18.1 The points I and J

Size: px
Start display at page:

Download "18.1 The points I and J"

Transcription

1 18 Euclidean Geometry Dieses schöne Resultat [...] blieb aber lange unbeachtet, vermutlich, weil sich die Geometer an den Gedanken gewöhnt hatten, dass Metrik und projektive Geometrie in keiner Beziehung zueinander ständen. Felix Klein about Laguerres formula, Vorlesungen über Nicht-Euklidische Geometrie, 1928 In this chapter we will merge two different worlds: CP 1 and RP 2. Both can be considered as representing a real two dimensional plane. They have different algebraic structures and they both represent different compactifications of the Euclidean plane: For RP 2 we added a line at infinity. For CP 1 we added a point at infinity. Both spaces have different weaknesses and strengths. In the first two parts of the book we learned that RP 2 is very well suited for dealing for instance with incidences of lines and points, with conics in their general form and with cross-ratios. We did not have a proper way to talk about circles, angels and distances in RP 2. The last two chapters introduced CP 1. This space was very good for dealing with co-circularity and also for dealing with angles. Unfortunately this space was not suitable for properly dealing with lines. However, lines were purely supported by CP 1. They had to be considered as circles with infinite radius and they were not even projectively invariant objects. We will now introduce an algebraic system that is capable of merging advantages of both worlds. We will end up with a framework in which we express all Euclidean properties by projectively invariant expressions.

2 Euclidean Geometry 18.1 The points I and J The key to expressing Euclidean properties in projective geometry is as simple as it is powerful. We have to introduce two special points. All Euclidean properties will be expressed as projectively invariant expressions in which these two points play a special role. There are several possibilities for the choice of these points. However, there is a special choice for the two points under which all formulas become very simple and elegant. The points are: I = i 1 and J = i Strictly speaking these points are not even members of RP 2, since they have complex coordinates. We will consider them formally as members of CP 2. All algebraic calculations will be carried out in CP 2 (we use homogeneous coordinates with complex coordinate entries). However the start elements and the results of our calculations will usually be in RP 2. All our considerations will refer to the standard embedding of the Euclidean plane R 2 into RP 2 by ( x y) x y. 1 Thus as usual the line at infinity will have coordinates l = (0, 0, 1) T. We will collect a few useful properties of the points I and J. I and J span the line at infinity: The points I and J both are on l since they have a zero in their last entry. Since they are two different points they even span l. i 1 i 1 = 0 0 = 2i i 1 I and J can transfer finite points from RP 1 to C: Consider a point p with homogeneous coordinates (a, b, 1) T in RP 2. In the euclidean plane this point would represent the point (a, b) T. In the complex plane it would represent the point a + ib. Now consider the 3 3 determinant [p, I,l ]. We get: [p, I,l ]= a i 0 b = a + ib. Similarly we get the conjugate a + ib: ai0 [p, J,l ]= b = a ib.

3 18.2 Cocircularity 281 I and J can express determinants in CP 1 as determinants in RP 2 : We consider also the standard embedding in CP 2 : ( z z. 1) Now consider two points p 1 and p 2 in RP 2 represented by vectors (a 1,b 1, 1) T and (a 2,b 2, 1) T. In CP 1 they would represent points p 1 =(a 1 + ib 1, 1) and p 2 =(a 2 + ib 2, 1). Considering the determinant [p 1,p 2, I] we get: a 1 a 2 i [p 1,p 2, I] = b 1 b = a 2 ib 1 a 1 + ib 2 And similarly we get the conjugate =(a 2 + ib 2 ) (a 1 + ib 1 ) =[ p 2, p 1 ]. [p 1,p 2, J] =[ p 2, p 1 ]. Thus the use of I and J allows us to express determinants of CP 1 (and their conjugates) as determinants of RP 2 and involving I and J. The fact that we rely on the standard embedding in both worlds will not harm later on since in both worlds we will only have to deal with projectively invariant conditions. The last property is crucial. It is the key to translate projective in variants of CP 1 to projective invariants of RP Cocircularity Let us start applying I and J to express Euclidean properties. The strategy here will be: Express the property in CP 1 as bracket identity. Translate the identity bracket by bracket to RP 2 (using I and J). Consider the translated identity as a projective invariant in RP 2. One of the most fundamental properties of CP 1 was cocircularity. Four points of CP 1 are cocircular if their cross ratio was real. Assume that we are given four points A, B, C, D in RP 2 (w.r.t standard embedding). We consider their corresponding counterparts in CP 1. The complex points Ã, B, C, D. It is easy to express cocircularity in terms of Ã, B, C, D. The points are cocircular if

4 Euclidean Geometry Or equivalently [Ã C][ B D] R. [Ã D][ B C] [Ã C][ B D] = [Ã D][ B C] Expressing the brackets in RP 2 we get ( ) [Ã C][ B D]. [Ã D][ B C] [ACI][BDI] [ADI][BCI] = [ACJ][BDJ] [ADJ][BCJ]. We can also multiply by the denominators to obtain a projectively invariant polynomial equation: [ACI][BDI][ADJ][BCJ] = [ACJ][BDJ][ADI][BCI]. Summarizing we get the following characterization of cocircularity: Theorem The points A, B, C, D in RP 2 are cocircular if [ACI][BDI][ADJ][BCJ] = [ACJ][BDJ][ADI][BCI]. Comparing the above bracket expression with the expression of derived in Section 10.2 that characterized when six points are on a conic we observe that the expression [ACI][BDI][ADJ][BCJ] = [ACJ][BDJ][ADI][BCI]. expresses the fact that A, B, C, D, I, J lie on a common conic (each point occurs quadratically on either side). Thus we can reformulate the last theorem and state Theorem The points A, B, C, D in RP 2 are cocircular if A, B, C, D, I, J are on a common conic. Or in other words: Circles are conics through I and J! The last fact can also be derived in a different way. If we consider the (euclidean) equation of a circle with midpoint (m x,m y ) and radius r (x m x ) 2 +(x m y ) 2 = r 2 we can translate this into a quadratic equation in homogeneous coordinates. We obtain x 2 + y 2 2m x xz 2m y yz +(m 2 x + m 2 y r 2 ) z 2 =0 which is for suitably chosen parameters a, b, c the special conic:

5 18.3 Transformations 283 J A D A I B B C C D Fig Projective interpretation of cocircularity. x 2 + y 2 + a xz + b yz + c z 2 =0. Inserting the coordinates of I in this equation we get ( i) a 0+b 0+c 0= = 0. Hence I lies on this arbitrarily chosen circle. A similar calculation also shows that J lies on any circle, as well. Figure 17.1 illustrates the projective interpretation of cocircularity. Projectively cocircularity of four points has to be considered as coconicallity of these four points with I and J. Usually one does not see I and J since they are complex (and at infinity) Transformations Before we discuss further examples of expressing Euclidean properties in projective terms we will have a brief look at the philosophy behind the approach of the last section. Our way of expressing cocircularity by a projectively invariant expression did heavily rely on the standard embedding of Euclidean geometry into projective geometry (as well in RP 2 as in CP 1 ). This standard embedding caused the particular choice of the coordinates for I and J. The crucial property of I and J is that they, considered as a pair, remain invariant under certain transformations (in particular under Euclidean transformations). At this point we have to be a little careful to exactly specify which groups of transformations we consider. Roughly speaking, the geometrically relevant groups of transformations in RP 2 form a hierarchical system. In order of generality the groups relevant for us are projective transformations, affine transformations, similarity transformations, Euclidean transformations. The following table lists transformations and invariant properties that belong to these different subgroups the projective transformations.

6 Euclidean Geometry projective affine similarity Euclidean Transformations : general projective shear scaling rotation reflection translation Invariants : cross-ratio ratios of length angles distances We speak of projective geometry if we only consider properties that remain invariant under projective transformations. We speak of affine geometry if we only consider properties that remain invariant under affine transformations, and so on. Thus Euclidean geometry deals with properties that remain invariant under rotation, translation and reflection. Our considerations will now deal with similarity geometry. The difference to Euclidean transformations is that in addition to rotation, translation and reflection also scaling is allowed. Thus lengths are no intrinsic concepts of similarity geometry, but angles and circles are. Ratios of lengths is also concept of similarity geometry. In fact, in the context of geometric theorems talking about similarity geometry is usually more appropriate then talking about Euclidean geometry. The only difference between the two geometries is that in Euclidean geometry we can actually measure a length whereas in similarity geometry we can only compare lengths. Theorems of Euclidean geometry however are most often not formulated on the level of concrete lengths. Usually they only compare lengths relative to each other (you would not start any reasonable theorem with a sentence like: Take a segment of length 3cm... ). So all the theorems we normally consider as Euclidean theorems are in fact theorems of similarity geometry. We will see that this is exactly the class of theorems governed by I and J. In Section 3.6 when we introduced projective transformations we first started by expressing rotations and translations and scalings (w.r.t. the standard embedding) as multiplication by a 3 3 matrix. There we obtained the following forms for each of the transformations, respectively: cos(α) sin(α) 0 sin(α) cos(α) 0 ; 10t x 01t y ; s 00 0 s

7 18.3 Transformations 285 A matrix that performs an orientation preserving similarity transformation (a combined rotation, translation and scaling) has the general form: c s a s c b Similarly, a general orientation reversing similarity transformation has the form: c s a s c b In both cases we must require c 2 + s 2 0 for having a non-zero determinant. Theorem W.r.t the standard embedding orientation preserving similarity transformations are exactly those matrices that leave I and J invariant. orientation reversing similarity transformations are exactly those matrices that interchange I and J. Proof. Applying an orientation preserving similarity S to I we get S I = c s a s c b i 1 = ic + s is + c =(c + is) i 1 =(c + is)i The scalar c + is is non-zero since c 2 + s 2 0. Similarly we obtain that S J =(c + is) J. For an orientation reversing similarity R the calculation is similar. We get: R I = c s a s c b i 1 = ic + s is c =( c is) i 1 =( c is)j Again similarly we get R J =( c is)i. Now we conversely assume that M is a matrix that leaves I invariant. Since M is assumed to be a matrix that represents a projective transformation of RP 2 we may assume that M has real entries. We then have M I = λi. We now successively determine constraints on the entries of M. We first consider the first two entries of the last row of M. i 1 = λ i 1. xy 0 0 We get ix + y = 0 Since the entries of the matrix must be real these two entries must be zero. Since the matrix must have a non vanishing determinant the last entry of the last row must be non-zero. We may assume that it is 1 by rescaling the matrix. We now focus on the upper left 2 2 matrix. We have,

8 Euclidean Geometry uv wx i 1 = λ i Thus we have ui + v = iλ and wi + x = λ. Subtracting i-times the first equation from the second we get: u iv wi + x = 0. Since the matrix entries are real we must have u = x and v = w. The remaining two matrix entries can be arbitrary since they are multiplied by 0. All in all our matrix has the form c s a s c b, an orientation preserving similarity. A similar calculation shows that if M interchanges I and J we get an orientation reversing similarity. The last theorem states that we can characterize similarities by the property that the pair of points {I, J} is left invariant. Thus we can in a sense reverse our point of view and make I and J to the first class citizens and use them to define what similarity geometry means. We will now provide a detailed example of how one can introduce the concepts of similarity geometry entirely based an two special points I and J. If I and J are fixed we can define similarity transformations to be those projective transformations that leave the pair {I, J} invariant. A property that is invariant under similarity transformations is called a similarity property. It will be our aim to base invariant properties and constructions of similarity geometry directly on I and J without the detour via similarity transformations. In the previous section we already achieved this goal for one specific similarity property: cocircularity. We expressed cocircularity of four points A, B, C, D as a purely projective condition involving these points and the pair {I, J}. Now we again turn the point of view, make I and J the primary objects and define cocircularity by the property of Theorem Thus we have: Similarity transformations are those that fix the pair {I, J}. A, B, C, D are cocircular if A, B, C, D, I, J lie on a conic. Based on these definitions we can derive the statement Cocircularity is invariant under similarity transformations. Proof. A proof of this fact would look as follows: Assume that A, B, C, D are cocircular (thus A, B, C, D, I, J lie on a conic ) and assume that τ: RP 2 RP 2 is a similarity transformation. We will prove that τ(a), τ(b), τ(c), τ(d), are cocircular as well. Since τ is a projective transformation and being on a conic is a projectively invariant property the six points. τ(a), τ(b), τ(c), τ(d), τ(i) and τ(j) are on a conic as well.

9 18.4 Translating theorems 287 A D B E J E H C H F G F A D I B C G Fig Miguel s theorem and its projective interpretation. Since τ is a similarity transformation we have either τ(i) =I and τ(j) =J or we have τ(i) =J and τ(j) =I. In either case this implies that τ(a), τ(b), τ(c), τ(d), I and J are on a conic which means that τ(a), τ(b), τ(c), τ(d), are cocircular. At first sight such a kind of reasoning may seem to be unnaturally complicated. However, it is conceptually very nice since we reduced the concept of cocircularity entirely to the introduction of two special points I and J and to projective invariants, without even referring to the particular coordinates of I and J. If we chose special coordinates I =( i, 1, 0) T and J =(i, 1, 0) T then (w.r.t. the standard embedding) this setup specializes to our usual picture of similarity transformations and cocircularity Translating theorems In particular the considerations of the last section show that behind every theorem of similarity geometry (or Euclidean geometry) there lies a projective truth. We will exemplify this by a nice theorem that needs cocircularity as only predicate. Theorem Miquel s Theorem: Let A, B, C, D, E, F, G, H be eight distinct points in the euclidean plane such that the following quadruples are cocircular: (A, B, C, D), (A, B, E, F ), (B, C, F, G), (C, D, G, H), (D, A, H, E). Then (E, F, G, H) will be cocircular as well. Proof. We will present a proof of Miquel s Theorem based on projective invariants. The hypotheses of the theorem imply that the following sixtuples lie on common conics: (A, B, C, D, I, J), (A, B, E, F, I, J), (B, C, F, G, I, J), (C, D, G, H, I, J), (D, A, H, E, I, J). This produces the following five bracket equations.

10 Euclidean Geometry [CDJ][ABJ][BCI][ADI] = [ABI][CDI][ADJ][BCJ] [ABI][AEJ][BFJ][EFI] = [ABJ][BFI][AEI][EFJ] [BCJ][BFI][CGI][FGJ] = [BCI][BFJ][CGJ][FGI] [CDI][CGJ][GHI][DHJ] = [CDJ][CGI][GHJ][DHJ] [ADJ][AEI][EHJ][DHI] = [ADI][AEJ][EHI][DHJ] All brackets in these expressions will be non-zero, since they always involve two distinct finite points and either I or J. Multiplying all left sides and all rights sides and canceling brackets that appear on both sides we derive the equation. [EFI][FGJ][EHJ][GHI] = [EFJ][FGI]EHI][GHJ]. This expression is exactly the condition that also (E, F, G, H, I, J) are on a conic. And this implies the cocircularity of (E, F, G, H). Our proof did (except for non-degeneracy assumptions) not refer to the coordinates of I and J. The bracket argument also proves a corresponding purely projective theorem about eight points and six conics, where I and J are located at real and finite positions. The corresponding theorem is shown in Figure 17.2 on the right. The labeling is consistent with the labeling of Miquel s Theorem on the left More geometric properties Our next aim is to derive projective conditions for other concepts of similarity geometry. We start with perpendicularity. We want to characterize the property that for three points A, B, C the lines AB and AC are perpendicular to each other. If we associate the three points with the corresponding points of the complex number plane Ã, B and C then we can represent the above relation as an algebraic condition. For this we have to certify that the angle between the complex numbers à B and à C is 90. This is the case if and only if à B ir. à C This in turn translates to the equation: à B ) (à à C = B à C. As before we reinterpret this equation in RP 2 with the help of I and J. We get: [ABI] [ACI] = [ABJ] [ACJ].

11 18.5 More geometric properties 289 B I L J A l M C A m l Fig Projective interpretation of orthogonality. Slightly reordering the terms we get: 1 = [ABI][ACJ] [ACI][ABJ] =(B, C; I, J) A. We can formulate this characterization in the following result. Theorem The lines AB and AC are orthogonal if and only if the pairs of lines (AB, AC) and (AI, AJ) are in harmonic position. Alternatively we can directly speak of orthogonal lines by considering their intersections with the line at infinity in relation to I and J Theorem Two lines l and m are orthogonal if and only if their intersections with the line at infinity L = l l and M = m l are in harmonic position with I and J. Proof. Assume that l and m are orthogonal and that A is their intersection. Let B be a point on l and let C be a point on m. The cross-ratio (B, C; I, J) A is the same as the cross-ratio (L, M; I, J). Hence by Theorem 17.5 the point pairs {L, M} and {I, J} are in harmonic position. Conversely, assume that {L, M} and {I, J} are in harmonic position. Then l and m cannot be parallel since otherwise the cross-ratio of the four infinite points would be 1. Thus we may assume that l and m have a finite intersection A. After again introducing two auxiliary points B and C on the two lines Theorem 17.5 proves the claim. The next geometric property we want to translate is closely related to characterizing circles by their midpoint and a point on the boundary. Theorem If the distance from A to B equals the distance from A to C, then [ABI][ACI][CBJ] 2 =[ABJ][ACJ][CBI] 2.

12 Euclidean Geometry B A C Fig Projective interpretation of orthogonality. Proof. Again we first consider the situation realized in the complex number plane C. If AB = AC then the three points A, B, C form an isosceles triangle. In this this case the angle B (A, C) is the same as the angle C (B, A). This means that we have This is equivalent to à B / B C C B R. à C ( ) (à B)(à C) ( C B)( B C) = (à B)(à C) ( C B)( B C) Translated to RP 2 this reads as: This is equivalent to Which is the desired equation. [ABI][ACI][CBJ] 2 =[ABJ][ACJ][CBI] 2 Observe that also the characterization in the last theorem turns out to be a projectively invariant expression. However, it is only a necessary condition for AB = AC. The point A occurs quadratically in this expression. Thus we may expect a conic section as locus for A if B and C are fixed. In fact this conic consists of two lines. One is the median of B and C the other this the join of B and C. The formula becomes zero if A is on one of this lines. Thus the case of A being the midpoint of a circle through B and C is only one situation when the above expression vanishes. Being on the join of B and C is the other. Later in this section we will also learn about a necessary and sufficient characterization of AB = AC.

13 18.6 Laguerre s Formula Laguerre s Formula So far we only used I and J to express properties that stay invariant under similarity transformations. We can even go one step further and perform measurements using I and J. In Section 4 we learned that the simplest way to extract projectively invariant data from point configurations was to calculate cross-ratios. We will now use cross ratios in combination with I and J to mimic measurements in a projective setup. The key result (which has also many beautiful generalizations, as we will see later) is Laguerre s formula. It was found in 1851 by Edmond Laguerre when he was just 19 years old. It allows to measure the angle between two lines. Before we state Laguerre s formula we will clarify what exactly we mean by the angle between two lines l and m. Let us assume first that these two lines intersect in a single finite point O of the Euclidean plane. By the angle (l, m) from l to m we mean the angle by which l has to be rotated counterclockwise around O until it coincides with m. By this angles between two lines will always lie in the open interval between 0 and π. If lines coincide or are parallel then the angle between them is 0. Alternatively one could say that the angle between l and m corresponds to the angle about which l has to be rotated that its direction coincides with m. One might expect that angles should be measured between 0 and 2π. However this makes no sense for unoriented lines. Now we are ready to state Laguerres formula Theorem Let l and m be two finite lines of RP 2 and let L = l l and M = m l the corresponding intersections with the line at infinity. Then the angle between l and m is (modulo π) 1 2i ln( (M, L; I, J) ). Proof. The proof of this surprising result is straight forward using the method of transferring geometric properties from RP 2 to C. Let l =(l 1,l 2,l 3 ) T and m =(m 1,m 2,m 3 ) T be the homogeneous coordinates of the lines. Then L and M have the coordinates L =(l 2, l 1, 0) T and M =(m 2, m 1, 0). The first two entries of these vectors are normal vectors to the two lines. The angle of these normal vectors (modulo π) is exactly the angle between the two lines. We translate the normal vectors to two corresponding complex numbers z l = l 2 i l 1 = r l e iψ l and z m = m 2 i m 1 = r m e iψm. The angle ψ m ψ l modulo π is exactly the angle we are looking for. We can extract this angle by forming the following ratio: z m z l = r m e iψm r l e iψl z m z l r m e iψm r l e iψ l = eiψm e iψ l e iψm e iψ l = e 2i(ψm ψ l) In the last expression the absolute values of z l and z m cancel since each number (or its conjugate) is used in the numerator and in the denominator.

14 Euclidean Geometry Our considerations of Section 17.1 show that we have z l =[L, I,l ]; z l =[L, J,l ]; z m =[M,I,l ]; z m =[M,J,l ]. Using these determinants to express the above expression we get (M, L; I, J) = [M,I,l ][L, J,l ] [M,J,l ][L, I,l ] = z m z l z m z l = e 2i(ψm ψ l). Resolving for the desired angle we get: ψ m ψ l = 1 2i ln( (M, L; I, J) ) which is exactly Laguerre s formula. Laguerre s formula relates the properties of angles to the properties of cross ratios in a surprising way. We will collect a few of these properties: Measurement modulo π: The fact that angles between lines are measured modulo π is reflected by the fact that the natural logarithm function is only unique modulo a factor of 2πi (we have e a = e ( a +2πi)) together with the factor 1 2i in Laguerre s formula. Real lines generate real angles: At first it is surprising that Laguerre s formula indeed produces only real values. If fact, if the lines have real coordinates then the numerator and the denominator of (M, L; I, J) = [M,I,l ][L,J,l ] [M,J,l ][L,I,l ] are complex conjugates. Dividing two conjugate numbers produces a complex number on the unit circle. Its logarithm is purely imaginary. This is compensated by the factor 1 2i. Interchange of lines reverses angle: We must have (l, m) = (m, l) modulo π. Interchanging L and M transforms the cross-ratio to its inverse: (L, M; I, J) = 1/(M, L; I, J). Since ln(a) = ln(1/a) modulo 2πi this produces the desired sign change. Angles are additive: If we have three lines h, l, m we must have (l, m)+ (m, h) = (l, h) modulo π. This formula expresses the multiplicativity of the cross ratio. We have (M, L; I, J)(H, M; I, J) = [M,I,l ][L, J,l ] [M,J,l ][L, I,l ] [H, I,l ][M,J,l ] [H, J,l ][M,I,l ] = [H, I,l ][L, J,l ] [H, J,l ][L, I,l ] =(H, L; I, J) By ln(a b) = ln(a) + ln(b) modulo 2πi this translates to additivity of angles.

15 18.7 Distances 293 Orientation preserving similarities leave angles invariant: If τ is an orientation preserving similarity we must have (l, m) = (τ(l), τ(m)) (here we interpret τ(l) as the corresponding action of τ on a line by multiplication of the inverse transformation matrix). To see this identity we calculate (M, K, I, J) = (τ(m),τ(k); τ(i),τ(j)) =(τ(m),τ(k); I, J). Hence the angle is the same after the transformation. Orientation reversing similarities reverse angles: If τ is an orientation reversing similarity we must have (l, m) = (τ(l), τ(m)). We get: (M, K, I, J) = (τ(m),τ(k); τ(i),τ(j)) =(τ(m),τ(k); J, I) =1/(τ(M),τ(K); I, J). Which produces (via the logarithm) the reversed angle. One should also observe that Laguerre s formula contains as a special case the characterization of right angles. If l and m are orthogonal we have (l, m) = π 2. Since eiπ = 1 this translates via Laguerre s formula to the condition (M, L; I, J) = 1. This is exactly our characterization of Theorem Distances Using I and J we can also express distances between two points P and Q in Euclidean geometry. This formula is a little bit tricky and we will present it her without a strictly formal proof. First of all we cannot expect to express the distance purely as a projectively invariant formula only involving P, Q, I and J, since distance is not an invariant of similarity geometry. The only thing we can hope for is that we obtain a formula that compares the distance of P and Q to the distance of two reference points A and B. Thus we will compute. If the distance A, B is normalized to be the unit length we will have a formula for the distance of two arbitrary points. We will finally have an invariant expression in the six points A, B, P, Q, I, J. For two points P =(p 1,p 2 ) and Q =(q 1,q 2 ) in the Euclidean plane R 2 we usually calculate the distance via Pythagoras s Theorem. a formula for P,Q A,B P, Q = (p 1 p 2 ) 2 +(q 1 q 2 ) 2. We will first express this formula in terms of determinants and than enlarge this formula to get a projectively invariant expression. Again we assume that

16 Euclidean Geometry P and Q have homogeneous coordinates w.r.t. the standard embedding. Thus we have P =(p 1,p 2, 1) T and Q =(q 1,q 2, 1) T. We now consider the expression [P, Q, I][P, Q, J] expanding this term, we get p 1 q 1 i p 2 q p 1 q 1 i p 2 q = ((q 1 p 1 )+i(q 2 p 2 )) ((q 1 p 1 ) i(q 2 p 2 )) = (p 1 p 2 ) 2 +(q 1 q 2 ) 2 = P, Q. This is exactly the desired distance. Unfortunately the expression [P, Q, I][P, Q, J] is not at all a projective invariant. The following expression however is: [P, Q, I][P, Q, J][A, I, J][B,I, J] [A, B, I][A, B, J][P, I, J][Q, I, J] = PQ This expression indeed is a projective invariant (each letter occurs with the same power in numerator and denominator) Furthermore for the case of the standard embedding and A, B = 1 we get exactly the right distance function (as the following comparison of terms shows): All in all we get: P,Q 2i 2i {}}{{}}{{}}{ [P, Q, I][P, Q, J] [A, I, J] [B,I, J] = P, Q. [A, B, I][A, B, J] [P, I, J] [Q, I, J] }{{}}{{}}{{} 1 2i 2i Theorem The Euclidean distance of two points P and Q can be calculated by [P, Q, I][P, Q, J][A, I, J][B,I, J] [A, B, I][A, B, J][P, I, J][Q, I, J], if A, B =1is a reference length. It is also instructive to inspect this formula more closely. The occurrence of the square-root function expresses that it only makes sense to speak of distances up to a global sign. We can also compare the last result to the formula derived in Theorem 17.7, where we expressed a necessary condition for the property AB = AC. Theorem 17.9 contains this situation as special case. If we set P = A and Q = C and square the expression of Theorem 17.9 we get

17 1= ( ) 2 [A, C, I][A, C, J][A, I, J][B,I, J] [A, B, I][A, B, J][A, I, J][C, I, J] 18.7 Distances 295 = [A, C, I][A, C, J][A, I, J]2 [B,I, J] 2 [A, B, I][A, B, J][A, I, J] 2 [C, I, J] 2 = [A, C, I][A, C, J][B,I, J]2 [A, B, I][A, B, J][C, I, J] 2. This is also a necessary condition of AB = AC. In the formula 1= [A, C, I][A, C, J][B,I, J]2 [A, B, I][A, B, J][C, I, J] 2 the points A, B and C occur quadratically. If we fix two of the points then the locus for the remaining one must be a conic. The conic for B is the circle with center A and perimeter point C. Similarly the conic for B is the circle with center A and perimeter point B. The conic for A turns out to consist of two lines. One of the lines is the median of B and C the other one is the line at infinity (as a simple calculation shows). If we combine the equation [A, C, I][A, C, J][B,I, J] 2 =[A, B, I][A, B, J][C, I, J] 2 With the equation of Theorem 17.7 [A, B, I][A, C, I][C, B, J] 2 =[A, B, J][A, C, J][C, B, I] 2 by multiplying left an right sides we get [A, C, I] 2 [C, B, J] 2 [B,I, J] 2 =[A, B, J] 2 [C, B, I] 2 [C, I, J] 2. Taking the square-root on both sides We arrive at [A, C, I][C, B, J][B,I, J] =±[A, B, J][C, B, I][C, I, J]. Choosing the right sign in this expression turns out to be a necessary and sufficient condition for AB = AC. The formula that characterizes this case is: [A, C, I][C, B, J][B,I, J] = [A, B, J][C, B, I][C, I, J]. Observe that A occurs linearly in this formula and both B and C occur quadratically.

The complex projective line

The complex projective line 17 The complex projective line Now we will to study the simplest case of a complex projective space: the complex projective line. We will see that even this case has already very rich geometric interpretations.

More information

Conics and their duals

Conics and their duals 9 Conics and their duals You always admire what you really don t understand. Blaise Pascal So far we dealt almost exclusively with situations in which only points and lines were involved. Geometry would

More information

Conics and perspectivities

Conics and perspectivities 10 Conics and perspectivities Some people have got a mental horizon of radius zero and call it their point of view. Attributed to David Hilbert In the last chapter we treated conics more or less as isolated

More information

More on Bracket Algebra

More on Bracket Algebra 7 More on Bracket Algebra Algebra is generous; she often gives more than is asked of her. D Alembert The last chapter demonstrated, that determinants (and in particular multihomogeneous bracket polynomials)

More information

THE ENVELOPE OF LINES MEETING A FIXED LINE AND TANGENT TO TWO SPHERES

THE ENVELOPE OF LINES MEETING A FIXED LINE AND TANGENT TO TWO SPHERES 6 September 2004 THE ENVELOPE OF LINES MEETING A FIXED LINE AND TANGENT TO TWO SPHERES Abstract. We study the set of lines that meet a fixed line and are tangent to two spheres and classify the configurations

More information

Constructions with ruler and compass.

Constructions with ruler and compass. Constructions with ruler and compass. Semyon Alesker. 1 Introduction. Let us assume that we have a ruler and a compass. Let us also assume that we have a segment of length one. Using these tools we can

More information

Weekly Activities Ma 110

Weekly Activities Ma 110 Weekly Activities Ma 110 Fall 2008 As of October 27, 2008 We give detailed suggestions of what to learn during each week. This includes a reading assignment as well as a brief description of the main points

More information

Chapter 12. The cross ratio Klein s Erlanger program The projective line. Math 4520, Fall 2017

Chapter 12. The cross ratio Klein s Erlanger program The projective line. Math 4520, Fall 2017 Chapter 12 The cross ratio Math 4520, Fall 2017 We have studied the collineations of a projective plane, the automorphisms of the underlying field, the linear functions of Affine geometry, etc. We have

More information

Chapter 2. Linear Algebra. rather simple and learning them will eventually allow us to explain the strange results of

Chapter 2. Linear Algebra. rather simple and learning them will eventually allow us to explain the strange results of Chapter 2 Linear Algebra In this chapter, we study the formal structure that provides the background for quantum mechanics. The basic ideas of the mathematical machinery, linear algebra, are rather simple

More information

Linear Algebra: Matrix Eigenvalue Problems

Linear Algebra: Matrix Eigenvalue Problems CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given

More information

1 Differentiable manifolds and smooth maps. (Solutions)

1 Differentiable manifolds and smooth maps. (Solutions) 1 Differentiable manifolds and smooth maps Solutions Last updated: February 16 2012 Problem 1 a The projection maps a point P x y S 1 to the point P u 0 R 2 the intersection of the line NP with the x-axis

More information

chapter 12 MORE MATRIX ALGEBRA 12.1 Systems of Linear Equations GOALS

chapter 12 MORE MATRIX ALGEBRA 12.1 Systems of Linear Equations GOALS chapter MORE MATRIX ALGEBRA GOALS In Chapter we studied matrix operations and the algebra of sets and logic. We also made note of the strong resemblance of matrix algebra to elementary algebra. The reader

More information

November 18, 2013 ANALYTIC FUNCTIONAL CALCULUS

November 18, 2013 ANALYTIC FUNCTIONAL CALCULUS November 8, 203 ANALYTIC FUNCTIONAL CALCULUS RODICA D. COSTIN Contents. The spectral projection theorem. Functional calculus 2.. The spectral projection theorem for self-adjoint matrices 2.2. The spectral

More information

1 Differentiable manifolds and smooth maps. (Solutions)

1 Differentiable manifolds and smooth maps. (Solutions) 1 Differentiable manifolds and smooth maps Solutions Last updated: March 17 2011 Problem 1 The state of the planar pendulum is entirely defined by the position of its moving end in the plane R 2 Since

More information

MAT1035 Analytic Geometry

MAT1035 Analytic Geometry MAT1035 Analytic Geometry Lecture Notes R.A. Sabri Kaan Gürbüzer Dokuz Eylül University 2016 2 Contents 1 Review of Trigonometry 5 2 Polar Coordinates 7 3 Vectors in R n 9 3.1 Located Vectors..............................................

More information

Linear Algebra. Preliminary Lecture Notes

Linear Algebra. Preliminary Lecture Notes Linear Algebra Preliminary Lecture Notes Adolfo J. Rumbos c Draft date May 9, 29 2 Contents 1 Motivation for the course 5 2 Euclidean n dimensional Space 7 2.1 Definition of n Dimensional Euclidean Space...........

More information

Elementary maths for GMT

Elementary maths for GMT Elementary maths for GMT Linear Algebra Part 2: Matrices, Elimination and Determinant m n matrices The system of m linear equations in n variables x 1, x 2,, x n a 11 x 1 + a 12 x 2 + + a 1n x n = b 1

More information

A-Level Notes CORE 1

A-Level Notes CORE 1 A-Level Notes CORE 1 Basic algebra Glossary Coefficient For example, in the expression x³ 3x² x + 4, the coefficient of x³ is, the coefficient of x² is 3, and the coefficient of x is 1. (The final 4 is

More information

Chapter 1. Theorems of Ceva and Menelaus

Chapter 1. Theorems of Ceva and Menelaus hapter 1 Theorems of eva and Menelaus We start these lectures by proving some of the most basic theorems in the geometry of a planar triangle. Let,, be the vertices of the triangle and,, be any points

More information

MODEL ANSWERS TO HWK #7. 1. Suppose that F is a field and that a and b are in F. Suppose that. Thus a = 0. It follows that F is an integral domain.

MODEL ANSWERS TO HWK #7. 1. Suppose that F is a field and that a and b are in F. Suppose that. Thus a = 0. It follows that F is an integral domain. MODEL ANSWERS TO HWK #7 1. Suppose that F is a field and that a and b are in F. Suppose that a b = 0, and that b 0. Let c be the inverse of b. Multiplying the equation above by c on the left, we get 0

More information

THEODORE VORONOV DIFFERENTIABLE MANIFOLDS. Fall Last updated: November 26, (Under construction.)

THEODORE VORONOV DIFFERENTIABLE MANIFOLDS. Fall Last updated: November 26, (Under construction.) 4 Vector fields Last updated: November 26, 2009. (Under construction.) 4.1 Tangent vectors as derivations After we have introduced topological notions, we can come back to analysis on manifolds. Let M

More information

Definition 5.1. A vector field v on a manifold M is map M T M such that for all x M, v(x) T x M.

Definition 5.1. A vector field v on a manifold M is map M T M such that for all x M, v(x) T x M. 5 Vector fields Last updated: March 12, 2012. 5.1 Definition and general properties We first need to define what a vector field is. Definition 5.1. A vector field v on a manifold M is map M T M such that

More information

ab = c a If the coefficients a,b and c are real then either α and β are real or α and β are complex conjugates

ab = c a If the coefficients a,b and c are real then either α and β are real or α and β are complex conjugates Further Pure Summary Notes. Roots of Quadratic Equations For a quadratic equation ax + bx + c = 0 with roots α and β Sum of the roots Product of roots a + b = b a ab = c a If the coefficients a,b and c

More information

NATIONAL BOARD FOR HIGHER MATHEMATICS. M. A. and M.Sc. Scholarship Test. September 24, Time Allowed: 150 Minutes Maximum Marks: 30

NATIONAL BOARD FOR HIGHER MATHEMATICS. M. A. and M.Sc. Scholarship Test. September 24, Time Allowed: 150 Minutes Maximum Marks: 30 NATIONAL BOARD FOR HIGHER MATHEMATICS M. A. and M.Sc. Scholarship Test September 24, 2011 Time Allowed: 150 Minutes Maximum Marks: 30 Please read, carefully, the instructions on the following page 1 INSTRUCTIONS

More information

Check boxes of Edited Copy of Sp Topics (was 261-pilot)

Check boxes of Edited Copy of Sp Topics (was 261-pilot) Check boxes of Edited Copy of 10023 Sp 11 253 Topics (was 261-pilot) Intermediate Algebra (2011), 3rd Ed. [open all close all] R-Review of Basic Algebraic Concepts Section R.2 Ordering integers Plotting

More information

MA 460 Supplement: Analytic geometry

MA 460 Supplement: Analytic geometry M 460 Supplement: nalytic geometry Donu rapura In the 1600 s Descartes introduced cartesian coordinates which changed the way we now do geometry. This also paved for subsequent developments such as calculus.

More information

Appendix A: Matrices

Appendix A: Matrices Appendix A: Matrices A matrix is a rectangular array of numbers Such arrays have rows and columns The numbers of rows and columns are referred to as the dimensions of a matrix A matrix with, say, 5 rows

More information

CALC 3 CONCEPT PACKET Complete

CALC 3 CONCEPT PACKET Complete CALC 3 CONCEPT PACKET Complete Written by Jeremy Robinson, Head Instructor Find Out More +Private Instruction +Review Sessions WWW.GRADEPEAK.COM Need Help? Online Private Instruction Anytime, Anywhere

More information

MODEL ANSWERS TO THE FIRST HOMEWORK

MODEL ANSWERS TO THE FIRST HOMEWORK MODEL ANSWERS TO THE FIRST HOMEWORK 1. Chapter 4, 1: 2. Suppose that F is a field and that a and b are in F. Suppose that a b = 0, and that b 0. Let c be the inverse of b. Multiplying the equation above

More information

Extra Problems for Math 2050 Linear Algebra I

Extra Problems for Math 2050 Linear Algebra I Extra Problems for Math 5 Linear Algebra I Find the vector AB and illustrate with a picture if A = (,) and B = (,4) Find B, given A = (,4) and [ AB = A = (,4) and [ AB = 8 If possible, express x = 7 as

More information

Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems

Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems To locate a point in a plane, two numbers are necessary. We know that any point in the plane can be represented as an ordered pair (a, b) of real numbers, where a is the x-coordinate and b is the y-coordinate.

More information

Solutions to Math 51 First Exam October 13, 2015

Solutions to Math 51 First Exam October 13, 2015 Solutions to Math First Exam October 3, 2. (8 points) (a) Find an equation for the plane in R 3 that contains both the x-axis and the point (,, 2). The equation should be of the form ax + by + cz = d.

More information

An eightfold path to E 8

An eightfold path to E 8 An eightfold path to E 8 Robert A. Wilson First draft 17th November 2008; this version 29th April 2012 Introduction Finite-dimensional real reflection groups were classified by Coxeter [2]. In two dimensions,

More information

10. Smooth Varieties. 82 Andreas Gathmann

10. Smooth Varieties. 82 Andreas Gathmann 82 Andreas Gathmann 10. Smooth Varieties Let a be a point on a variety X. In the last chapter we have introduced the tangent cone C a X as a way to study X locally around a (see Construction 9.20). It

More information

Technische Universität München Zentrum Mathematik

Technische Universität München Zentrum Mathematik Technische Universität München Zentrum Mathematik Prof. Dr. Dr. Jürgen Richter-Gebert, Bernhard Werner Projective Geometry SS 2016 www-m10.ma.tum.de/projektivegeometriess16 Solutions for Worksheet 6 (2016-05-26)

More information

Linear Algebra. Preliminary Lecture Notes

Linear Algebra. Preliminary Lecture Notes Linear Algebra Preliminary Lecture Notes Adolfo J. Rumbos c Draft date April 29, 23 2 Contents Motivation for the course 5 2 Euclidean n dimensional Space 7 2. Definition of n Dimensional Euclidean Space...........

More information

Inversion Geometry on its head

Inversion Geometry on its head Inversion Geometry on its head Bibliography: Geometry revisited Coxeter & Greitzer MAA 1967 Introduction to Geometry Coxeter Wiley Classics 1961 Algebraic Projective Geometry Semple & Kneebone OUP 1952

More information

Two applications of the theorem of Carnot

Two applications of the theorem of Carnot Two applications of the theorem of Carnot Zoltán Szilasi Abstract Using the theorem of Carnot we give elementary proofs of two statements of C Bradley We prove his conjecture concerning the tangents to

More information

STEP Support Programme. STEP 2 Matrices Topic Notes

STEP Support Programme. STEP 2 Matrices Topic Notes STEP Support Programme STEP 2 Matrices Topic Notes Definitions............................................. 2 Manipulating Matrices...................................... 3 Transformations.........................................

More information

Linear Algebra. The analysis of many models in the social sciences reduces to the study of systems of equations.

Linear Algebra. The analysis of many models in the social sciences reduces to the study of systems of equations. POLI 7 - Mathematical and Statistical Foundations Prof S Saiegh Fall Lecture Notes - Class 4 October 4, Linear Algebra The analysis of many models in the social sciences reduces to the study of systems

More information

There are two things that are particularly nice about the first basis

There are two things that are particularly nice about the first basis Orthogonality and the Gram-Schmidt Process In Chapter 4, we spent a great deal of time studying the problem of finding a basis for a vector space We know that a basis for a vector space can potentially

More information

Mathematics 3210 Spring Semester, 2005 Homework notes, part 8 April 15, 2005

Mathematics 3210 Spring Semester, 2005 Homework notes, part 8 April 15, 2005 Mathematics 3210 Spring Semester, 2005 Homework notes, part 8 April 15, 2005 The underlying assumption for all problems is that all points, lines, etc., are taken within the Poincaré plane (or Poincaré

More information

- 1 - Items related to expected use of technology appear in bold italics.

- 1 - Items related to expected use of technology appear in bold italics. - 1 - Items related to expected use of technology appear in bold italics. Operating with Geometric and Cartesian Vectors Determining Intersections of Lines and Planes in Three- Space Similar content as

More information

M. Matrices and Linear Algebra

M. Matrices and Linear Algebra M. Matrices and Linear Algebra. Matrix algebra. In section D we calculated the determinants of square arrays of numbers. Such arrays are important in mathematics and its applications; they are called matrices.

More information

Introduction to Group Theory

Introduction to Group Theory Chapter 10 Introduction to Group Theory Since symmetries described by groups play such an important role in modern physics, we will take a little time to introduce the basic structure (as seen by a physicist)

More information

Overview of Complex Numbers

Overview of Complex Numbers Overview of Complex Numbers Definition 1 The complex number z is defined as: z = a+bi, where a, b are real numbers and i = 1. General notes about z = a + bi Engineers typically use j instead of i. Examples

More information

CUMBERLAND COUNTY SCHOOL DISTRICT BENCHMARK ASSESSMENT CURRICULUM PACING GUIDE

CUMBERLAND COUNTY SCHOOL DISTRICT BENCHMARK ASSESSMENT CURRICULUM PACING GUIDE CUMBERLAND COUNTY SCHOOL DISTRICT BENCHMARK ASSESSMENT CURRICULUM PACING GUIDE School: CCHS Subject: Algebra II Grade: 10 th Grade Benchmark Assessment 1 Instructional Timeline: 1 st Nine Weeks Topic(s):

More information

Representations of Sp(6,R) and SU(3) carried by homogeneous polynomials

Representations of Sp(6,R) and SU(3) carried by homogeneous polynomials Representations of Sp(6,R) and SU(3) carried by homogeneous polynomials Govindan Rangarajan a) Department of Mathematics and Centre for Theoretical Studies, Indian Institute of Science, Bangalore 560 012,

More information

B Elements of Complex Analysis

B Elements of Complex Analysis Fourier Transform Methods in Finance By Umberto Cherubini Giovanni Della Lunga Sabrina Mulinacci Pietro Rossi Copyright 21 John Wiley & Sons Ltd B Elements of Complex Analysis B.1 COMPLEX NUMBERS The purpose

More information

Definition: A vector is a directed line segment which represents a displacement from one point P to another point Q.

Definition: A vector is a directed line segment which represents a displacement from one point P to another point Q. THE UNIVERSITY OF NEW SOUTH WALES SCHOOL OF MATHEMATICS AND STATISTICS MATH Algebra Section : - Introduction to Vectors. You may have already met the notion of a vector in physics. There you will have

More information

MATH 320, WEEK 11: Eigenvalues and Eigenvectors

MATH 320, WEEK 11: Eigenvalues and Eigenvectors MATH 30, WEEK : Eigenvalues and Eigenvectors Eigenvalues and Eigenvectors We have learned about several vector spaces which naturally arise from matrix operations In particular, we have learned about the

More information

10. Noether Normalization and Hilbert s Nullstellensatz

10. Noether Normalization and Hilbert s Nullstellensatz 10. Noether Normalization and Hilbert s Nullstellensatz 91 10. Noether Normalization and Hilbert s Nullstellensatz In the last chapter we have gained much understanding for integral and finite ring extensions.

More information

MEI Core 1. Basic Algebra. Section 1: Basic algebraic manipulation and solving simple equations. Manipulating algebraic expressions

MEI Core 1. Basic Algebra. Section 1: Basic algebraic manipulation and solving simple equations. Manipulating algebraic expressions MEI Core Basic Algebra Section : Basic algebraic manipulation and solving simple equations Notes and Examples These notes contain subsections on Manipulating algebraic expressions Collecting like terms

More information

2. Intersection Multiplicities

2. Intersection Multiplicities 2. Intersection Multiplicities 11 2. Intersection Multiplicities Let us start our study of curves by introducing the concept of intersection multiplicity, which will be central throughout these notes.

More information

Introduction to Matrix Algebra

Introduction to Matrix Algebra Introduction to Matrix Algebra August 18, 2010 1 Vectors 1.1 Notations A p-dimensional vector is p numbers put together. Written as x 1 x =. x p. When p = 1, this represents a point in the line. When p

More information

Mathematics Standards for High School Precalculus

Mathematics Standards for High School Precalculus Mathematics Standards for High School Precalculus Precalculus is a rigorous fourth-year launch course that prepares students for college and career readiness and intended specifically for those students

More information

2. Preliminaries. x 2 + y 2 + z 2 = a 2 ( 1 )

2. Preliminaries. x 2 + y 2 + z 2 = a 2 ( 1 ) x 2 + y 2 + z 2 = a 2 ( 1 ) V. Kumar 2. Preliminaries 2.1 Homogeneous coordinates When writing algebraic equations for such geometric objects as planes and circles, we are used to writing equations that

More information

PHYS 705: Classical Mechanics. Rigid Body Motion Introduction + Math Review

PHYS 705: Classical Mechanics. Rigid Body Motion Introduction + Math Review 1 PHYS 705: Classical Mechanics Rigid Body Motion Introduction + Math Review 2 How to describe a rigid body? Rigid Body - a system of point particles fixed in space i r ij j subject to a holonomic constraint:

More information

Course Number 432/433 Title Algebra II (A & B) H Grade # of Days 120

Course Number 432/433 Title Algebra II (A & B) H Grade # of Days 120 Whitman-Hanson Regional High School provides all students with a high- quality education in order to develop reflective, concerned citizens and contributing members of the global community. Course Number

More information

Algebra I Fall 2007

Algebra I Fall 2007 MIT OpenCourseWare http://ocw.mit.edu 18.701 Algebra I Fall 007 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.701 007 Geometry of the Special Unitary

More information

Eigenvalues and Eigenvectors: An Introduction

Eigenvalues and Eigenvectors: An Introduction Eigenvalues and Eigenvectors: An Introduction The eigenvalue problem is a problem of considerable theoretical interest and wide-ranging application. For example, this problem is crucial in solving systems

More information

Congruence. Chapter The Three Points Theorem

Congruence. Chapter The Three Points Theorem Chapter 2 Congruence In this chapter we use isometries to study congruence. We shall prove the Fundamental Theorem of Transformational Plane Geometry, which states that two plane figures are congruent

More information

Linear Algebra V = T = ( 4 3 ).

Linear Algebra V = T = ( 4 3 ). Linear Algebra Vectors A column vector is a list of numbers stored vertically The dimension of a column vector is the number of values in the vector W is a -dimensional column vector and V is a 5-dimensional

More information

ELEMENTARY LINEAR ALGEBRA

ELEMENTARY LINEAR ALGEBRA ELEMENTARY LINEAR ALGEBRA K. R. MATTHEWS DEPARTMENT OF MATHEMATICS UNIVERSITY OF QUEENSLAND Second Online Version, December 1998 Comments to the author at krm@maths.uq.edu.au Contents 1 LINEAR EQUATIONS

More information

Chapter 8. Rigid transformations

Chapter 8. Rigid transformations Chapter 8. Rigid transformations We are about to start drawing figures in 3D. There are no built-in routines for this purpose in PostScript, and we shall have to start more or less from scratch in extending

More information

2009 Math Olympics Level II Solutions

2009 Math Olympics Level II Solutions Saginaw Valley State University 009 Math Olympics Level II Solutions 1. f (x) is a degree three monic polynomial (leading coefficient is 1) such that f (0) 3, f (1) 5 and f () 11. What is f (5)? (a) 7

More information

Pure Core 2. Revision Notes

Pure Core 2. Revision Notes Pure Core Revision Notes June 06 Pure Core Algebra... Polynomials... Factorising... Standard results... Long division... Remainder theorem... 4 Factor theorem... 5 Choosing a suitable factor... 6 Cubic

More information

This pre-publication material is for review purposes only. Any typographical or technical errors will be corrected prior to publication.

This pre-publication material is for review purposes only. Any typographical or technical errors will be corrected prior to publication. This pre-publication material is for review purposes only. Any typographical or technical errors will be corrected prior to publication. Copyright Pearson Canada Inc. All rights reserved. Copyright Pearson

More information

Precalculus. Precalculus Higher Mathematics Courses 85

Precalculus. Precalculus Higher Mathematics Courses 85 Precalculus Precalculus combines the trigonometric, geometric, and algebraic techniques needed to prepare students for the study of calculus, and strengthens students conceptual understanding of problems

More information

Topic 1: Fractional and Negative Exponents x x Topic 2: Domain. 2x 9 2x y x 2 5x y logg 2x 12

Topic 1: Fractional and Negative Exponents x x Topic 2: Domain. 2x 9 2x y x 2 5x y logg 2x 12 AP Calculus BC Summer Packet Name INSTRUCTIONS: Where applicable, put your solutions in interval notation. Do not use any calculator (ecept on Topics 5:#5 & :#6). Please do all work on separate paper and

More information

Linear Algebra. Min Yan

Linear Algebra. Min Yan Linear Algebra Min Yan January 2, 2018 2 Contents 1 Vector Space 7 1.1 Definition................................. 7 1.1.1 Axioms of Vector Space..................... 7 1.1.2 Consequence of Axiom......................

More information

Basic facts and definitions

Basic facts and definitions Synopsis Thursday, September 27 Basic facts and definitions We have one one hand ideals I in the polynomial ring k[x 1,... x n ] and subsets V of k n. There is a natural correspondence. I V (I) = {(k 1,

More information

4.2. ORTHOGONALITY 161

4.2. ORTHOGONALITY 161 4.2. ORTHOGONALITY 161 Definition 4.2.9 An affine space (E, E ) is a Euclidean affine space iff its underlying vector space E is a Euclidean vector space. Given any two points a, b E, we define the distance

More information

The Fast Fourier Transform

The Fast Fourier Transform The Fast Fourier Transform 1 Motivation: digital signal processing The fast Fourier transform (FFT) is the workhorse of digital signal processing To understand how it is used, consider any signal: any

More information

Contents. 1 Vectors, Lines and Planes 1. 2 Gaussian Elimination Matrices Vector Spaces and Subspaces 124

Contents. 1 Vectors, Lines and Planes 1. 2 Gaussian Elimination Matrices Vector Spaces and Subspaces 124 Matrices Math 220 Copyright 2016 Pinaki Das This document is freely redistributable under the terms of the GNU Free Documentation License For more information, visit http://wwwgnuorg/copyleft/fdlhtml Contents

More information

Devil s Staircase Rotation Number of Outer Billiard with Polygonal Invariant Curves

Devil s Staircase Rotation Number of Outer Billiard with Polygonal Invariant Curves Devil s Staircase Rotation Number of Outer Billiard with Polygonal Invariant Curves Zijian Yao February 10, 2014 Abstract In this paper, we discuss rotation number on the invariant curve of a one parameter

More information

Inversion in the Plane. Part II: Radical Axes 1

Inversion in the Plane. Part II: Radical Axes 1 BERKELEY MATH CIRCLE, October 18 1998 Inversion in the Plane. Part II: Radical Axes 1 Zvezdelina Stankova-Frenkel, UC Berkeley Definition 2. The degree of point A with respect to a circle k(o, R) is defined

More information

Abstract & Applied Linear Algebra (Chapters 1-2) James A. Bernhard University of Puget Sound

Abstract & Applied Linear Algebra (Chapters 1-2) James A. Bernhard University of Puget Sound Abstract & Applied Linear Algebra (Chapters 1-2) James A. Bernhard University of Puget Sound Copyright 2018 by James A. Bernhard Contents 1 Vector spaces 3 1.1 Definitions and basic properties.................

More information

IDEAL CLASSES AND SL 2

IDEAL CLASSES AND SL 2 IDEAL CLASSES AND SL 2 KEITH CONRAD Introduction A standard group action in complex analysis is the action of GL 2 C on the Riemann sphere C { } by linear fractional transformations Möbius transformations:

More information

College Prep Algebra III Course #340. Course of Study. Findlay City School

College Prep Algebra III Course #340. Course of Study. Findlay City School College Prep Algebra III Course #340 Course of Study Findlay City School Algebra III Table of Contents 1. Findlay City Schools Mission Statement and Beliefs 2. Algebra III Curriculum Map 3. Algebra III

More information

4.1 Distance and Length

4.1 Distance and Length Chapter Vector Geometry In this chapter we will look more closely at certain geometric aspects of vectors in R n. We will first develop an intuitive understanding of some basic concepts by looking at vectors

More information

2016 EF Exam Texas A&M High School Students Contest Solutions October 22, 2016

2016 EF Exam Texas A&M High School Students Contest Solutions October 22, 2016 6 EF Exam Texas A&M High School Students Contest Solutions October, 6. Assume that p and q are real numbers such that the polynomial x + is divisible by x + px + q. Find q. p Answer Solution (without knowledge

More information

(Refer Slide Time: 2:08 min)

(Refer Slide Time: 2:08 min) Applied Mechanics Prof. R. K. Mittal Department of Applied Mechanics Indian Institute of Technology, Delhi Lecture No. 11 Properties of Surfaces (Contd.) Today we will take up lecture eleven which is a

More information

A2 HW Imaginary Numbers

A2 HW Imaginary Numbers Name: A2 HW Imaginary Numbers Rewrite the following in terms of i and in simplest form: 1) 100 2) 289 3) 15 4) 4 81 5) 5 12 6) -8 72 Rewrite the following as a radical: 7) 12i 8) 20i Solve for x in simplest

More information

Lecture 10: A (Brief) Introduction to Group Theory (See Chapter 3.13 in Boas, 3rd Edition)

Lecture 10: A (Brief) Introduction to Group Theory (See Chapter 3.13 in Boas, 3rd Edition) Lecture 0: A (Brief) Introduction to Group heory (See Chapter 3.3 in Boas, 3rd Edition) Having gained some new experience with matrices, which provide us with representations of groups, and because symmetries

More information

Chapter 5 Eigenvalues and Eigenvectors

Chapter 5 Eigenvalues and Eigenvectors Chapter 5 Eigenvalues and Eigenvectors Outline 5.1 Eigenvalues and Eigenvectors 5.2 Diagonalization 5.3 Complex Vector Spaces 2 5.1 Eigenvalues and Eigenvectors Eigenvalue and Eigenvector If A is a n n

More information

Curvilinear coordinates

Curvilinear coordinates C Curvilinear coordinates The distance between two points Euclidean space takes the simplest form (2-4) in Cartesian coordinates. The geometry of concrete physical problems may make non-cartesian coordinates

More information

Chapter 7. Linear Algebra: Matrices, Vectors,

Chapter 7. Linear Algebra: Matrices, Vectors, Chapter 7. Linear Algebra: Matrices, Vectors, Determinants. Linear Systems Linear algebra includes the theory and application of linear systems of equations, linear transformations, and eigenvalue problems.

More information

Course Name - Strategic Math - Algebra 2

Course Name - Strategic Math - Algebra 2 1 of22 HPS Sem. 1 Sept. Algebraic Language Writing algebraic expressionsl1.2.1 Use mathematical symbols to MA.9-12.A-SSE.1.a represent quantitative relationships and 1. Interpret expressions that represent

More information

Some Notes on Linear Algebra

Some Notes on Linear Algebra Some Notes on Linear Algebra prepared for a first course in differential equations Thomas L Scofield Department of Mathematics and Statistics Calvin College 1998 1 The purpose of these notes is to present

More information

Glossary. Glossary Hawkes Learning Systems. All rights reserved.

Glossary. Glossary Hawkes Learning Systems. All rights reserved. A Glossary Absolute value The distance a number is from 0 on a number line Acute angle An angle whose measure is between 0 and 90 Acute triangle A triangle in which all three angles are acute Addends The

More information

Geometry in the Complex Plane

Geometry in the Complex Plane Geometry in the Complex Plane Hongyi Chen UNC Awards Banquet 016 All Geometry is Algebra Many geometry problems can be solved using a purely algebraic approach - by placing the geometric diagram on a coordinate

More information

1 Matrices and vector spaces

1 Matrices and vector spaces Matrices and vector spaces. Which of the following statements about linear vector spaces are true? Where a statement is false, give a counter-example to demonstrate this. (a) Non-singular N N matrices

More information

Coordinates for Projective Planes

Coordinates for Projective Planes Chapter 8 Coordinates for Projective Planes Math 4520, Fall 2017 8.1 The Affine plane We now have several eamples of fields, the reals, the comple numbers, the quaternions, and the finite fields. Given

More information

The Circle. 1 The equation of a circle. Nikos Apostolakis. Spring 2017

The Circle. 1 The equation of a circle. Nikos Apostolakis. Spring 2017 The Circle Nikos Apostolakis Spring 017 1 The equation of a circle Definition 1. Given a point C in the plane and a positive number r, the circle with center C and radius r, is the locus of points that

More information

LINEAR EQUATIONS WITH UNKNOWNS FROM A MULTIPLICATIVE GROUP IN A FUNCTION FIELD. To Professor Wolfgang Schmidt on his 75th birthday

LINEAR EQUATIONS WITH UNKNOWNS FROM A MULTIPLICATIVE GROUP IN A FUNCTION FIELD. To Professor Wolfgang Schmidt on his 75th birthday LINEAR EQUATIONS WITH UNKNOWNS FROM A MULTIPLICATIVE GROUP IN A FUNCTION FIELD JAN-HENDRIK EVERTSE AND UMBERTO ZANNIER To Professor Wolfgang Schmidt on his 75th birthday 1. Introduction Let K be a field

More information

8. Prime Factorization and Primary Decompositions

8. Prime Factorization and Primary Decompositions 70 Andreas Gathmann 8. Prime Factorization and Primary Decompositions 13 When it comes to actual computations, Euclidean domains (or more generally principal ideal domains) are probably the nicest rings

More information

We simply compute: for v = x i e i, bilinearity of B implies that Q B (v) = B(v, v) is given by xi x j B(e i, e j ) =

We simply compute: for v = x i e i, bilinearity of B implies that Q B (v) = B(v, v) is given by xi x j B(e i, e j ) = Math 395. Quadratic spaces over R 1. Algebraic preliminaries Let V be a vector space over a field F. Recall that a quadratic form on V is a map Q : V F such that Q(cv) = c 2 Q(v) for all v V and c F, and

More information

Part IB GEOMETRY (Lent 2016): Example Sheet 1

Part IB GEOMETRY (Lent 2016): Example Sheet 1 Part IB GEOMETRY (Lent 2016): Example Sheet 1 (a.g.kovalev@dpmms.cam.ac.uk) 1. Suppose that H is a hyperplane in Euclidean n-space R n defined by u x = c for some unit vector u and constant c. The reflection

More information

Mobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti

Mobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti Mobile Robotics 1 A Compact Course on Linear Algebra Giorgio Grisetti SA-1 Vectors Arrays of numbers They represent a point in a n dimensional space 2 Vectors: Scalar Product Scalar-Vector Product Changes

More information