Solutions to Exercises Chapter 3: Subsets, partitions, permutations

Size: px
Start display at page:

Download "Solutions to Exercises Chapter 3: Subsets, partitions, permutations"

Transcription

1 Solutios to Exercises Chapter 3: Subsets, partitios, permutatios 1 A restaurat ear Vacouver offered Dutch pacaes with a thousad ad oe combiatios of toppigs. What do you coclude? Sice 1001 ( 14 4, it is plausible that there were fourtee differet toppigs, of which ay four could be chose. (I fact, 1001 simply meat a large umber; there were 25 differet toppigs, ay subset beig permitted. 2 Usig the umberig of subsets of {0,1,..., 1} defied i Sectio 3.1, prove that, if X X l, the l (but ot coversely. Let (a 0,...,a 1 ad (b 0,...,b 1 be the characteristic fuctios of X ad X l (the biary digits of ad l respectively. Each of a i ad b i is either zero or oe. If X X l, the it is impossible that a i 1 ad b i 0; hece a i b i for all i, ad so a i 2 i b i 2 i l. The coverse is false: 1 < 2, but X 1 {0} is ot a subset of X 2 {1}. 3 Prove that, for fixed, the greatest biomial coefficiet ( occurs whe /2 or /2. The ratio of successive biomial coefficiets is give by ( ( +1 / ( /( +1. Now, if < ( 1/2, the + > +1, ad this ratio is greater tha 1, so the biomial coefficiets icrease. If is odd, the the ratio is equal to 1 for ( 1/2, so that ( ( ( 1/2 (+1/2 ; after that, the ratio is less tha 1 ad the coefficiets decrease agai. So the two middle coefficiets are the largest. If is eve, the ratio is ever equal to 1, ad so the largest biomial coefficiet is the first for which the ratio is less tha 1, amely ( /2. 1

2 4 Prove the followig idetities: ( ( ( ( l (a. l l l (b (c (d (e ( ( ( m m + i i (recall the covetio that ( 0 if < 0 or >. ( ( + i i i0 i0 1 ( ( 1 ( 2 { 0 if is odd; ( 1 m( 2m m if 2m. (a Let X be a -set. Cout pairs (Y,Z with Z Y X ad Y, Z l. There are ( ( choices for Y, ad the l choices of a l-subset Z of Y. Alterately, there are ( l choices of Z; the we obtai Y by choosig l poits from X \ Z to adjoi to Z, ad sice X \ Z l, this ca be doe i ( l l ways. If < l, the o choices are possible, ad both sides are zero, as a result of our covetios about biomial coefficiets. (b Choose a team of players from a class with m girls ad boys. (c The result ca be proved by iductio, beig clear whe 0. Assumig it for 1, we have i0 ( + i i ( ( ( ad the iductio step is proved. (d Differetiatig the Biomial Theorem gives 1 ( t 1 (1 + t 1 (the term with 0 is costat ad its derivative is zero. Now put t 1. (e Cosider the idetity (1 + t (1 t (1 t 2, ad calculate the coefficiet of t. O the left, we multiply the coefficiet of t i (1 +t (which is ( ( by the coefficiet of t i (1 t (which is ( 1 (, ad sum over 2,

3 . O the right, it is zero if is odd (sice oly eve powers of t appear, ad ( 1 /2( /2 if is eve, as required. 5 Followig the method i the text, calculate the umber of subsets of a -set of size cogruet to m (mod 3 (m 0,1,2 for each value of (mod 6. Let α e 2πi/3 ad β e 4πi/3 be the two cube roots of uity. The 1+α+β 0. We have ( ( , 0 ( α (1 + α ( β, 0 ( β (1 + β ( α. 0 The values of ( β are 1, β,α, 1,β, α accordig as 0,1,2,3,4,5 (mod 6. Also, 1 + α + β 3 if 0 (mod 3, ad 0 otherwise. Addig the three equatios shows that the umber of subsets of size divisible by 3 is equal to (2 +( β +( α /3, which is (2 +2/3, (2 +1/3, (2 1/3, (2 2/3, (2 1/3, or (2 + 1/3, depedig o the cogruece of modulo 6. For sets of size cogruet to 1 mod 3, multiply the secod equatio by α 2 ad the third by β 2 before addig; for 2 mod 3, use the multipliers α ad β istead. 6 Let be a give positive iteger. Show that ay o-egative iteger N ca be writte uiquely i the form ( ( ( x x 1 x1 N , 1 1 where 0 x 1 <... < x 1 < x. [HINT: Let x be such that ( x ( N < x+1. The ay possible represetatio has x x. Now use iductio ad the fact that N ( x ( < x 1 (Fact to show the existece ad uiqueess of the represetatio.] Show that the order of -subsets correspodig i this way to the usual order of the atural umbers is the same as the reverse lexicographic order geerated by the algorithm i Sectio [HINT: i ( j ( j0 i j +1 i.] 3

4 Give N ad, choose x such that ( x ( N < x+1. We must have x x. Moreover, ( ( ( xi x + i x + 1 1, i i i1 i1 (the first iequality holdig sice x i x + i ad the secod by Exercise 3(c; so x < x would be impossible. Thus we must have x x. By iductio o, the iteger N ( x has a uique expressio i the same form with 1 replacig ; ad, as i the hit, Fact shows that x 1 < x. So the result is proved. The last part is proved by iductio; the iductive step ivolves cosiderig the algorithm ( Suppose that, i the expressio for N, we have x i + i for i 1,...,r 1, ad x r > + r. The r 1 ( xi ( i1 i +r r 1, by Exercise 3(c (ote that the term i 0 is missig from the sum. So, if we icrease x r 1 by 1 ad set x i i 1 for i < r 1, the correspodig sum is icreased by 1, ad represets N + 1. So the -set represetig N + 1 is the same as the ext oe produced by (3.12.3, that is, the ext i reverse lexicographic order. 7 Use the fact that (1 +t p 1 +t p (mod p to prove by iductio that p (mod p for all positive itegers. Clearly 1 p 1 (mod p. Assumig that the result is true for, we have (1 + p 1 + p 1 + (mod p, the first cogruece by the give fact ad the secod by the iductive hypothesis. So the iductive step holds. The give fact is proved o page 28. 4

5 8 A computer is to be used to calculate values of biomial coefficiets. The largest iteger which ca be hadled by the computer is Four possible methods are proposed: ( (1!/!(!; ( (2 ( 1...( + 1/!; ( ( ( (3 1, + 1 for > 0; ( 1 + for 0 < < (i.e., Pas- 0 ( ( (4 1, 0 cal s Triagle. 1 ( ( 1 1 For which values of ad ca ( be calculated by each method? What ca you say about the relative speed of the differet methods? Method 1 will fail if! > 32767, that is, if > 7; so it computes ( for 7. Method 2 requires the umerator to be at most 32767; so, for give, it wors for up to some value (, where ( 32767, 181, 33, 15, 10, 8, 7 for 1,...,7. Method 3 requires ( ( , which bouds by a similar fuctio m(; we fid that m( 32767, 181, 41, 22,.... Method 4 reaches each biomial coefficiet from below, so it calculates all those ( which do ot exceed For example, for 2, 3 it wors up to 256, 59 respectively. (I additio, it is the oly method which wors for close to. Of the best two, methods 3 ad 4, we see that 3 is iferior i terms of the rage of values for which it wors. It is faster for computig a sigle biomial coefficiet, requirig oly 1 multiplicatios ad 1 divisios, whereas method 4 eeds roughly additios. But method 4 is preferable if the etire Pascal triagle is required, sice each additio yields a ew piece of data. 9 Show that there are ( 1! cyclic permutatios of a set of poits. A cyclic permutatio, writte i cycle form, cosists of the poits writte i some order iside a bracet. There are! orders. However, the cycle ca start at 5

6 ay poit, ad so orders yield the same cyclic permutatio. Thus, the umber of cyclic permutatios is!/ ( 1!, as required. 10 The order of a permutatio π is the least positive iteger m such that π m is the idetity permutatio. Prove that the order of a cycle o poits is. Prove that the order of a arbitrary permutatio is the least commo multiple of the legths of the cycles i its cycle decompositio. If the poit x lies i a -cycle of π, the o successive applicatios of π, x visits i tur all the poits of the cycle, returig to its startig poit after applicatios. So xπ x ad xπ x for 0 < <. Thus, if π is a sigle -cycle, the π is ot the idetity for 0 < < ; but π maps every poit to itself, that is, π is the idetity. So the order of π is. More geerally, we see that xπ x if ad oly if is a multiple of the legth of the cycle i which x lies. So, for a arbitrary permutatio π, we see that π is the idetity if ad oly if is a multiple of the legth of every cycle of π. So the order of π, beig the least positive for which this holds, is the least commo multiple of the cycle legths. 11 How may words ca be made from the letters of the word ESTATE? There are words with o repeated letters. If E but ot T is repeated, there are words. (If there are 2 further letters, the there are ( 2 positios for the Es, ad (3 2 choices for the positios of the other letters, where ( ( 1 ( + 1. The same umber occur if T but ot E is repeated. If both pairs are repeated, there are words. (Choose the positios for the Es, the those for the Ts, the those for the remaiig letters. I total, 523 words. 12 Give letters, of which m are idetical ad the rest are all distict, fid a formula for the umber of words which ca be made. If we use of the m idetical letters ad l of the others, there are ( +l ( ml possible words. Now sum over ad l. 13 Show that, for 2,3,4,5,6, the umber of ulabelled trees o vertices is 1, 1, 2, 3, 6 respectively. This is doe by drawig the trees: for 4, the two trees are a path with three edges, or three edges radiatig from a cetral vertex. As a chec that othig has bee forgotte, the umber of labelligs of a tree is equal to! divided by the umber of automorphisms (symmetries of the tree. (So, for example, the 4-vertex 6

7 path has two symmetries, the idetity ad reversal, ad the other tree has six, sice the three edges ca be permuted arbitrarily; ad we have 4!/2 + 4!/ , i accordace with Cayley s Theorem. You ca fid drawigs of the trees i N. J. A. Sloae ad S. Plouffe, The Ecyclopedia of Iteger Sequeces, Academic Press, 1995, Figure M0791. The sequece is olie here. 14 The lie segmets from (i,logi to (i + 1,log(i + 1 lie below the curve y log x. (This is because the curve is covex, i.e., its secod derivative 1/x 2 is egative. The area uder these lie segmets from i 1 to i is log! log( + 1, sice it cosists of the rectagles of Fig. 3.1(b together with triagles with width 1 ad heights summig to log( + 1. Deduce that! e ( + 1. e This is mostly spelt out i the questio. The area of the rectagles is i2 logi log!, while the triagles add up to half a rectagle of height log( + 1. So log! log( logxdx ( + 1log( + 1 ( , givig! + 1(( + 1/e. The fial result is obtaied usig the fact that ( + 1 e (see Exercise 3(b of Chapter Use Stirlig s Formula to prove that ( / π. By Stirlig s formula, ( 2 (2! (! 2 4π(2/e 2 2π(/e 2 22 π. (Chec the defiitio of the relatio to esure that this is valid. 7

8 16 (a Let 2 be eve, ad X a set of elemets. Defie a factor to be a partitio of X ito sets of size 2. Show that the umber of factors is equal to (2 1. This umber is sometimes called a double factorial, writte (2 1!! (with!! regarded as a sigle symbol, the two exclamatio mars suggestig the gap of two, ot the factorial fuctio iterated! (b Show that a permutatio of X iterchages some -subset with its complemet if ad oly if all its cycles have eve legth. Prove that the umber of such permutatios is ((2 1!! 2. (c Deduce that the probability that a radom elemet of S iterchages some 1 2 -set with its complemet is O(1/. We get a 1-factor by writig /2 boxes each with room for two etries, ad fillig them with the elemets 1,...,. These ca be writte i the boxes i! ways. However, permutig the boxes (i! ways, or the elemets withi the boxes (i 2 ways does t chage the 1-factor. The product of these umbers is (2; dividig, we obtai (2 1 (2 1!! for the umber of 1-factors. (b Suppose that the -set A is exchaged with its complemet B by a permutatio. The elemets of A ad B alterate aroud each cycle, which thus has eve legth. Coversely, if all cycles have eve legth, we may colour the elemets i each cycle alterately red ad blue; the red ad blue sets are the exchaged. From a permutatio with all cycles eve we obtai a pair of 1-factors as follows. A 2-cycle is assiged to both 1-factors; i a loger cycle, the cosecutive pairs are assiged alterately to the two 1-factors. The process is t uique, sice the startig poit is t specified; ideed, a permutatio gives rise to 2 d ordered pairs of 1-factors, where d is the umber of cycles of legth greater tha 2. Coversely, let a pair of 1-factors be give. Their uio is a graph with all vertices of valecy 2, thus a disjoit uio of circuits, all of eve legth (sice the 1-factors alterate aroud a circuit. We tae these circuits to be the cycles of a permutatio. I fact, for a circuit of legth greater tha 2, there are two choices for the directio of traversal. So the umber of permutatios obtaied is 2 d, where d is as before. (c The proportio is ((2 1!! 2 /(2! i1 e 1/2i i1 8 (1 1/2i

9 e i1 (1/2i e log/2+o(1 O( 1/2. 17 How may relatios o a -set are there? How may are (a reflexive, (b symmetric, (c reflexive ad symmetric, (d reflexive ad atisymmetric? A relatio is a set of ordered pairs, so the first part of the questio ass for the umber of subsets of a set of size 2 (the umber of ordered pairs. (a For a reflexive relatio, we must iclude all pairs (x,x, together with a arbitrary subset of the pairs (x,y with x y; there are ( 1 uequal pairs. (b There are ( + 1/2 uordered selectios of two elemets from, with repetitios allowed. A symmetric relatio is determied by a subset of these, sice it must iclude both or either of (x,y ad (y,x for all x,y. (c We must put i all pairs (x,x, ad decide about pairs (x,y with x y. So the relatio is determied by a subset of the set of uordered selectios without repetitios. (d Agai we iclude all pairs (x,x. For each x,y with x y, there are three possibilities: iclude (x,y, or (y,x, or either. 18 Give a relatio R o X, defie R + {(x,y : (x,y R or x y}. Prove that the map R R + is a bijectio betwee the irreflexive, atisymmetric ad trasitive relatios o X, ad the reflexive, atisymmetric ad trasitive relatios o X. Show further that this bijectio preserves the property of trichotomy. Let R be irreflexive, atisymmetric ad trasitive. We claim first that R + is reflexive, atisymmetric ad trasitive. The first two assertios are clear; we must verify the trasitive law. So suppose that (x,y,(y,z R +. If (x,y,(y,z R the (x,z R by the trasitivity of R; if (x,y R ad y z the (x,z R; ad the other two cases are similar. Coversely, let S be reflexive, atisymmetric ad trasitive, ad let S {(x,y : (x,y S ad x y}. Clearly S is irreflexive ad atisymmetric. We show that it is trasitive. Suppose that (x,y,(y,z S. The (x,y,(y,z S, so (x,z S, by trasitivity of S. Could we have x z? No, sice the (x,y,(y,x S with x y, cotradictig atisymmetry. So (x,z S. Moreover, (R + R ad (S + S, so we have a bijectio as claimed. 9

10 For the fial part, we must show that R + satisfies trichotomy if ad oly if R does. This is clear: for trichotomy ca be expressed as if x y, the oe of (x,y ad (y,x satisfies the relatio, ad the pairs (x,y with x y are the same i R ad R Recall that a partial preorder is a relatio R o X which is reflexive ad trasitive. Let R be a partial preorder. Defie a relatio S by the rule that (x,y S if ad oly if both (x,y ad (y,x belog to R. Prove that S is a equivalece relatio. Show further that R iduces a partial order R o the set of equivalece classes of S i a atural way: if (x,y R, the (x,y R, where x is the S- equivalece class cotaiig x, etc. (You should verify that this defiitio is idepedet of the choice of represetatives x ad y. Coversely, let X be a set carryig a partitio, ad R a partial order o the parts of the partitio. Prove that there is a uique partial preorder o X givig rise to this partitio ad partial order as i the first part of the questio. Show further that the results of this questio remai valid if we replace partial preorder ad partial order by preorder ad order respectively, where a preorder is a partial preorder satisfyig trichotomy. Now Let R be a partial preorder (a reflexive ad trasitive relatio, ad let S {(x,y : (x,y R ad (y,x R}. S is reflexive: for (x,x R for all x. S is symmetric, by defiitio. S is trasitive: for, give (x,y,(y,z S, we have (x,y,(y,z R so (x,z R, ad also (y,x,(z,y R so (z,x R, whece (x,z S. Thus S is a equivalece relatio. Let x be the equivalece class cotaiig x. We put R {(x,y : (x,y R}. Note that the defiitio is idepedet of the choice of represetatives of the equivalece classes. For, if x x ad y y, ad (x,y R, the (x,x,(x,y,(y,y R, so (x,y R. Now the verificatio that R is reflexive ad trasitive is straightforward. To show that R is atisymmetric, suppose that (x,y,(y,x R. The (x,y,(y,x R, ad so (x,y S, whece x y. The verificatio that trichotomy carries over from R to R is more of the same. 10

11 20 List the (a partial preorders, (b preorders, (c partial orders, (d orders o the set {1,2,3}. The umbers are (a 29, (b 13, (c 19, (d 6. (The umbers of ulabelled structures are 9, 4, 5, 1 respectively. It is ot too much labour to draw all the ulabelled orders, etc., ad cout the umbers of labelligs of each. For partial orders o up to four poits, see N. J. A. Sloae ad S. Plouffe, The Ecyclopedia of Iteger Sequeces, Academic Press, 1995, Figure M1495, olie here. 21 Prove that B <! for all > 2. With every permutatio of a set, there is associated the partitio of the set ito disjoit cycles of the permutatio (see page 30. Every partitio arises from a permutatio i this way: tae a cyclic permutatio of each part of the partitio, ad compose them. So the umber! of permutatios is at least as great as the umber B of partitios. The partitio with a sigle part is associated with the cyclic permutatios, of which there are ( 1! (Exercise 8; this umber is greater tha 1 if 3, so B <! for > Verify, theoretically or practically, the followig algorithm for geeratig all partial permutatios of {1,...,}: ( Algorithm: Partial permutatios of {1,..., } FIRST PARTIAL PERMUTATION is the empty sequece. NEXT OBJECT after (x 1,...,x m : If the legth m of the curret sequece is less tha, exted it by adjoiig the least elemet it does t cotai. Otherwise, proceed as i the algorithm for permutatios, up to the poit where x j ad x are iterchaged; the, istead of reversig the terms after x j, remove them from the sequece. Use iductio o : chec by had that the algorithm is correct for small. (For example, whe 3, it geerates i tur /0, 1, 12, 123, 13, 132, 2, 21, 213, 23, 231, 3, 31, 312, 32, 321, ad the termiates. Whe it reaches i, it the geerates i order all partial permutatios which begi with i (sice the same algorithm is beig applied to the last 1 positios usig the symbols differet from i, ad the proceeds to i

12 23 Verify the followig recursive procedure for geeratig the set of partitios of a set X. ( Recursive algorithm: Partitios of X If X /0, the /0 is the oly partitio. If X /0, the select a elemet x X; geerate all subsets of X \ {x}; for each subset Y, geerate all partitios of X \ ({x} Y, ad adjoi to each the additioal part {x} Y. This algorithm is just the costructive form of the proof of the recurrece ( Let A (a i j ad B (b i j be ( + 1 ( + 1 matrices (with rows ad colums idexed from 0 to defied by a i j ( i j, bi j ( 1 i+ j( i j (where 0 if i < j. Prove that B A 1. ( i j The vector (x 0,...,x represets (with respect to the basis cosistig of powers of t the polyomial f (t x( i t i. Now let f (t + 1 x i (t + 1 i y i t i. By the Biomial Theorem, y j i i j x j ; so the trasformatio f (t f (t + 1 is represeted by the matrix A. Similarly, the trasformatio f (t f (t 1 is represeted by B. But these trasformatios are obviously iverses of each other. A direct proof would ru as follows. We are required to show that j ( i j ( j ( 1 j { 1 if i 0 if i. Now ( i j 0 uless j i, ad ( j 0 uless j; so the sum is zero if i >, ad is oe if i (sice the oly the term j i is o-zero. Try to prove that the sum is zero for i <. (Your coclusio may well be that this is harder tha the coceptual proof outlied above. No solutios will be give for the projects. A crib for 25 is I. P. Goulde ad D. M. Jacso, Combiatorial Eumeratio, Joh Wiley & Sos, New Yor, I 27, the limitig ratio was show by A. Réyi to be e: see Publ. Math. Ist. Hugar. Acad. Sci. 4 (1959,

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

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

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

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

5.3. Generalized Permutations and Combinations

5.3. Generalized Permutations and Combinations 53 GENERALIZED PERMUTATIONS AND COMBINATIONS 73 53 Geeralized Permutatios ad Combiatios 53 Permutatios with Repeated Elemets Assume that we have a alphabet with letters ad we wat to write all possible

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

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

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

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

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

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

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

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

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

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

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

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

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

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

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

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

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

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

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu> (March 16, 004) Factorig x 1: cyclotomic ad Aurifeuillia polyomials Paul Garrett Polyomials of the form x 1, x 3 1, x 4 1 have at least oe systematic factorizatio x 1 = (x 1)(x 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

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

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

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

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

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

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

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

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

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

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

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

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

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

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

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 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

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 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 Combinatorics. Peter J. Cameron

Notes on Combinatorics. Peter J. Cameron Notes o Combiatorics Peter J Camero ii Preface: What is Combiatorics? Combiatorics, the mathematics of patters,, helps us desig computer etwors, crac security codes, or solve sudous Ursula Marti, Vice-Pricipal

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

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

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

2. Degree Sequences. 2.1 Degree Sequences

2. Degree Sequences. 2.1 Degree Sequences 2. Degree Sequeces The cocept of degrees i graphs has provided a framewor for the study of various structural properties of graphs ad has therefore attracted the attetio of may graph theorists. Here we

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

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design A Combied Cotiuous/Biary Geetic Algorithm for Microstrip Atea Desig Rady L. Haupt The Pesylvaia State Uiversity Applied Research Laboratory P. O. Box 30 State College, PA 16804-0030 haupt@ieee.org Abstract:

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

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

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

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

Mathematical goals. Starting points. Materials required. Time needed

Mathematical goals. Starting points. Materials required. Time needed Level A1 of challege: C A1 Mathematical goals Startig poits Materials required Time eeded Iterpretig algebraic expressios To help learers to: traslate betwee words, symbols, tables, ad area represetatios

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

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

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 [C] Commuicatio Measuremet A1. Solve problems that ivolve liear measuremet, usig: SI ad imperial uits of measure estimatio strategies measuremet strategies.

More information

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Cosider a legth- sequece x[ with a -poit DFT X[ where Represet the idices ad as +, +, Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Usig these

More information

PERMUTATIONS AND COMBINATIONS

PERMUTATIONS AND COMBINATIONS Chapter 7 PERMUTATIONS AND COMBINATIONS Every body of discovery is mathematical i form because there is o other guidace we ca have DARWIN 7.1 Itroductio Suppose you have a suitcase with a umber lock. The

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

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients 652 (12-26) Chapter 12 Sequeces ad Series 12.5 BINOMIAL EXPANSIONS I this sectio Some Examples Otaiig the Coefficiets The Biomial Theorem I Chapter 5 you leared how to square a iomial. I this sectio you

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

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

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

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

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

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

Proof of Geeratig Fuctio For J.B.S.A.R.D.T.a.a.

Proof of Geeratig Fuctio For J.B.S.A.R.D.T.a.a. Ca. J. Math., Vol. XXXVII, No. 6, 1985, pp. 1201-1210 DIRECTED GRAPHS AND THE JACOBI-TRUDI IDENTITY I. P. GOULDEN 1. Itroductio. Let \a i L X deote the X determiat with (/', y)-etry a-, ad h k = h k (x

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

Math 114- Intermediate Algebra Integral Exponents & Fractional Exponents (10 )

Math 114- Intermediate Algebra Integral Exponents & Fractional Exponents (10 ) Math 4 Math 4- Itermediate Algebra Itegral Epoets & Fractioal Epoets (0 ) Epoetial Fuctios Epoetial Fuctios ad Graphs I. Epoetial Fuctios The fuctio f ( ) a, where is a real umber, a 0, ad a, is called

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

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

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

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern. 5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers

More information

Fast Fourier Transform

Fast Fourier Transform 18.310 lecture otes November 18, 2013 Fast Fourier Trasform Lecturer: Michel Goemas I these otes we defie the Discrete Fourier Trasform, ad give a method for computig it fast: the Fast Fourier Trasform.

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

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

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4 GCE Further Mathematics (660) Further Pure Uit (MFP) Tetbook Versio: 4 MFP Tetbook A-level Further Mathematics 660 Further Pure : Cotets Chapter : Comple umbers 4 Itroductio 5 The geeral comple umber 5

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

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

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

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chi-square (χ ) distributio.

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

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

Multiplexers and Demultiplexers

Multiplexers and Demultiplexers I this lesso, you will lear about: Multiplexers ad Demultiplexers 1. Multiplexers 2. Combiatioal circuit implemetatio with multiplexers 3. Demultiplexers 4. Some examples Multiplexer A Multiplexer (see

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

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

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

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

CS85: You Can t Do That (Lower Bounds in Computer Science) Lecture Notes, Spring 2008. Amit Chakrabarti Dartmouth College

CS85: You Can t Do That (Lower Bounds in Computer Science) Lecture Notes, Spring 2008. Amit Chakrabarti Dartmouth College CS85: You Ca t Do That () Lecture Notes, Sprig 2008 Amit Chakrabarti Dartmouth College Latest Update: May 9, 2008 Lecture 1 Compariso Trees: Sortig ad Selectio Scribe: William Che 1.1 Sortig Defiitio 1.1.1

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

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

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

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

Hypergeometric Distributions

Hypergeometric Distributions 7.4 Hypergeometric Distributios Whe choosig the startig lie-up for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you

More information