 0 () is a real algebra i that it is closed uder the usual liear operatios ad poitwise multiplicatio. The orm f = sup f(x) x makes C 0 () ito a Baach space i which fg f g. We will make essetial use of the atural order o elemets of C 0 (), defied by f g f(x) g(x) for every x. This orderig makes C 0 () ito a lattice, ad the lattice operatios ca be defied poitwise by f g (x) = max(f(x), g(x)), f g (x) = mi(f(x), g(x)), x. Fially, P will deote the coe of all positive fuctios, P = {f C 0 () : f 0}. Notice that P P = {0} ad P P = C 0 (). There is a atural orderig iduced o the dual C 0 () by the orderig o C 0 (), amely ρ σ ρ(f) σ(f), for every f P, ad a liear fuctioal ρ is called positive if ρ 0.

20 20 WILLIAM ARVESON Propositio 5.1. Every positive liear fuctioal o C 0 () is bouded. Moreover, for every ρ C 0 () there is a smallest positive liear fuctioal Λ such that ρ Λ. Remarks. The secod assertio meas that if Λ is aother positive liear fuctioal satisfyig ρ Λ, the Λ Λ. After a simple argumet (which we omit), this implies that the dual of C 0 () is itself a lattice with respect to the orderig defied above; ad of course Λ = ρ 0. proof. To establish the first assertio let Λ be a positive liear fuctioal. Sice every elemet i the uit ball of C 0 () is a differece of positive fuctios i the uit ball of C 0 (), to show that Λ is bouded it suffices to show that sup{λ(f) : f P, f 1} < +. But if this supremum is ifiite the we ca fid a sequece f P satisfyig f 1 ad Λ(f ) > 2. Lettig g P be the fuctio defied by the absolutely coverget series g = 2 k f k, we have g 2 k f k for every, hece Λ(g) Λ( 2 k f k ) = 2 k Λ(f k ) >. The latter is absurd for large. To prove the secod assertio choose ρ C 0 (). For every f P we defie a oegative umber Λ 0 (f) by Λ 0 (f) = sup{ρ(u) : 0 u f}. Clearly Λ 0 (αf) = αλ 0 (f) for every oegative scalar α ad every f P. We claim that for all f, g P, Λ 0 (f + g) = Λ 0 (f) + Λ 0 (g). The iequality follows from the fact that if 0 u f ad 0 v g the 0 u + v f + g, hece Λ 0 (f + g) ρ(u + v) = ρ(u) + ρ(v). follows after takig the sup over u ad v. For the opposite iequality, fix f, g P ad choose u satisfyig 0 u f + g. Now for each x, f u (x) is either f(x) or u(x), ad i either case u(x) (f u) (x) + g(x). It follows that 0 u f u g. Therefore ρ(u) ρ(f u) = ρ(u f u) Λ 0 (g),

21 ad hece MEASURE AND INTEGRATION 21 ρ(u) ρ(f u) + Λ 0 (g) Λ 0 (f) + Λ 0 (g). The iequality ow follows after takig the supremum over u. Fially, we claim that Λ 0 exteds uiquely to a liear fuctioal Λ defied o all of C 0 (). Give that, the remaiig assertios of 5.1 follow; for if Λ is aother positive liear fuctioal satisfyig ρ Λ, the for every f P ad every 0 u f we have ρ(u) Λ (u) Λ (f), ad Λ(f) Λ (f) follows from the precedig iequality after takig the sup over u. I order to exted Λ 0, choose a arbitrary f C 0 (), write f = f 1 f 2 i ay way as the differece of two elemets f k P, ad defie Λ(f) = Λ 0 (f 1 ) Λ 0 (f 2 ). The oly questio is whether or ot this is well-defied; but that is clear from the fact that if f k, g k P ad f 1 f 2 = g 1 g 2 the Λ 0 (f 1 ) + Λ 0 (g 2 ) = Λ 0 (f 1 + g 2 ) = Λ 0 (g 1 + f 2 ) = Λ 0 (g 1 ) + Λ 0 (f 2 ), hece Λ 0 (f 1 ) Λ 0 (f 2 ) = Λ 0 (g 1 ) Λ 0 (g 2 ). The liearity of the exteded Λ follows from the restricted liearity of the origial Λ 0 o the positive coe. Uiqueess of the extesio is obvious from the fact that P P = C 0 () Remarks. Let Λ be a positive liear fuctioal o C 0 (), ad let Λ be its orm. By the Riesz-Markov theorem there is a positive Rado measure µ such that Λ(f) = f dµ, f C c (). Notice that if f C c () satisfies 0 f 1, the f dµ Λ(f) Λ. From this, we may deduce that for every compact set K we have µ(k) Λ, ad fially by ier regularity µ() Λ < +. I particular, µ is a fiite measure, ad hece is both outer ad ier regular (Propositio 1.2). It is easy to see that i fact µ() = Λ. More geerally, we may deduce the followig descriptio of the dual of C 0 (); this result (together with myriad other results which bear it o relatio whatsoever) is (are) ofte called the Riesz Represetatio Theorem. Theorem 5.2. For every bouded liear fuctioal ρ o C 0 () there is a fiite siged Borel measure µ such that ρ(f) = f dµ, f C 0 (). proof. Note first that ρ ca be decomposed ito a differece Λ 1 Λ 2 of positive liear fuctioals. Ideed, usig 5.1 we may defie Λ 1 = ρ 0, ad it is clear from

22 22 WILLIAM ARVESON the statemet of 5.1 that the liear fuctioal Λ 2 = Λ 1 ρ is positive. Thus the existece of µ follows from the Riesz-Markov theorem ad 5.1 above. Remarks. If oe stipulates that the total variatio measure µ of µ should be a Rado measure, the µ is uique, ad moreover µ is outer regular as well as ier regular because it is fiite. We omit the argumet [2, Chapter 13, 5]. If we agree to defie a siged Rado measure as a fiite siged measure µ whose variatio µ = µ + + µ is ier regular (ad therefore also outer regular), the the vector space M() of all siged Rado measures becomes a Baach space with orm µ = sup µ(e k ) k the supremum beig exteded over all fiite families E 1, E 2,..., E of mutually disjoit Borel sets. Such a measure gives rise to a liear fuctioal ρ C 0 () as i 5.2, ad it is ot hard to show that ρ = µ, [2, Chapter 13, 5]. Thus the dual of C 0 () is aturally isometrically isomorphic to the Baach space M() i such a way that the order structure o the dual of C 0 () correspods to the atural orderig of siged measures. Refereces 1. Halmos, P. R., Measure Theory, Va Nostrad, Priceto, Royde, H. L., Real Aalysis, third editio, Macmilla, New York, 1988.

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

MARTINGALES AND A BASIC APPLICATION

MARTINGALES AND A BASIC APPLICATION MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measure-theoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series 8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Notes on exponential generating functions and structures.

Notes on exponential generating functions and structures. Notes o expoetial geeratig fuctios ad structures. 1. The cocept of a structure. Cosider the followig coutig problems: (1) to fid for each the umber of partitios of a -elemet set, (2) to fid for each the

More information

Metric, Normed, and Topological Spaces

Metric, Normed, and Topological Spaces Chapter 13 Metric, Normed, ad Topological Spaces A metric space is a set X that has a otio of the distace d(x, y) betwee every pair of poits x, y X. A fudametal example is R with the absolute-value metric

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

Class Meeting # 16: The Fourier Transform on R n

Class Meeting # 16: The Fourier Transform on R n MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Permutations, the Parity Theorem, and Determinants

Permutations, the Parity Theorem, and Determinants 1 Permutatios, the Parity Theorem, ad Determiats Joh A. Guber Departmet of Electrical ad Computer Egieerig Uiversity of Wiscosi Madiso Cotets 1 What is a Permutatio 1 2 Cycles 2 2.1 Traspositios 4 3 Orbits

More information

Analysis Notes (only a draft, and the first one!)

Analysis Notes (only a draft, and the first one!) Aalysis Notes (oly a draft, ad the first oe!) Ali Nesi Mathematics Departmet Istabul Bilgi Uiversity Kuştepe Şişli Istabul Turkey aesi@bilgi.edu.tr Jue 22, 2004 2 Cotets 1 Prelimiaries 9 1.1 Biary Operatio...........................

More information

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

Irreducible polynomials with consecutive zero coefficients

Irreducible polynomials with consecutive zero coefficients Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem

More information

PART TWO. Measure, Integration, and Differentiation

PART TWO. Measure, Integration, and Differentiation PART TWO Measure, Itegratio, ad Differetiatio Émile Félix-Édouard-Justi Borel (1871 1956 Émile Borel was bor at Sait-Affrique, Frace, o Jauary 7, 1871, the third child of Hooré Borel, a Protestat miister,

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

Lecture 5: Span, linear independence, bases, and dimension

Lecture 5: Span, linear independence, bases, and dimension Lecture 5: Spa, liear idepedece, bases, ad dimesio Travis Schedler Thurs, Sep 23, 2010 (versio: 9/21 9:55 PM) 1 Motivatio Motivatio To uderstad what it meas that R has dimesio oe, R 2 dimesio 2, etc.;

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

4.3. The Integral and Comparison Tests

4.3. The Integral and Comparison Tests 4.3. THE INTEGRAL AND COMPARISON TESTS 9 4.3. The Itegral ad Compariso Tests 4.3.. The Itegral Test. Suppose f is a cotiuous, positive, decreasig fuctio o [, ), ad let a = f(). The the covergece or divergece

More information

Exploratory Data Analysis

Exploratory Data Analysis 1 Exploratory Data Aalysis Exploratory data aalysis is ofte the rst step i a statistical aalysis, for it helps uderstadig the mai features of the particular sample that a aalyst is usig. Itelliget descriptios

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

NOTES ON PROBABILITY Greg Lawler Last Updated: March 21, 2016

NOTES ON PROBABILITY Greg Lawler Last Updated: March 21, 2016 NOTES ON PROBBILITY Greg Lawler Last Updated: March 21, 2016 Overview This is a itroductio to the mathematical foudatios of probability theory. It is iteded as a supplemet or follow-up to a graduate course

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

Overview of some probability distributions.

Overview of some probability distributions. Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

On the L p -conjecture for locally compact groups

On the L p -conjecture for locally compact groups Arch. Math. 89 (2007), 237 242 c 2007 Birkhäuser Verlag Basel/Switzerlad 0003/889X/030237-6, ublished olie 2007-08-0 DOI 0.007/s0003-007-993-x Archiv der Mathematik O the L -cojecture for locally comact

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k.

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k. 18.409 A Algorithmist s Toolkit September 17, 009 Lecture 3 Lecturer: Joatha Keler Scribe: Adre Wibisoo 1 Outlie Today s lecture covers three mai parts: Courat-Fischer formula ad Rayleigh quotiets The

More information

Factors of sums of powers of binomial coefficients

Factors of sums of powers of binomial coefficients ACTA ARITHMETICA LXXXVI.1 (1998) Factors of sums of powers of biomial coefficiets by Neil J. Cali (Clemso, S.C.) Dedicated to the memory of Paul Erdős 1. Itroductio. It is well ow that if ( ) a f,a = the

More information

THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E. MCCARTHY, SANDRA POTT, AND BRETT D. WICK

THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E. MCCARTHY, SANDRA POTT, AND BRETT D. WICK THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E MCCARTHY, SANDRA POTT, AND BRETT D WICK Abstract We provide a ew proof of Volberg s Theorem characterizig thi iterpolatig sequeces as those for

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

Chapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity

More information

ON THE DENSE TRAJECTORY OF LASOTA EQUATION

ON THE DENSE TRAJECTORY OF LASOTA EQUATION UNIVERSITATIS IAGELLONICAE ACTA MATHEMATICA, FASCICULUS XLIII 2005 ON THE DENSE TRAJECTORY OF LASOTA EQUATION by Atoi Leo Dawidowicz ad Najemedi Haribash Abstract. I preseted paper the dese trajectory

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 6-12 pages of text (ca be loger with appedix) 6-12 figures (please

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

A Recursive Formula for Moments of a Binomial Distribution

A Recursive Formula for Moments of a Binomial Distribution A Recursive Formula for Momets of a Biomial Distributio Árpád Béyi beyi@mathumassedu, Uiversity of Massachusetts, Amherst, MA 01003 ad Saverio M Maago smmaago@psavymil Naval Postgraduate School, Moterey,

More information

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

How To Solve The Homewor Problem Beautifully

How To Solve The Homewor Problem Beautifully Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log

More information

Ekkehart Schlicht: Economic Surplus and Derived Demand

Ekkehart Schlicht: Economic Surplus and Derived Demand Ekkehart Schlicht: Ecoomic Surplus ad Derived Demad Muich Discussio Paper No. 2006-17 Departmet of Ecoomics Uiversity of Muich Volkswirtschaftliche Fakultät Ludwig-Maximilias-Uiversität Müche Olie at http://epub.ub.ui-mueche.de/940/

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if

More information

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required.

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required. S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,

More information

How To Understand The Theory Of Coectedess

How To Understand The Theory Of Coectedess 35 Chapter 1: Fudametal Cocepts Sectio 1.3: Vertex Degrees ad Coutig 36 its eighbor o P. Note that P has at least three vertices. If G x v is coected, let y = v. Otherwise, a compoet cut off from P x v

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

THE HEIGHT OF q-binary SEARCH TREES

THE HEIGHT OF q-binary SEARCH TREES THE HEIGHT OF q-binary SEARCH TREES MICHAEL DRMOTA AND HELMUT PRODINGER Abstract. q biary search trees are obtaied from words, equipped with the geometric distributio istead of permutatios. The average

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

3 Basic Definitions of Probability Theory

3 Basic Definitions of Probability Theory 3 Basic Defiitios of Probability Theory 3defprob.tex: Feb 10, 2003 Classical probability Frequecy probability axiomatic probability Historical developemet: Classical Frequecy Axiomatic The Axiomatic defiitio

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

The Stable Marriage Problem

The Stable Marriage Problem The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,

More information

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas: Chapter 7 - Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries

More information

A RANDOM PERMUTATION MODEL ARISING IN CHEMISTRY

A RANDOM PERMUTATION MODEL ARISING IN CHEMISTRY J. Appl. Prob. 45, 060 070 2008 Prited i Eglad Applied Probability Trust 2008 A RANDOM PERMUTATION MODEL ARISING IN CHEMISTRY MARK BROWN, The City College of New York EROL A. PEKÖZ, Bosto Uiversity SHELDON

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

A Faster Clause-Shortening Algorithm for SAT with No Restriction on Clause Length

A Faster Clause-Shortening Algorithm for SAT with No Restriction on Clause Length Joural o Satisfiability, Boolea Modelig ad Computatio 1 2005) 49-60 A Faster Clause-Shorteig Algorithm for SAT with No Restrictio o Clause Legth Evgey Datsi Alexader Wolpert Departmet of Computer Sciece

More information

ON THE EDGE-BANDWIDTH OF GRAPH PRODUCTS

ON THE EDGE-BANDWIDTH OF GRAPH PRODUCTS ON THE EDGE-BANDWIDTH OF GRAPH PRODUCTS JÓZSEF BALOGH, DHRUV MUBAYI, AND ANDRÁS PLUHÁR Abstract The edge-badwidth of a graph G is the badwidth of the lie graph of G We show asymptotically tight bouds o

More information

Chapter 11 Convergence in Distribution

Chapter 11 Convergence in Distribution Chapter Covergece i Distributio. Weak covergece i metric spaces 2. Weak covergece i R 3. Tightess ad subsequeces 4. Metrizig weak covergece 5. Characterizig weak covergece i spaces of fuctios 2 Chapter

More information

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph.

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph. 4. Trees Oe of the importat classes of graphs is the trees. The importace of trees is evidet from their applicatios i various areas, especially theoretical computer sciece ad molecular evolutio. 4.1 Basics

More information

2 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS

2 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS 2 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS 1. The group rig k[g] The mai idea is that represetatios of a group G over a field k are the same as modules over the group rig k[g]. First I

More information

Part - I. Mathematics

Part - I. Mathematics Part - I Mathematics CHAPTER Set Theory. Objectives. Itroductio. Set Cocept.. Sets ad Elemets. Subset.. Proper ad Improper Subsets.. Equality of Sets.. Trasitivity of Set Iclusio.4 Uiversal Set.5 Complemet

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Ramsey-type theorems with forbidden subgraphs

Ramsey-type theorems with forbidden subgraphs Ramsey-type theorems with forbidde subgraphs Noga Alo Jáos Pach József Solymosi Abstract A graph is called H-free if it cotais o iduced copy of H. We discuss the followig questio raised by Erdős ad Hajal.

More information

A Note on Sums of Greatest (Least) Prime Factors

A Note on Sums of Greatest (Least) Prime Factors It. J. Cotemp. Math. Scieces, Vol. 8, 203, o. 9, 423-432 HIKARI Ltd, www.m-hikari.com A Note o Sums of Greatest (Least Prime Factors Rafael Jakimczuk Divisio Matemática, Uiversidad Nacioal de Luá Bueos

More information

Solutions to Exercises Chapter 4: Recurrence relations and generating functions

Solutions to Exercises Chapter 4: Recurrence relations and generating functions Solutios to Exercises Chapter 4: Recurrece relatios ad geeratig fuctios 1 (a) There are seatig positios arraged i a lie. Prove that the umber of ways of choosig a subset of these positios, with o two chose

More information

EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND 1 OBTAINED WITH ENGEL SERIES

EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND 1 OBTAINED WITH ENGEL SERIES EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND OBTAINED WITH ENGEL SERIES ELVIA NIDIA GONZÁLEZ AND JULIA BERGNER, PHD DEPARTMENT OF MATHEMATICS Abstract. The aciet Egyptias epressed ratioal

More information

Perfect Packing Theorems and the Average-Case Behavior of Optimal and Online Bin Packing

Perfect Packing Theorems and the Average-Case Behavior of Optimal and Online Bin Packing SIAM REVIEW Vol. 44, No. 1, pp. 95 108 c 2002 Society for Idustrial ad Applied Mathematics Perfect Packig Theorems ad the Average-Case Behavior of Optimal ad Olie Bi Packig E. G. Coffma, Jr. C. Courcoubetis

More information

1 The Gaussian channel

1 The Gaussian channel ECE 77 Lecture 0 The Gaussia chael Objective: I this lecture we will lear about commuicatio over a chael of practical iterest, i which the trasmitted sigal is subjected to additive white Gaussia oise.

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive

More information

Entropy of bi-capacities

Entropy of bi-capacities Etropy of bi-capacities Iva Kojadiovic LINA CNRS FRE 2729 Site école polytechique de l uiv. de Nates Rue Christia Pauc 44306 Nates, Frace iva.kojadiovic@uiv-ates.fr Jea-Luc Marichal Applied Mathematics

More information