discuss how to describe points, lines and planes in 3 space.


 Collin Bates
 1 years ago
 Views:
Transcription
1 Chapter 2 3 Space: lines and planes In this chapter we discuss how to describe points, lines and planes in 3 space. introduce the language of vectors. discuss various matters concerning the relative position of lines and planes: in particular, intersections and angles. show how to translate and rotate lines and planes in relatively simple cases space and vectors Coordinatizing 3 space The physical world around us is called three dimensional because through any point precisely three mutually perpendicular axes (and no more) can pass. Such axes can be used to describe points in 3space by triples of numbers: the (signed) distances to planes formed by two of these axes. We call such a set of perpendicular axes through a given point a cartesian coordinate system. Each of the axes is called a coordinate axis and the point of intersection of the three axes is called the origin. Every point of a coordinate axis corresponds to a real number. As you know, often, but not necessarily always, these axes are called x axis, y axis and z axis. By convention, we agree that the coordinate system be right handed: if you turn the positive x axis to the positive y axis, then a screw positioned along the z axis following this movement would move in the direction of the positive z axis. Every two of the three axes span a plane: the y,z plane, the x,z plane and the x,y plane, respectively. The signed distances u, v and w of a point P in 3 space to each of these planes in the given order are the coordinates of P; we usually put the three together as follows: (u,v,w) and still call this the coordinates of P. Notations for points that occur often: (x,y,z), (x 1,x 2,x 3 ), (a,b,c), (a 1,a 2,a 3 ), (u,v,w), etc. For a point P with coordinates (x, y, z) we also write P = (x, y, z). 21
2 22 3 Space: lines and planes z z P=(2,3,4) y 4 y x x Figure 2.1: Left: The third coordinate of P = (2, 3, 4) is the signed distance of P to the x,y plane; so if the point were below the x,y plane, the third coordinate would have been negative. Right: Cartesian coordinate systems are taken to be right handed: a screw positioned along the z axis moves along the positive z axis if turned in the direction from x axis to y axis. Most discussions in this chapter will focus on 3 space. But with some adaptations (usually simplifications) the material makes sense in 2 space Example. Given the point P with coordinates (x,y,z), the point Q with coordinates (x,y, 0) is the vertical projection of P on the x,y plane. The distance between P and Q is z (note the absolute value). Similarly, the (horizontal) projection on the y, z plane has coordinates (0, y, z). Finally, the horizontal projection on the x, z plane has coordinates (x, 0,z) Vectors: introduction In dealing with geometric issues, it is convenient to have the language of vectors at our disposal. A vector is a quantity with both magnitude and direction. A well known example z E p=op B C F O y A AB D x Figure 2.2: The arrow with tail at A and head at B represents a vector (left). The two arrows CD and EF have the same length and direction, and so represent the same vector (middle). A vector with tail in the origin is usually represented by a lower case boldface symbol, like p (right).
3 2.1 3 space and vectors 23 of a vector occurring in physics is velocity. A vector is usually represented by an arrow in the sense of a directed line segment; the length of the segment corresponds to the magnitude. For us the most important aspects about the notion of a vector are summarized below. For points A and B in 2 space or 3 space, the vector AB is an arrow with tail at A and head at B. (It points from A to B.) Only its magnitude (length) and its direction are of importance, not the positioning of the arrow in the following sense: if two vectors have the same length and the same direction, we consider them equal. The representing arrows need not coincide at all (but one can be translated so that it coincides with the other). The vectors in 3 space with tail at the origin O = (0, 0, 0) and head at a point P = (x,y,z) play a special role. Such a vector OP is usually represented by the corresponding lower case boldface symbol, say p. If we want to emphasize the vector aspect, we write p = (x,y,z) rather than P = (x,y,z). The very scrupulous reader may find this abuse of notation a bit distressing. In practice, it almost never goes wrong. To assign coordinates/numbers to any vector, say the vector AB with A = (a 1,a 2,a 3 ) and B = (b 1,b 2,b 3 ), we note that this vector can also be represented by the arrow with tail at the origin and head at (b 1 a 1,b 2 a 2,b 3 a 3 ). And so we write AB = (b 1 a 1,b 2 a 2,b 3 a 3 ). In 3 space with a cartesian coordinate system, we single out three special vectors, the so called standard basis vectors. e 1 with tail at (0, 0, 0) and head at (1, 0, 0) (the standard basis vector of length 1 along the positive x axis). e 2 with tail at (0, 0, 0) and head at (0, 1, 0) (the standard basis vector of length 1 along the positive y axis). e 3 with tail at (0, 0, 0) and head at (0, 0, 1) (the standard basis vector of length 1 along the positive z axis). In the following, vectors usually refer to directed segments with their tail in the origin, unless the context suggests otherwise. Also note that in writing on paper or on the blackboard the use of boldface symbols is not convenient. So we also use notations like x = (x 1,x 2,x 3 ), where we underline the symbol for a vector The first arithmetic with vectors We can do a kind of arithmetic with vectors that turns out to make sense in the context of geometry.
4 24 3 Space: lines and planes Scalar multiplication and vector addition. Vectors can be added and multiplied by scalars in the following way: x + y = (x 1 + y 1,x 2 + y 2,x 3 + y 3 ) λx = (λx 1,λx 2,λx 3 ) addition, sum of vectors scalar multiplication. So the first coordinate of the sum of two vectors is the sum of the first coordinates of the two vectors, etc. By combining these operations more complicated vectors can be built from two or more vectors. For instance, x y is the sum of x and ( 1) y; the vector 2x + 3y is obtained by multiplying x by 2 and y by 3 and adding the results. The operations addition and scalar multiplication have a clear geometric meaning. To add two vectors u and v, here is what happens geometrically. Move v, say, so v u+v u u 2u Figure 2.3: Vector addition (left) and scalar multiplication (right). that its tail coincides with the head of u. The new vector whose tail is at the tail of u and whose head is at the head of v represents the sum. A scalar multiple of u is obtained by changing the length of the vector u by the scalar factor, say λ. If λ > 0 then the direction remains the same, but if λ < 0, then the direction of the vector is reversed. The zero vector. A vector with length 0 is called the zero vector. It is usually denoted by 0. In coordinates: 0 = (0, 0, 0). If we multiply any vector p by 0, we get 0, i.e., 0 p = 0. Adding 0 to any other vector q yields q again. In symbols: 0 + q = q. Also, q q = 0 fo any vector q. Using the standard basis vectors. In terms of the standard basis vectors, any vector x = (x 1,x 2,x 3 ) can be written as a combination of them in a straightforward way: x 1 e 1 +x 2 e 2 +x 1 e 1. This is not the moment to understand in which situations this is useful. Length. The length p of a vector p = (a,b,c) is a2 + b 2 + c 2.
5 2.1 3 space and vectors 25 Of course, this is the same as the distance from the origin to the point with coordinates (a, b, c). The squared length is therefore p 2 = a 2 + b 2 + c 2 z P=(a,b,c) O y (a,0,0) x Q=(a,b,0) Figure 2.4: The length of a vector with end point at (a,b,c) is computed by repeated application of Pythagoras theorem: OQ = a 2 + b 2 and OP = OQ 2 + QP 2 = a2 + b 2 + c 2. Dot or inner product. The dot product or inner product of two vectors x = (x 1,x 2,x 3 ) and y = (y 1,y 2,y 3 ) is defined as x y = x 1 y 1 + x 2 y 2 + x 3 y 3. This product does not have an immediate geometric interpretation, but will show up indirectly in various geometrically relevant situations, such as the length of a vector and the angle between two vectors: For instance, the squared length of x equals the inner product of x with itself: x 2 = x x x 2 3 = x 1 x 1 + x 2 x 2 + x 3 x 3 = x x. Distance. The distance between the points P = (x, y, z) and Q = (a, b, c) is (x a)2 + (y b) 2 + (z c) 2 ; it equals the length of the line segment PQ. We define the distance between two vectors as the distance between their endpoints, or, equivalently, the length of the difference. The distance between p = (x, y, z) and q = (a, b, c) is p q = (x a,y b,z c) = (x a) 2 + (y b) 2 + (z c) 2.
6 26 3 Space: lines and planes Perpendicular vectors. Using the dot product we can express when two vectors are perpendicular. It turns out (no proof here) that this is the case precisely if their inner product equals 0: x y = 0 x and y perpendicular. Angle between vectors. If a and b are nonzero vectors, then the angle α between them is usually computed via its cosine. From the Cosine Law the following relation involving the dot product can be shown to hold: cosα = a b a b. To determine the angle, two steps are therefore required: first determine the cosine of the angle using the formula above, then solve for the angle. In the case of perpendicular vectors, the angle between them is 90 or π/2 radians, since cosα = 0 in this case Examples. Here are a few examples of the notions mentioned above. a) 2 (3, 4, 1) 3 (2, 5, 1) = (0, 7, 5). b) The length of the vector p = (1, 2, 3) is p = = 14. c) The inner product of the vectors (1, 2, 3) and (2, 1, 4) is (1, 2, 3) (2, 1, 4) = ( 1) = 12. d) The vectors (1, 1, 2) and (2, 2, 2) are perpendicular since their inner product equals 0:: (1, 1, 2) (2, 2, 2) = ( 2) = 0. e) To compute the angle α between the vectors (1, 1, 0) and (1, 2, 1) we first determine cosα: 3 3 cosα = = 4 3 (1, 1, 0) (1, 2, 1) (1, 1, 0) (1, 2, 1) = = = Hence the angle is 30 or π/6 radians. 2.2 Describing lines and planes Describing a line by a vector parametric equation To specify a line l in 3 space, it suffices to give a point, say P 0, through which the line passes and to give the line s direction, say given by the nonzero vector v. In terms of vectors, start with a vector p 0 with end point P 0 and add an arbitrary scalar multiple λv of v to it: p 0 + λv. 3 2.
7 2.2 Describing lines and planes 27 As λ ranges from to, all vectors with endpoints on the line can be obtained. Not surprisingly, the vector v is called a direction vector of the line. Of course, any nonzero multiple of v can be used as direction vector, i.e., p 0 + µ(3v) describes the same line. Here is an example. The line l passing through P 0 = (2, 3, 5) and having direction vector a + λv a v Figure 2.5: Parametric description of a line involves a support vector p 0 and a nonzero direction vector v. The line can be seen as resting on p 0. Any vector on the line is obtained by adding a suitable multiple of v to p 0. x = (1, 1, 2) is (2, 3, 5) + λ(1, 1, 2). Any value of λ produces a specific vector or point on the line. For instance, for λ = 2, we get (2, 3, 5) + 2 (1, 1, 2) = (4, 1, 9). An arbitrary point on the line can be described as Two remarks are in place here. (2 + λ, 3 λ, 5 + 2λ). Any vector on the line can be taken as the support vector. So, for example, (4, 1, 9)+ µ(1, 1, 2) is the same line, since (4, 1, 9) is on the line. Any two direction vectors differ by a (nonzero) multiple. This means that, for example, (2, 3, 5) + ρ( 2, 2, 4) is the same line Describing a plane in 3 space by an equation Suppose V is a plane in 3 space. If you want to explain to someone else which plane it is, it suffices to give the following information: a point P 0 through which the plane passes, and its direction. Well, a plane contains many directions. Now a smart move is to specify the plane s direction by giving the direction of a line which is perpendicular to the plane. The direction of that line can be given by a single nonzero vector (the length of the vector is irrelevant, only its direction matters). Let us translate this into mathematics, first in an example and then in the general case.
8 28 3 Space: lines and planes Here is the example. Suppose the point P 0 = (2, 1, 3) is on the plane U, and let p 0 = OP 0 be the vector pointing to P 0. Suppose moreover that n = (1, 2, 2) is perpendicular to U. Our task is to catch those vectors x = (x,y,z) whose endpoints are in the plane. Now such an endpoint is in U if the direction from P 0 to this endpoint is perpendicular to n. This direction is represented by the vector x p 0 = (x 2,y 1,z 3). So we require that this vector is perpendicular to n = (1, 2, 2), i.e., (x 2,y 1,z 3) (1, 2, 2) = 0 or (x 2) + 2 (y 1) + 2 (z 3) = 0. Of course, we can rewrite this equation as x + 2y + 2z = 10, but the form x 2 + 2(y 1) + 2(z 3) = 0 shows clearly that (2, 1, 3) is on the plane. Please note that: For any nonzero real number t, the equation tx+2ty +2tz = 10t represents the same equation. For example, 6x + 12y + 12z = 60. The coefficients of x, y, z in the equation x + 2y + 2z = 10 form a vector which is (a multiple of) the vector n = (1, 2, 2) perpendicular to the plane we started with. This is not a coincidence as we will see below. The coordinates of P 0 satisfy the equation: = 10. The last two items provide an easy check on the correctness of the equation obtained. z n p 0 P 0 x P x y Figure 2.6: Given a point P 0 in the plane and a vector n perpendicular to the plane, any vector x satisfying (x p 0 ) n = 0 has its endpoint on the plane. Here is the general story. Let P 0 = (x 0,y 0,z 0 ) be a point and let n = (a,b,c) be a nonzero vector, which we suppose to be perpendicular to the plane we wish to describe. The plane through P and perpendicular to n consists of all the points P = (x,y,z) such that P 0 P is perpendicular to n, i.e., (x x 0,y y 0,z z 0 ) is perpendicular to (a,b,c). In vector form: (a,b,c) (x x 0,y y 0,z z 0 ) = 0,
9 2.2 Describing lines and planes 29 and in equation form (i.e., we have expanded the inner product): Of course, an equivalent form is a(x x 0 ) + b(y y 0 ) + c(z z 0 ) = 0. ax + by + cz = ax 0 + by 0 + cz 0 (the right hand side is a constant). If we replace the right hand side by a single symbol, the general form of the equation of a plane becomes: ax + by + cz = d. The vector (a,b,c) is a vector perpendicular to the plane Definition. (The point normal equation of a plane) The plane which passes through the point P 0 = (x 0,y 0,z 0 ) and which is perpendicular to the direction of the nonzero vector n = (a,b,c) has equation a(x x 0 ) + b(y y 0 ) + c(z z 0 ) = 0. In vector form, the equation is usually written as: n (x p 0 ) = 0, where the vector p 0 corresponds to the point P Remark. Suppose the plane U has equation 2x 1 3x 2 + 5x 3 = 10. If you multiply every coefficient by the same number, say 5, then the resulting equation, 10x 1 15x 2 +25x 3 = 50, describes U as well. In practice we usually (but not necessarily) multiply the coefficients by a number so that the equation looks more pleasant. For instance, 3 2 x x x 3 = 9 2 looks better in the form 45x 1 42x 2 +20x 3 = 135 (where the first mentioned equation has been multiplied through by 30). What we just said is one instance of the fact that we are free to change the appearance of an equation according to our specific needs, as long as we do not violate any mathematical rule. Here are a few examples of the equation 2x 1 3x 2 + 5x 3 = 10 in different guises: 2x 1 3x 2 + 5x 3 10 = 0 (all terms on one side). x 2 = 2 3 x x (the second variable has been isolated on one side). 2(x 1 1) 3(x 2 + 1) + 5(x 3 1) = 0 shows clearly that (1, 1, 1) is on the plane.
10 30 3 Space: lines and planes Vector parametric description of a plane A second way of representing a plane is by a vector parametric equation, similar to the description of a line. First we discuss an example, where we use an intuitive approach, then we turn to a general strategy. So here is the first example. Suppose the plane passes through (0, 0, 0), like for instance the plane U given by x 1 + 2x 2 3x 3 = 0. Take two vectors in the plane which are not multiples of each other (they point in really distinct directions), for example (2, 1, 0) and (3, 0, 1). Geometrically, it is quite clear that any vector in the plane can be obtained by taking suitable multiples of the vectors and then adding them, see Figure (2.7). b a x b a p Figure 2.7: Left hand side: If U is a plane through the origin and a and b are two vectors which are not along the same line, then any vector x in the plane can be obtained by adding suitable multiples of a and b. Right hand side: the ingredients of a vector parametric description of a plane: a vector p with endpoint in the plane and two direction vectors a and b. For varying λ and µ, the vector p + λa + µb runs through all vectors of the plane. So, any vector in the plane can be written as a so called linear combination λ(2, 1, 0) + µ(3, 0, 1) of (2, 1, 0) and (3, 0, 1) for suitable λ and µ. Note that in this way we have described the vectors/points of the plane explicitly: every value of λ and µ produces a point in the plane. For example, for λ = 2 and µ = 3 we find 2 (2, 1, 0) 3 (3, 0, 1) = ( 5, 2, 3). Next we turn to a general strategy for finding vector parametric descriptions. The way to find a vector parametric equation of a plane, starting from an equation, is to solve the equation (and don t forget: there are infinitely many solutions; it s a plane after all). Suppose V is the plane with equation x 1 + 2x 2 3x 3 = 4; so V is parallel to U, since the vector (1, 2, 3) is perpendicular to both planes. If we rewrite this equation as x 1 = 2x 2 + 3x 3 + 4, then we clearly see, that for any given values of x 2 and x 3, there is exactly one value of x 1 such that the triple (x 1,x 2,x 3 ) is on the plane. So assign the value λ to x 2 and µ to x 3. Then x 1 = 4 2λ + 3µ. We gain more insight in the solutions if we
11 2.2 Describing lines and planes 31 rewrite this explicit description in vector notation as follows: (x 1,x 2,x 3 ) = (4 2λ + 3µ,λ,µ) = (4, 0, 0) + λ( 2, 1, 0) + µ(3, 0, 1). In this case you see (again) that V is parallel to U (how?). The vector (4, 0, 0) is usually called a support vector, while ( 2, 1, 0) and (3, 0, 1) are called direction vectors. To summarize: we have described two ways of finding a vector parametric description of a plane: Solve an equation describing the plane and rewrite the solutions in the form p+λa+ µb. Find any vector p whose endpoint is in the plane and find two vectors a and b representing two independent directions in the plane. Then the endpoints of p + λa + µb for varying λ and µ run through all points of the plane. Please note that the same plane can be described by parametric descriptions which may look quite different. See the following example. Equations of a plane, however, show less variation: if we only consider equations of the form ax + by + cz = d, then two such equations describe the same plane precisely when the coefficients differ by a common (nonzero) multiple Example. To find a vector parametric description of the plane U : x + y + z = 4, any of the following approaches can be taken. a) We solve for x, so we first rewrite the equation as x = 4 y z. If we let y = λ and z = µ, then x = 4 λ µ. In vector form this becomes: (x,y,z) = (4 λ µ,λ,µ) = (4, 0, 0) + λ( 1, 1, 0) + µ( 1, 0, 1). Intuitively: the plane rests on (4, 0, 0) and an arbitrary vector in the plane is described by adding any combination of the vectors ( 1, 1, 0) and ( 1, 0, 1) to (4, 0, 0). b) Alternatively, by looking carefully at the equation, pick any vector in U, say (1, 1, 2). We use this as the support vector. Now take any two vectors which are perpendicular to (1, 1, 1) and which are not multiples of one another ( independent vectors ), i.e., pick two independent solutions of u+v+w = 0, e.g., (2, 3, 5) and (1, 1, 2). Then the plane is described as (x,y,z) = (1, 1, 2) + λ(2, 3, 5) + µ(1, 1, 2). Note that this description is quite different from the previous description. This illustrates the fact that vector parametric descriptions of planes are far from being unique. Incidentally, in picking two vectors perpendicular to (1, 1, 1), usually two vectors are chosen which look relatively simple, like (1, 1, 0) and (0, 1, 1). Our choice of vectors (2, 3, 5) and (1, 1, 2) is correct, but may be more cumbersome in computations. The advantage of the method in a) is that it produces relatively simple vectors.
12 32 3 Space: lines and planes c) If you follow the method as explained in a) but solve for y rather than for x, then the resulting parametric equation is again somewhat different: start by rewriting the equation as y = 4 x z, then set x = λ and z = µ. Finally, you get (x,y,z) = (0, 4, 0) + λ(1, 1, 0) + µ(0, 1, 1) Transforming vector parametric descriptions into equations We have come across two ways of representing planes: by equations and by parametric descriptions. To go from the first to the second comes down to solving an equation representing a plane and rewriting the solutions in the appropriate vector form. This has been discussed above. Here, we discuss how to transform vector parametric descriptions into equations. This is best illustrated by an example. Suppose the plane U is given by (2, 1, 3) + λ(1, 2, 1) + µ(1, 1, 3). Our task is to find an equation of the form ax + by + cz = d representing U, i.e., we have to find a, b, c and d. This problem splits in two parts: To find a, b and c note that the vector (a,b,c) is perpendicular to the plane, so is perpendicular to both direction vectors (1, 2, 1) and (1, 1, 3). This means: So we have to solve: (a,b,c) (1, 2, 1) = 0, and (a,b,c) (1, 1, 3) = 0. a + 2b + c = 0 a + b + 3c = 0 for a, b and c. To solve this system of two equations, first eliminate a from the second equation by subtracting the first equation from the second: a + 2b + c = 0 b + 2c = 0. To make the first equation simpler, add twice the new second equation to the first: a + 5c = 0 b + 2c = 0. So a = 5c and b = 2c. So one vector perpendicular to (1, 2, 1) and (1, 1, 3) is, for example, the vector (a,b,c) = ( 5, 2, 1) obtained by taking c = 1. Thus, our equation looks like 5x + 2y + z = d and it remains to find d. To find d in 5x + 2y + z = d is simple: just substitute any vector of U, for instance the support vector (2, 1, 3): ( 5) = d. In conclusion, an equation for U is 5x + 2y + z = 5.
13 2.3 The relative position of lines and planes: intersections The relative position of lines and planes: intersections Intersecting a line and a plane The intersection of a line and a plane usually consists of exactly one point. If the line and the plane happen to be parallel, the intersection may be empty or consist of the whole line. How does this work out in actual computations? Here is an example. Suppose the plane U has equation 2x 1 + 3x 2 5x 3 = 3, and the line l has the parametric description ( 1, 4, 3) + λ(1, 1, 2). Finding the intersection comes down to finding the value(s) of λ for which the corresponding point on l belongs to U as well, i.e., satisfies the equation for U. So we substitute ( 1 + λ, 4 λ, 3 + 2λ) in the equation: 2( 1 + λ) + 3(4 λ) 5( 3 + 2λ) = 3. This reduces to 25 11λ = 3, so that λ = 2. Now substitute this value of λ in the parametric equation of l to find the point of intersection: (1, 2, 1) Intersecting two planes Geometrically it is clear that in general two planes meet along a line. This is how you find that line of intersection explicitly. Suppose U and V are two planes, with equations x 1 + 2x 2 x 3 = 4 and 2x 1 + x 2 5x 3 = 2, respectively. We have to solve both equations simultaneously, i.e., find all (x 1,x 2,x 3 ) which satisfy both equations. To manipulate the equations efficiently, we write them as follows: x 1 + 2x 2 x 3 = 4 2x 1 + x 2 5x 3 = 2. Subtract the first equation 2 times from the second (and leave the first equation as it is): x 1 + 2x 2 x 3 = 4 3x 2 3x 3 = 6. Divide the resulting second equation by 3 to obtain: x 1 + 2x 2 x 3 = 4 x 2 + x 3 = 2. Now get rid of x 2 in the first equation by subtracting the new second equation from the first: x 1 + 3x 3 = 0 x 2 + x 3 = 2. In this stage, both x 1 and x 2 can be expressed in terms of x 3 : to see this more clearly, rewrite as follows: x 1 = 3x 3 x 2 = x
14 34 3 Space: lines and planes Assign an arbitrary value λ to x 3, then we get (x 1,x 2,x 3 ) = (3λ, λ + 2,λ) = (0, 2, 0) + λ(3, 1, 1), the parametric description of a line with direction vector (3, 1, 1). 2.4 The relative position of lines and planes: angles The angle between two lines It is fairly straightforward to define the angle between two lines l and m, although one detail has to be taken care of. a) Take a vector a in the direction of l and a vector b in the direction of m; Figure 2.8: To find the angle between two lines, choose vectors in the direction of the lines. b) Then compute the angle between a and b using the inner product; if the angle between a and b is obtuse, then replace a by a and compute the angle between a and b. In other words, make sure you end up with an acute angle (between 0 and 90 ). To summarize: If a and b are vectors in the directions of the lines l and m, resp., then the angle α between l and m is computed from cos α = a b a b. By using the absolute value in the numerator, we ensure that the fraction is non negative. Consequently, we get an acute angle Example. The line l is given by (2, 7, 1)+λ(1, 0, 1) and the line m is given by (2, π, 1)+ µ(1, 1, 2). To compute the angle α between l and m we take a = (1, 0, 1) and b = (1, 1, 2) (the other vectors occurring in the parametric descriptions are irrelevant), and solve α from cos α = (1, 0, 1) (1, 1, 2) (1, 0, 1) (1, 1, 2) = 3 = = 3 2.
15 2.4 The relative position of lines and planes: angles 35 Therefore, the angle is 60. Note that in this example, the absolute value signs in (1, 0, 1) (1, 1, 2) were not necessary. This would have been different if, for example, the line l were given by (2, 7, 1) + λ( 1, 0, 1) The angle between a line and a plane You quickly realize that it is not so clear what the angle between a line l and a plane U should be precisely. The reason is that the plane contains so many directions. So, which direction of the plane should we compare with the direction of the line? Suppose l and U meet in a point P. The plane U contains a whole family of lines through P. Any such line makes an angle with l, and the angle with such a line in the plane varies as this line varies in the family. (There is one case where the angle does not change, do you see which case this is?) l P U l β m P U Figure 2.9: To find the angle between a line and a plane, it is convenient to introduce a line perpendicular to the plane as in the right hand picture. If β is the angle between these two lines, then the angle between the line and the plane is defined to be 90 β. But there is a way out if you realize that the direction of a plane is also characterized by the direction of a line perpendicular to the plane. So, let us take a line m through P which is perpendicular to the plane U. Of course, we know what the angle between l and m is. Now this angle is of course not really what we want, but 90 minus this angle turns out to be the appropriate angle. Summarizing: to compute the angle α between the line l with direction a and the plane U with vector n perpendicular to U, solve α from sin α = a n a n, and make sure to take α in the range between 0 and 90. (Note the occurrence of sin: with cos we would find the angle between l and the line perpendicular to U, but we need 90 minus this angle.) Here, we are using cos(90 α) = sin α Example. To compute the angle between the line l with parametric equation (2, 0, 3) + λ(0, 1, 1) and the plane U with equation x 1 +x 2 +2x 3 = 13, we need the vector a = (0, 1, 1)
16 36 3 Space: lines and planes from the parametric description and we need a vector perpendicular to the plane U, for example, n = (1, 1, 2) (taken from the coefficients in the plane s equation). Now we can proceed in two ways: (a) Solve for α in sin α = Therefore the angle is 60. a n a n = = (b) Or we compute the angle β between a and n from cos β = a n a n = = 3 2, 3 2. so that β = 30. The angle between l and U is then = The angle between two planes Suppose U and V are two planes which meet along the line l. Of course they seem to make a definite angle with each other, but it takes some thinking to realize that an appropriate way to make precise what this angle is, is to look at the angle between two vectors perpendicular to the two planes (cf. the case of a line and a plane). The only detail you have to be careful about is that you may have to replace one of the vectors by its opposite vector in order to guarantee that the angle is acute (at most 90 ), or introduce an absolute value just like we did in the case of a line and plane. To summarize: To compute the angle between the planes U and V, take a nonzero vector u perpendicular to U and a nonzero vector v perpendicular to V. The angle α between U and V is then computed from cos α = u v u v. Figure 2.10: To find the angle between two planes, determine the angle between two vectors perpendicular to the two planes, respectively. In particular, two planes are perpendicular if the two vectors perpendicular to the planes are perpendicular.
17 2.5 The relative position of points, lines and planes: distances Example. Consider the planes U and V with equations x 1 x 3 = 7 and x 1 x 2 2x 3 = 25, respectively. To compute the angle between the two planes, consider the vector (1, 0, 1) which is perpendicular to U and the vector (1, 1, 2) which is perpendicular to V. Both vectors are taken from the coefficients of the two equations. Now solve α from cos α = Therefore the angle is 30. (1, 0, 1) (1, 1, 2) (1, 0, 1) (1, 1, 2) = = The angle between two planes: alternative approach Here is another approach to compute the angle between two intersecting planes U and V. Fix a point P on the line of intersection l of U and V. Then take a plane W through P which is perpendicular to l. Now W meets U along a line m, and W meets V along a line n. Finally, compute the angle between the lines m and n. W 3 2. P α α m n Figure 2.11: An alternative approach to computing the angle between two intersecting planes U and V. The picture shows the cross section with the plane W perpendicular to the line of intersection l of U and V. The intersection of U and W is denoted by m; the intersection of V and W is denoted by n. To see why this approach leads to the same answer, just realize that normal vectors to U and V with their tails at P lie in W. This is illustrated in Fig. (2.11). This approach usually leads to more complicated computations. 2.5 The relative position of points, lines and planes: distances Computing the distance between two points is straightforward, but the computation of distances between points and lines, points and planes, etc., is more subtle. In this section we are not aiming for explicit formulas, but for strategies to compute distances The distance between a point and a line Suppose P is a point and l is a line. The distance between P and a point on l varies as this
18 38 3 Space: lines and planes point varies through l. So the question is: for which point on l is this distance minimal? A bit of experimentation with the Theorem of Pythagoras (see Fig. (2.12)) shows that the point Q such that PQ is perpendicular to l is the point we are looking for. P l Q R Figure 2.12: The shortest distance between a point P and a point on the line l occurs for the point Q where PQ is perpendicular to the line l. For instance, applying Pythagoras theorem to the triangle PQR, shows that PQ < PR. Let us work this out in a specific example, where P is the point (7, 2, 3) and l is the line with parametric description (2, 1, 1) + λ(1, 1, 2). First, an arbitrary point Q on l is described by (2 + λ, 1 λ, 1 + 2λ). The vector QP is then ( 5 + λ, 3 λ, 4 + 2λ). The next step is to solve λ from the condition that PQ is perpendicular to the direction vector (1, 1, 2) of l, i.e., solve ( 5 + λ, 3 λ, 4 + 2λ) (1, 1, 2) = 0. This comes down to ( 5+λ) ( 3 λ)+2(4+2λ) = 0 or 6+6λ = 0. So λ = 1 and the point Q on l closest to P is therefore (substitute for example in the parametric equation of l) (2, 1, 1) (1, 1, 2) = (1, 0, 1). Finally, the distance between P and the line l is calculated as the distance between P and Q: (7 1)2 + (2 0) 2 + ( 3 1) 2 = 44 = The distance between a point and a plane The distance between a point P and a point Q in the plane U varies as Q varies. By the distance between a point P and a plane U we mean the shortest possible distance between P and any of the points of U. But how do we find such a point in the plane? It is geometrically obvious that we can locate such a point by moving from P in the direction perpendicular to U until we meet U, i.e., we need the line through P perpendicular to the plane. Just as in the previous case (2.5.2), this is based on Pythagoras theorem. Let us use this strategy when P is the point (5, 4, 6) and U is the plane given by the equation x 2y + 2z = 7.
19 2.5 The relative position of points, lines and planes: distances 39 P locate Q distance U Q Figure 2.13: To find the distance between P and the plane U, first determine the intersection of U and the line through P perpendicular to U. Then calculate the distance between P and the point of intersection. First we look for the line through P and perpendicular to U. A vector perpendicular to U is easily extracted from its equation: (1, 2, 2). So the line through P with direction vector (1, 2, 2) is described by (5, 4, 6) + λ(1, 2, 2). The next step is to intersect this line with U. We substitute (5, 4, 6) + λ(1, 2, 2) in the equation: (5 + λ) 2( 4 2λ) + 2(6 + 2λ) = 7. This is easily rewritten as λ = 7, so that λ = 2. For λ = 2, we find the point Q = (5, 4, 6) 2(1, 2, 2) = (3, 0, 2). So Q = (3, 0, 2) is the point in U closest to P. Finally, the distance between P and U is calculated as the distance between P and Q: (5 3) 2 + ( 4 0) 2 + (6 2) 2 = 36 = 6. There is at least one but: what if the plane is given by a vector parametric equation? Then there are a couple of strategies available: One strategy is to find an equation for the plane and then proceed as before. An alternative is to find a point Q in the plane such that the direction of PQ is perpendicular to every direction in U The distance between two non intersecting lines If two distinct lines in 3 space do not intersect (in one point), then it makes sense to consider their distance. In case the lines are parallel, i.e., their direction vectors are multiples of each other, you take a point on one line and compute the distance to the other line, just like before. In case the direction vectors are not multiples of one another, the computation of the distance turns out to be more complicated. Suppose l and m are two such lines. Among all points P on l and all points Q on m we need to find points for which the distance PQ is
20 40 3 Space: lines and planes minimal. As before, this condition turns out to be related to right angles: we have to find P on l and Q on m in such a way that PQ is perpendicular to both l and m. The procedure is best illustrated with an example. Suppose l is given by (1, 3, 3)+λ(2, 0, 1) and m is given l m Figure 2.14: To find the distance between two non intersecting lines l and m, find vectors p on l and q on m such that q p is perpendicular to both l and m. The distance is the length q p of q p. by (1, 6, 3) +µ(0, 1, 1). The lines are certainly not parallel since the direction vectors are not multiples of one another. They may intersect, but then our distance computation will simply yield 0. So begin by taking a vector p = (1, 3, 3) + λ(2, 0, 1) = (1 + 2λ, 3, 3 + λ) on l and taking q = (1, 6, 3) + µ(0, 1, 1) = (1, 6 + µ, 3 + µ) on m. Here are the steps to follow: First, we impose the condition that q p be perpendicular to (2, 0, 1) and (0, 1, 1) (the direction vectors of l and m, respectively), i.e., (q p) (2, 0, 1) = 0 and (q p) (0, 1, 1) = 0. Since q p = ( 2λ, 3+µ, 6+µ λ), these conditions translate into two equations in the variables λ and µ: Rearranging leads to 4λ + ( 6 + µ λ) = 0 (3 + µ) + ( 6 + µ λ) = 0. 5λ + µ = 6 λ + 2µ = 3. An easy calculation shows that this system has exactly one solution: λ = 1 and µ = 1. Second, corresponding to this solution we find the vector p = ( 1, 3, 2) and q = (1, 7, 2). The distance between the lines is therefore (1 1)2 + (7 3) 2 + ( 2 2) 2 = 36 = 6.
21 2.6 Geometric operations: translating lines and planes Geometric operations: translating lines and planes In designing shapes, you may want to move or rotate certain elements of your design, say a window or a wall. The question is how you do this mathematically. Such mathematical computations are at the basis of computer software implementations. In this section, we will briefly discuss a few aspects of translations Translating lines A fairly simple geometric operation is translation: we move an object in a given direction over a given distance. We can represent this direction and distance by a vector t. Any vector (x,y,z) is moved to another vector, given by (x,y,z) + t. If t = (2, 5, 1), then (x,y,z) is translated to (x+2,y+5,z 1). No problem here. Now let us turn to the effect of a translation on lines. If a line l is given by p + λv, then a translation over t produces again a line, now with parametric equation (p+t)+λv. For example, if l is given by (1, 0, 6)+λ(1, 1, 1), then after translation over t = (2, 5, 1) we obtain the line with support vector (1, 0, 6)+(2, 5, 1) = (3, 5, 5) and direction vector (1, 1, 1), i.e., the line (3, 5, 5) + λ(1, 1, 1). Of course, if you translate a line then the resulting line is parallel with the line you started with Translating planes Next, we investigate what happens to a plane when we translate that plane over a vector, say t = (2, 5, 1) as before. Since we have two standard ways of representing a plane, our discussion splits into two parts. In the case where the plane is described by a vector parametric description, the situation is similar to the case of a line. Suppose the plane U is described by (1, 1, 0) + λ(1, 1, 0) + µ(0, 4, 1), then the effect of translating over t = (2, 5, 1) is: (2, 5, 1) + (1, 1, 0) + λ(1, 1, 0) + µ(0, 4, 1) or (3, 4, 1) + λ(1, 1, 0) + µ(0, 4, 1). Suppose the plane U is given by its equation x y +4z = 2. The trouble now is that the description of the plane is implicit. If we translate a vector (x,y,z) of U then the result is (2, 5, 1)+(x,y,z), but then what? Instead, it is better to work backwards: we begin with a vector from the translated plane, say (u,v,w). If we translate this vector back, so subtract (2, 5, 1), then the result should be on U, i.e., satisfy the equation of U. So we substitute (u,v,w) (2, 5, 1) = (u 2,v 5,w + 1) in the equation of U: (u 2) (v 5) + 4(w + 1) = 2. This reduces to u v + 4w = 5. So the plane U : x y + 4z = 2 is translated to the plane V : x y + 4z = 5. Of course, as the equations show, these planes are parallel. Note that the distance between the planes is not equal to the length of t, since t is not perpendicular to U.
22 42 3 Space: lines and planes If you realize that translating a plane produces a parallel plane, another strategy suggests itself: the translated plane must have an equation of the form x y + 4z = d for some d. Now, d can be determined by translating a single point of U and substituting the result. For instance, (2, 0, 0) is in U. Translating the point gives (2, 0, 0) + (2, 5, 1) = (4, 5, 1). Substitute (4, 5, 1) in the equation x y + 4z = d to find that d = Example. (Armada s Paleiskwartier, Den Bosch) Close to the railway station in Den Bosch, a number of buildings with a special appearance have been built during the last few years. From a distance they look somewhat like a fleet of ships from historic times, which is probably the reason for the district s name: Armada s Paleiskwartier. Now some of the buildings are identical in shape (or at least at first glance), so they could be considered as translates of one given central building. Apart from details, we can consider this problem as a problem of translating planes (to be fair: the shape of the buildings is not flat on all sides, so our discussion is limited to flat parts only). Suppose one wall is given by (part of) the plane U : x 3y = 0 (the zdirection is assumed to point upward) in some cartesian coordinate system. Suppose we want to translate this plane over (2, 3, 0), (3, 2, 0) and (5, 5, 0), resulting in the planes U 1, U 2 and U 3. To find the equation of the first translate, we work backwards again: start with (u,v,w) in U 1, then (u,v,w) (2, 3, 0) is in U, so (u 2,v 3,w) should satisfy (u 2) 3(v 3) = 0. A bit of simplification yields U 1 : u 3v = 7. The other translates are found in a similar way. The result is: U 1 : u 3v = 7, U 2 : u 3v = 3, U 3 : u 3v = 10. (In this case, a simpler strategy works faster, since you know that a) all equations should have the form x 3y =... and b) (2, 3, 0) is on U 1, etc.) 2.7 Geometric operations: rotating lines and planes Rotation around a coordinate axis If you rotate the point (x,y,z) around the z axis over 90, the result is either ( y,x,z) or (y, x,z) depending on the direction of orientation (the two rotations are each other s inverse: if you apply the two rotations after each other, the total effect will be that nothing has happened). So rotating a single point isn t that difficult. But suppose we want to rotate a whole plane. Let us concentrate on the first rotation where any point (x,y,z) is rotated to ( y,x,z). What would be the equation or parametric description of the new plane? Let us consider these two descriptions separately. Let U be the plane given by the parametric description (0, 2, 1) + λ(2, 1, 0) + µ(1, 2, 5). So an arbitrary vector in U looks like (2λ + µ, 2 λ + 2µ, 1 5µ). If we rotate this vector we obtain the vector ( 2 + λ 2µ, 2λ + µ, 1 5µ)
23 2.7 Geometric operations: rotating lines and planes 43 y ( y,x) 90 o (x,y) x Figure 2.15: A rotation around the z axis doesn t affect the z coordinate; the figure concentrates on what happens to the x and y coordinates. (apply (x,y,z) ( y,x,z)). This provides us with the vector parametric equation of the rotated plane: ( 2, 0, 1) + λ(1, 2, 0) + µ( 2, 1, 5). Now suppose we want to rotate the plane U, but U is given through its equation x + 2y + z = 5. After rotation we get a new plane, which we call V. If we take a point (x,y,z) of V and rotate it back, then we should get a point of U. Now, rotating (x,y,z) back produces the point (y, x,z) (since the two rotations are inverses). This point (y, x,z) should satisfy the equation of U, so y + 2( x) + z = 5. Rewriting, we get the equation 2x + y + z = 5. A clever approach to rotating the plane U : x + 2y + z = 5 is to rotate its normal vector (1, 2, 1). This results in the vector ( 2, 1, 1) and this should be the normal vector of the rotated plane V. So V has equation 2x + y + z = d for some d. Now, the point (0, 0, 5) belongs to U and remains fixed when we apply the rotation. Substituting (0, 0, 5) in 2x + y + z = d gives d = 5. Therefore, the equation of V is 2x + y + z = 5. (There are more ways to argue that the right hand side coefficient 5 of the equation of U does not change for the rotation at hand.) Example. If we rotate the plane U with equation x+2y +z = 5 around the z axis in the positive direction, we obtain the plane V with equation 2x + y + z = 5. Now suppose we rotate V again over 90 in the same direction to obtain a plane W. If (x,y,z) is a point of W, then rotating it backwards we find the point (y, x,z), a point that belongs to V, so should satisfy the equation of V. This leads to 2y + ( x) + z = 5, or x 2y + z = 5. This equation looks pretty much like the equation x + 2y + z = 5 of U. Can you explain the minus signs by directly considering a rotation over 180?
24 44 3 Space: lines and planes Fans of planes If two planes U and V are not parallel, they intersect along a line, say l. By rotating one of the planes around l, we generate a whole family of planes, the fan of planes on l. Of course, all these planes contain l. The question is: what is the equation of a member of such a fan, given the equations of U and V? To answer this question, we take two specific planes, say U: x y z = 1 and V : 3x y + 3z = 9. These planes turn out to intersect along l: (1, 0, 2) + λ(2, 3, 1) (this follows from a computation similar to the one explained on p. 33). Now every point of l satisfies both equations, but then every point of l also satisfies a combination of the two equations, like 2 times the first equation + 3 times the second equation, or 2(x y z) + 3(3x y + 3z) = 2 ( 1) + 3 9, which simplifies to 11x 5y + 7z = 25. Likewise, any combination a(x y z) + b(3x y + 3z) = a + 9b (with a and b not both equal to 0) is an equation of a plane that contains l. Sometimes, it is more convenient in this setting to bring all terms of the equation of a plane to the left hand side: x y z + 1 = 0 and 3x y + 3z 9 = 0 describe the two planes and a(x y z + 1) + b(3x y + 3z 9) = 0 is, for fixed a and b, the equation of a specific member of the fan Example. The planes U : x + y = 1 and V : x + 2y = 3 generate a fan. In this example we compute a member of the fan which is perpendicular to U. The equation of a general member of the fan is a(x + y 1) + b(x + 2y 3) = 0, or (a + b)x + (a + 2b)y a 3b = 0. The plane with equation (a + b)x + (a + 2b)y a 3b = 0 is perpendicular to U if (a + b,a + 2b, 0) (1, 1, 0) = 0. This reduces to 2a + 3b = 0. For a = 3 and b = 2 we obtain the plane with equation x y + 3 = 0. (Other non zero values of a and b which satisfy 2a + 3b = 0 lead to a multiple of this equation and therefore to the same plane.) 2.8 Geometric operations: reflecting lines and planes Mirror images Sometimes you do not even notice it at first sight when two (types of) buildings have been constructed as mirror images of one another: they look so much the same. Sizes, distances and angles are preserved under reflections, but somehow the orientation has
25 2.8 Geometric operations: reflecting lines and planes 45 changed: a right handed screw changes into a left handed screw. One immediate property of a reflection is that taking the mirror image of the mirror image of a point or vector brings you back to the original point or vector. In this section we restrict our discussion to reflections where the mirror is a coordinate plane or another easy to describe plane. In particular, the mirrors we consider are flat. To begin with, we consider the reflection in the y,z plane. In this case, the mirror image z ( x,z) (x,z) Figure 2.16: In picturing the reflection in the y,z plane, we have left out the y axis for the sake of simplicity and pictured the resulting 2 d version of reflection in the z axis. The vector (x,z) is reflected into the vector ( x,z). The two chairs are mirror images. of any point (x,y,z) (or vector) is ( x,y,z) (so only the x coordinate has changed). The image of a line, say l : (2, 3, 4) +λ(1, 2, 3), is obtained by reflecting each point of the line individually: so the image of l is l : ( 2, 3, 4) + λ( 1, 2, 3). To be precise, every vector (2+λ, 3+2λ, 4+3λ) of l is mapped to ( 2 λ, 3+2λ, 4+3λ) = ( 2, 3, 4)+λ( 1, 2, 3). Similarly, the image of a plane given in parametric form is easily obtained. But if the plane is given by an equation, we have to work backwards again, just like we did for translations and rotations, or exploit a normal vector of the plane. Suppose the plane U has equation 2x + y 3z = 7. If (u,v,w) is a vector on the mirror image U of U, then reflecting the vector yields the vector ( u,v,w) in U, so ( u,v,w) satisfies the equation of U: 2( u) + v 3w = 7. Therefore, U has equation 2u + v 3w = 7. If the mirror is not a coordinate plane, like the y,z plane, things usually become a lot harder to describe. In later chapters we will go into this in more detail. In the case at hand, the normal vector approach runs as follows. Reflect the normal vector (2, 1, 3) to obtain ( 2, 1, 3), so the equation of U is of the form 2x+y 3z = d for some d. To determine d, pick a point on U, say (2, 3, 0) and reflect it: ( 2, 3, 0). Substitute in 2x + y 3z = d and conclude that d = 7. The equation of U is therefore 2x + y 3z = More reflections The case where the mirror is the plane x = y is still easy to handle. The mirror image of x
26 46 3 Space: lines and planes the vector (x,y,z) is easily seen to be (y,x,z) (just change the first two coordinates). From this description in coordinates it is also clear that reflecting twice in succession brings you back to where you started: (x,y,z) reflect (y,x,z) reflect (x, y, z) Example. Two towers of a building are designed as mirror images. Both towers have triangular horizontal cross sections. In a suitable coordinate system, the walls of one of these towers are (parts of) the three planes U : x = 3, V : y x = 1 and W : y = 1. The mirror is the plane x = y. To find the mirror image of the plane V : y x = 1, start with a vector (u,v,w) on the mirror image V of the plane. The mirror image of the vector (u,v,w) is (v,u,w) and is on the plane V : y x = 1, so u v = 1. Therefore the equation of the mirror image V of the plane V : y x = 1 is x y = 1 (so the roles of x and y have been interchanged). Here is an overview of the equations of the planes and their mirror images. planes mirror images x = 3 y = 3 y x = 1 x y = 1 y = 1 x = More complicated reflections A straightforward observation on reflections in a plane U is the following. If you connect a point P and its mirror image P, then this segment is perpendicular to the plane U. Moreover, the distance from P to U is equal to the distance from P to U. This translates into the following strategy for computing mirror images: a) Start at P and move along the line through P which is perpendicular to U, so first set up the parametric equation of this line, say p + λv. b) Compute the intersection of the line with the plane U. For a specific value of the parameter, say λ 0, the line intersects the plane U. So p + λ 0 v is halfway between P and its mirror image. c) But then p + 2λ 0 v is the mirror image (note the factor 2). By way of example let us compute the mirror image P of the point P = (2, 3, 4) in the plane x+y+z = 3. The idea is to start moving from P in the direction (1, 1, 1) (perpendicular to U). Then locate on this line the second point which has the same distance to U. Let l be the line (2, 3, 4) + λ(1, 1, 1). First find out where this line meets the plane U, so substitute in x + y + z = 3: (2 + λ) + (3 + λ) + (4 + λ) = 3. This equation reduces easily to 9 + 3λ = 3 so that λ = 2. So if you add 2 (1, 1, 1) to (2, 3, 4) you end up in the plane. Therefore, if you go twice as far you will get the mirror
THREE DIMENSIONAL GEOMETRY
Chapter 8 THREE DIMENSIONAL GEOMETRY 8.1 Introduction In this chapter we present a vector algebra approach to three dimensional geometry. The aim is to present standard properties of lines and planes,
More informationSection 1.1. Introduction to R n
The Calculus of Functions of Several Variables Section. Introduction to R n Calculus is the study of functional relationships and how related quantities change with each other. In your first exposure to
More informationFURTHER VECTORS (MEI)
Mathematics Revision Guides Further Vectors (MEI) (column notation) Page of MK HOME TUITION Mathematics Revision Guides Level: AS / A Level  MEI OCR MEI: C FURTHER VECTORS (MEI) Version : Date: 97 Mathematics
More informationA vector is a directed line segment used to represent a vector quantity.
Chapters and 6 Introduction to Vectors A vector quantity has direction and magnitude. There are many examples of vector quantities in the natural world, such as force, velocity, and acceleration. A vector
More information13.4 THE CROSS PRODUCT
710 Chapter Thirteen A FUNDAMENTAL TOOL: VECTORS 62. Use the following steps and the results of Problems 59 60 to show (without trigonometry) that the geometric and algebraic definitions of the dot product
More informationVector Algebra CHAPTER 13. Ü13.1. Basic Concepts
CHAPTER 13 ector Algebra Ü13.1. Basic Concepts A vector in the plane or in space is an arrow: it is determined by its length, denoted and its direction. Two arrows represent the same vector if they have
More informationAdding vectors We can do arithmetic with vectors. We ll start with vector addition and related operations. Suppose you have two vectors
1 Chapter 13. VECTORS IN THREE DIMENSIONAL SPACE Let s begin with some names and notation for things: R is the set (collection) of real numbers. We write x R to mean that x is a real number. A real number
More information1.3. DOT PRODUCT 19. 6. If θ is the angle (between 0 and π) between two nonzero vectors u and v,
1.3. DOT PRODUCT 19 1.3 Dot Product 1.3.1 Definitions and Properties The dot product is the first way to multiply two vectors. The definition we will give below may appear arbitrary. But it is not. It
More informationOne advantage of this algebraic approach is that we can write down
. Vectors and the dot product A vector v in R 3 is an arrow. It has a direction and a length (aka the magnitude), but the position is not important. Given a coordinate axis, where the xaxis points out
More information11.1. Objectives. Component Form of a Vector. Component Form of a Vector. Component Form of a Vector. Vectors and the Geometry of Space
11 Vectors and the Geometry of Space 11.1 Vectors in the Plane Copyright Cengage Learning. All rights reserved. Copyright Cengage Learning. All rights reserved. 2 Objectives! Write the component form of
More information9 Multiplication of Vectors: The Scalar or Dot Product
Arkansas Tech University MATH 934: Calculus III Dr. Marcel B Finan 9 Multiplication of Vectors: The Scalar or Dot Product Up to this point we have defined what vectors are and discussed basic notation
More informationGeometry of Vectors. 1 Cartesian Coordinates. Carlo Tomasi
Geometry of Vectors Carlo Tomasi This note explores the geometric meaning of norm, inner product, orthogonality, and projection for vectors. For vectors in threedimensional space, we also examine the
More informationFigure 1.1 Vector A and Vector F
CHAPTER I VECTOR QUANTITIES Quantities are anything which can be measured, and stated with number. Quantities in physics are divided into two types; scalar and vector quantities. Scalar quantities have
More informationTwo vectors are equal if they have the same length and direction. They do not
Vectors define vectors Some physical quantities, such as temperature, length, and mass, can be specified by a single number called a scalar. Other physical quantities, such as force and velocity, must
More informationUnified Lecture # 4 Vectors
Fall 2005 Unified Lecture # 4 Vectors These notes were written by J. Peraire as a review of vectors for Dynamics 16.07. They have been adapted for Unified Engineering by R. Radovitzky. References [1] Feynmann,
More informationSolving Simultaneous Equations and Matrices
Solving Simultaneous Equations and Matrices The following represents a systematic investigation for the steps used to solve two simultaneous linear equations in two unknowns. The motivation for considering
More informationSection 9.5: Equations of Lines and Planes
Lines in 3D Space Section 9.5: Equations of Lines and Planes Practice HW from Stewart Textbook (not to hand in) p. 673 # 35 odd, 237 odd, 4, 47 Consider the line L through the point P = ( x, y, ) that
More informationLecture L3  Vectors, Matrices and Coordinate Transformations
S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3  Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between
More informationMathematics 205 HWK 6 Solutions Section 13.3 p627. Note: Remember that boldface is being used here, rather than overhead arrows, to indicate vectors.
Mathematics 205 HWK 6 Solutions Section 13.3 p627 Note: Remember that boldface is being used here, rather than overhead arrows, to indicate vectors. Problem 5, 13.3, p627. Given a = 2j + k or a = (0,2,
More informationEquations Involving Lines and Planes Standard equations for lines in space
Equations Involving Lines and Planes In this section we will collect various important formulas regarding equations of lines and planes in three dimensional space Reminder regarding notation: any quantity
More informationCHAPTER FIVE. 5. Equations of Lines in R 3
118 CHAPTER FIVE 5. Equations of Lines in R 3 In this chapter it is going to be very important to distinguish clearly between points and vectors. Frequently in the past the distinction has only been a
More informationSection 1.4. Lines, Planes, and Hyperplanes. The Calculus of Functions of Several Variables
The Calculus of Functions of Several Variables Section 1.4 Lines, Planes, Hyperplanes In this section we will add to our basic geometric understing of R n by studying lines planes. If we do this carefully,
More informationSolutions to Exercises, Section 5.1
Instructor s Solutions Manual, Section 5.1 Exercise 1 Solutions to Exercises, Section 5.1 1. Find all numbers t such that ( 1 3,t) is a point on the unit circle. For ( 1 3,t)to be a point on the unit circle
More information1.5 Equations of Lines and Planes in 3D
40 CHAPTER 1. VECTORS AND THE GEOMETRY OF SPACE Figure 1.16: Line through P 0 parallel to v 1.5 Equations of Lines and Planes in 3D Recall that given a point P = (a, b, c), one can draw a vector from
More information1 VECTOR SPACES AND SUBSPACES
1 VECTOR SPACES AND SUBSPACES What is a vector? Many are familiar with the concept of a vector as: Something which has magnitude and direction. an ordered pair or triple. a description for quantities such
More informationMATH 304 Linear Algebra Lecture 24: Scalar product.
MATH 304 Linear Algebra Lecture 24: Scalar product. Vectors: geometric approach B A B A A vector is represented by a directed segment. Directed segment is drawn as an arrow. Different arrows represent
More informationLINES AND PLANES IN R 3
LINES AND PLANES IN R 3 In this handout we will summarize the properties of the dot product and cross product and use them to present arious descriptions of lines and planes in three dimensional space.
More informationVectors, Gradient, Divergence and Curl.
Vectors, Gradient, Divergence and Curl. 1 Introduction A vector is determined by its length and direction. They are usually denoted with letters with arrows on the top a or in bold letter a. We will use
More informationVectors 2. The METRIC Project, Imperial College. Imperial College of Science Technology and Medicine, 1996.
Vectors 2 The METRIC Project, Imperial College. Imperial College of Science Technology and Medicine, 1996. Launch Mathematica. Type
More informationChapter 5 Polar Coordinates; Vectors 5.1 Polar coordinates 1. Pole and polar axis
Chapter 5 Polar Coordinates; Vectors 5.1 Polar coordinates 1. Pole and polar axis 2. Polar coordinates A point P in a polar coordinate system is represented by an ordered pair of numbers (r, θ). If r >
More informationMath Review. for the Quantitative Reasoning Measure of the GRE revised General Test
Math Review for the Quantitative Reasoning Measure of the GRE revised General Test www.ets.org Overview This Math Review will familiarize you with the mathematical skills and concepts that are important
More informationMath 241, Exam 1 Information.
Math 241, Exam 1 Information. 9/24/12, LC 310, 11:1512:05. Exam 1 will be based on: Sections 12.112.5, 14.114.3. The corresponding assigned homework problems (see http://www.math.sc.edu/ boylan/sccourses/241fa12/241.html)
More informationL 2 : x = s + 1, y = s, z = 4s + 4. 3. Suppose that C has coordinates (x, y, z). Then from the vector equality AC = BD, one has
The line L through the points A and B is parallel to the vector AB = 3, 2, and has parametric equations x = 3t + 2, y = 2t +, z = t Therefore, the intersection point of the line with the plane should satisfy:
More informationVECTOR ALGEBRA. 10.1.1 A quantity that has magnitude as well as direction is called a vector. is given by a and is represented by a.
VECTOR ALGEBRA Chapter 10 101 Overview 1011 A quantity that has magnitude as well as direction is called a vector 101 The unit vector in the direction of a a is given y a and is represented y a 101 Position
More information2. THE xy PLANE 7 C7
2. THE xy PLANE 2.1. The Real Line When we plot quantities on a graph we can plot not only integer values like 1, 2 and 3 but also fractions, like 3½ or 4¾. In fact we can, in principle, plot any real
More informationMathematics Notes for Class 12 chapter 10. Vector Algebra
1 P a g e Mathematics Notes for Class 12 chapter 10. Vector Algebra A vector has direction and magnitude both but scalar has only magnitude. Magnitude of a vector a is denoted by a or a. It is nonnegative
More information12.5 Equations of Lines and Planes
Instructor: Longfei Li Math 43 Lecture Notes.5 Equations of Lines and Planes What do we need to determine a line? D: a point on the line: P 0 (x 0, y 0 ) direction (slope): k 3D: a point on the line: P
More informationSection 8.8. 1. The given line has equations. x = 3 + t(13 3) = 3 + 10t, y = 2 + t(3 + 2) = 2 + 5t, z = 7 + t( 8 7) = 7 15t.
. The given line has equations Section 8.8 x + t( ) + 0t, y + t( + ) + t, z 7 + t( 8 7) 7 t. The line meets the plane y 0 in the point (x, 0, z), where 0 + t, or t /. The corresponding values for x and
More informationDecember 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS
December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in twodimensional space (1) 2x y = 3 describes a line in twodimensional space The coefficients of x and y in the equation
More informationReview Sheet for Test 1
Review Sheet for Test 1 Math 26100 2 6 2004 These problems are provided to help you study. The presence of a problem on this handout does not imply that there will be a similar problem on the test. And
More informationCOLLEGE ALGEBRA. Paul Dawkins
COLLEGE ALGEBRA Paul Dawkins Table of Contents Preface... iii Outline... iv Preliminaries... Introduction... Integer Exponents... Rational Exponents... 9 Real Exponents...5 Radicals...6 Polynomials...5
More informationC relative to O being abc,, respectively, then b a c.
2 EPProgram  Strisuksa School  Roiet Math : Vectors Dr.Wattana Toutip  Department of Mathematics Khon Kaen University 200 :Wattana Toutip wattou@kku.ac.th http://home.kku.ac.th/wattou 2. Vectors A
More informationSolutions to old Exam 1 problems
Solutions to old Exam 1 problems Hi students! I am putting this old version of my review for the first midterm review, place and time to be announced. Check for updates on the web site as to which sections
More information... ... . (2,4,5).. ...
12 Three Dimensions ½¾º½ Ì ÓÓÖ Ò Ø ËÝ Ø Ñ So far wehave been investigatingfunctions ofthe form y = f(x), withone independent and one dependent variable Such functions can be represented in two dimensions,
More information3. INNER PRODUCT SPACES
. INNER PRODUCT SPACES.. Definition So far we have studied abstract vector spaces. These are a generalisation of the geometric spaces R and R. But these have more structure than just that of a vector space.
More information9.4. The Scalar Product. Introduction. Prerequisites. Learning Style. Learning Outcomes
The Scalar Product 9.4 Introduction There are two kinds of multiplication involving vectors. The first is known as the scalar product or dot product. This is socalled because when the scalar product of
More informationVector Algebra II: Scalar and Vector Products
Chapter 2 Vector Algebra II: Scalar and Vector Products We saw in the previous chapter how vector quantities may be added and subtracted. In this chapter we consider the products of vectors and define
More informationSection 13.5 Equations of Lines and Planes
Section 13.5 Equations of Lines and Planes Generalizing Linear Equations One of the main aspects of single variable calculus was approximating graphs of functions by lines  specifically, tangent lines.
More informationLinear Algebra Notes for Marsden and Tromba Vector Calculus
Linear Algebra Notes for Marsden and Tromba Vector Calculus ndimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of
More informationJUST THE MATHS UNIT NUMBER 8.5. VECTORS 5 (Vector equations of straight lines) A.J.Hobson
JUST THE MATHS UNIT NUMBER 8.5 VECTORS 5 (Vector equations of straight lines) by A.J.Hobson 8.5.1 Introduction 8.5. The straight line passing through a given point and parallel to a given vector 8.5.3
More informationLINEAR ALGEBRA W W L CHEN
LINEAR ALGEBRA W W L CHEN c W W L Chen, 1982, 2008. This chapter originates from material used by author at Imperial College, University of London, between 1981 and 1990. It is available free to all individuals,
More information6. Vectors. 1 20092016 Scott Surgent (surgent@asu.edu)
6. Vectors For purposes of applications in calculus and physics, a vector has both a direction and a magnitude (length), and is usually represented as an arrow. The start of the arrow is the vector s foot,
More informationLinear algebra and the geometry of quadratic equations. Similarity transformations and orthogonal matrices
MATH 30 Differential Equations Spring 006 Linear algebra and the geometry of quadratic equations Similarity transformations and orthogonal matrices First, some things to recall from linear algebra Two
More informationUnderstanding Basic Calculus
Understanding Basic Calculus S.K. Chung Dedicated to all the people who have helped me in my life. i Preface This book is a revised and expanded version of the lecture notes for Basic Calculus and other
More informationNumerical Analysis Lecture Notes
Numerical Analysis Lecture Notes Peter J. Olver 5. Inner Products and Norms The norm of a vector is a measure of its size. Besides the familiar Euclidean norm based on the dot product, there are a number
More informationCopyrighted Material. Chapter 1 DEGREE OF A CURVE
Chapter 1 DEGREE OF A CURVE Road Map The idea of degree is a fundamental concept, which will take us several chapters to explore in depth. We begin by explaining what an algebraic curve is, and offer two
More informationVector Algebra and Calculus
Vector Algebra and Calculus 1. Revision of vector algebra, scalar product, vector product 2. Triple products, multiple products, applications to geometry 3. Differentiation of vector functions, applications
More informationMAT 1341: REVIEW II SANGHOON BAEK
MAT 1341: REVIEW II SANGHOON BAEK 1. Projections and Cross Product 1.1. Projections. Definition 1.1. Given a vector u, the rectangular (or perpendicular or orthogonal) components are two vectors u 1 and
More informationEuclidean Geometry. We start with the idea of an axiomatic system. An axiomatic system has four parts:
Euclidean Geometry Students are often so challenged by the details of Euclidean geometry that they miss the rich structure of the subject. We give an overview of a piece of this structure below. We start
More informationLinear Algebra: Vectors
A Linear Algebra: Vectors A Appendix A: LINEAR ALGEBRA: VECTORS TABLE OF CONTENTS Page A Motivation A 3 A2 Vectors A 3 A2 Notational Conventions A 4 A22 Visualization A 5 A23 Special Vectors A 5 A3 Vector
More informationConcepts in Calculus III
Concepts in Calculus III Beta Version UNIVERSITY PRESS OF FLORIDA Florida A&M University, Tallahassee Florida Atlantic University, Boca Raton Florida Gulf Coast University, Ft. Myers Florida International
More informationWhat are the place values to the left of the decimal point and their associated powers of ten?
The verbal answers to all of the following questions should be memorized before completion of algebra. Answers that are not memorized will hinder your ability to succeed in geometry and algebra. (Everything
More informationWe call this set an ndimensional parallelogram (with one vertex 0). We also refer to the vectors x 1,..., x n as the edges of P.
Volumes of parallelograms 1 Chapter 8 Volumes of parallelograms In the present short chapter we are going to discuss the elementary geometrical objects which we call parallelograms. These are going to
More information(a) We have x = 3 + 2t, y = 2 t, z = 6 so solving for t we get the symmetric equations. x 3 2. = 2 y, z = 6. t 2 2t + 1 = 0,
Name: Solutions to Practice Final. Consider the line r(t) = 3 + t, t, 6. (a) Find symmetric equations for this line. (b) Find the point where the first line r(t) intersects the surface z = x + y. (a) We
More informationLINES AND PLANES CHRIS JOHNSON
LINES AND PLANES CHRIS JOHNSON Abstract. In this lecture we derive the equations for lines and planes living in 3space, as well as define the angle between two nonparallel planes, and determine the distance
More informationLecture 14: Section 3.3
Lecture 14: Section 3.3 Shuanglin Shao October 23, 2013 Definition. Two nonzero vectors u and v in R n are said to be orthogonal (or perpendicular) if u v = 0. We will also agree that the zero vector in
More informationExam 1 Sample Question SOLUTIONS. y = 2x
Exam Sample Question SOLUTIONS. Eliminate the parameter to find a Cartesian equation for the curve: x e t, y e t. SOLUTION: You might look at the coordinates and notice that If you don t see it, we can
More informationBiggar High School Mathematics Department. National 5 Learning Intentions & Success Criteria: Assessing My Progress
Biggar High School Mathematics Department National 5 Learning Intentions & Success Criteria: Assessing My Progress Expressions & Formulae Topic Learning Intention Success Criteria I understand this Approximation
More informationMechanics 1: Vectors
Mechanics 1: Vectors roadly speaking, mechanical systems will be described by a combination of scalar and vector quantities. scalar is just a (real) number. For example, mass or weight is characterized
More informationFactoring Patterns in the Gaussian Plane
Factoring Patterns in the Gaussian Plane Steve Phelps Introduction This paper describes discoveries made at the Park City Mathematics Institute, 00, as well as some proofs. Before the summer I understood
More informationSolutions to Practice Problems
Higher Geometry Final Exam Tues Dec 11, 57:30 pm Practice Problems (1) Know the following definitions, statements of theorems, properties from the notes: congruent, triangle, quadrilateral, isosceles
More informationα = u v. In other words, Orthogonal Projection
Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v
More informationSolution of Linear Systems
Chapter 3 Solution of Linear Systems In this chapter we study algorithms for possibly the most commonly occurring problem in scientific computing, the solution of linear systems of equations. We start
More informationLinear Algebra I. Ronald van Luijk, 2012
Linear Algebra I Ronald van Luijk, 2012 With many parts from Linear Algebra I by Michael Stoll, 2007 Contents 1. Vector spaces 3 1.1. Examples 3 1.2. Fields 4 1.3. The field of complex numbers. 6 1.4.
More informationChapter 17. Orthogonal Matrices and Symmetries of Space
Chapter 17. Orthogonal Matrices and Symmetries of Space Take a random matrix, say 1 3 A = 4 5 6, 7 8 9 and compare the lengths of e 1 and Ae 1. The vector e 1 has length 1, while Ae 1 = (1, 4, 7) has length
More information5 VECTOR GEOMETRY. 5.0 Introduction. Objectives. Activity 1
5 VECTOR GEOMETRY Chapter 5 Vector Geometry Objectives After studying this chapter you should be able to find and use the vector equation of a straight line; be able to find the equation of a plane in
More informationa 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.
Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given
More informationMTH4103: Geometry I. Dr John N. Bray, Queen Mary, University of London
MTH4103: Geometry I Dr John N Bray, Queen Mary, University of London January March 2014 Contents Preface iv 1 Vectors 1 11 Introduction 1 12 Vectors 2 13 The zero vector 3 14 Vector negation 3 15 Parallelograms
More information9 MATRICES AND TRANSFORMATIONS
9 MATRICES AND TRANSFORMATIONS Chapter 9 Matrices and Transformations Objectives After studying this chapter you should be able to handle matrix (and vector) algebra with confidence, and understand the
More informationPYTHAGOREAN TRIPLES KEITH CONRAD
PYTHAGOREAN TRIPLES KEITH CONRAD 1. Introduction A Pythagorean triple is a triple of positive integers (a, b, c) where a + b = c. Examples include (3, 4, 5), (5, 1, 13), and (8, 15, 17). Below is an ancient
More informationWelcome to Math 19500 Video Lessons. Stanley Ocken. Department of Mathematics The City College of New York Fall 2013
Welcome to Math 19500 Video Lessons Prof. Department of Mathematics The City College of New York Fall 2013 An important feature of the following Beamer slide presentations is that you, the reader, move
More informationELEMENTS OF VECTOR ALGEBRA
ELEMENTS OF VECTOR ALGEBRA A.1. VECTORS AND SCALAR QUANTITIES We have now proposed sets of basic dimensions and secondary dimensions to describe certain aspects of nature, but more than just dimensions
More informationVector algebra Christian Miller CS Fall 2011
Vector algebra Christian Miller CS 354  Fall 2011 Vector algebra A system commonly used to describe space Vectors, linear operators, tensors, etc. Used to build classical physics and the vast majority
More informationCIRCLE COORDINATE GEOMETRY
CIRCLE COORDINATE GEOMETRY (EXAM QUESTIONS) Question 1 (**) A circle has equation x + y = 2x + 8 Determine the radius and the coordinates of the centre of the circle. r = 3, ( 1,0 ) Question 2 (**) A circle
More informationIntroduction. The Aims & Objectives of the Mathematical Portion of the IBA Entry Test
Introduction The career world is competitive. The competition and the opportunities in the career world become a serious problem for students if they do not do well in Mathematics, because then they are
More information5.3 The Cross Product in R 3
53 The Cross Product in R 3 Definition 531 Let u = [u 1, u 2, u 3 ] and v = [v 1, v 2, v 3 ] Then the vector given by [u 2 v 3 u 3 v 2, u 3 v 1 u 1 v 3, u 1 v 2 u 2 v 1 ] is called the cross product (or
More informationSection 2.4: Equations of Lines and Planes
Section.4: Equations of Lines and Planes An equation of three variable F (x, y, z) 0 is called an equation of a surface S if For instance, (x 1, y 1, z 1 ) S if and only if F (x 1, y 1, z 1 ) 0. x + y
More informationMath 215 HW #6 Solutions
Math 5 HW #6 Solutions Problem 34 Show that x y is orthogonal to x + y if and only if x = y Proof First, suppose x y is orthogonal to x + y Then since x, y = y, x In other words, = x y, x + y = (x y) T
More informationDefinition: A vector is a directed line segment that has and. Each vector has an initial point and a terminal point.
6.1 Vectors in the Plane PreCalculus 6.1 VECTORS IN THE PLANE Learning Targets: 1. Find the component form and the magnitude of a vector.. Perform addition and scalar multiplication of two vectors. 3.
More informationExample SECTION 131. XAXIS  the horizontal number line. YAXIS  the vertical number line ORIGIN  the point where the xaxis and yaxis cross
CHAPTER 13 SECTION 131 Geometry and Algebra The Distance Formula COORDINATE PLANE consists of two perpendicular number lines, dividing the plane into four regions called quadrants XAXIS  the horizontal
More informationISOMETRIES OF R n KEITH CONRAD
ISOMETRIES OF R n KEITH CONRAD 1. Introduction An isometry of R n is a function h: R n R n that preserves the distance between vectors: h(v) h(w) = v w for all v and w in R n, where (x 1,..., x n ) = x
More information2. Spin Chemistry and the Vector Model
2. Spin Chemistry and the Vector Model The story of magnetic resonance spectroscopy and intersystem crossing is essentially a choreography of the twisting motion which causes reorientation or rephasing
More informationEXPONENTS. To the applicant: KEY WORDS AND CONVERTING WORDS TO EQUATIONS
To the applicant: The following information will help you review math that is included in the Paraprofessional written examination for the Conejo Valley Unified School District. The Education Code requires
More informationPRIMARY CONTENT MODULE Algebra I Linear Equations & Inequalities T71. Applications. F = mc + b.
PRIMARY CONTENT MODULE Algebra I Linear Equations & Inequalities T71 Applications The formula y = mx + b sometimes appears with different symbols. For example, instead of x, we could use the letter C.
More informationThe Dot and Cross Products
The Dot and Cross Products Two common operations involving vectors are the dot product and the cross product. Let two vectors =,, and =,, be given. The Dot Product The dot product of and is written and
More informationMath Review Large Print (18 point) Edition Chapter 2: Algebra
GRADUATE RECORD EXAMINATIONS Math Review Large Print (18 point) Edition Chapter : Algebra Copyright 010 by Educational Testing Service. All rights reserved. ETS, the ETS logo, GRADUATE RECORD EXAMINATIONS,
More informationSouth Carolina College and CareerReady (SCCCR) PreCalculus
South Carolina College and CareerReady (SCCCR) PreCalculus Key Concepts Arithmetic with Polynomials and Rational Expressions PC.AAPR.2 PC.AAPR.3 PC.AAPR.4 PC.AAPR.5 PC.AAPR.6 PC.AAPR.7 Standards Know
More informationGeometry in architecture and building
Geometry in architecture and building Hans Sterk Faculteit Wiskunde en Informatica Technische Universiteit Eindhoven ii Lecture notes for 2DB60 Meetkunde voor Bouwkunde The picture on the cover was kindly
More informationAlgebra and Geometry Review (61 topics, no due date)
Course Name: Math 112 Credit Exam LA Tech University Course Code: ALEKS Course: Trigonometry Instructor: Course Dates: Course Content: 159 topics Algebra and Geometry Review (61 topics, no due date) Properties
More information