Date | What happened | ||||||||||||||||||||||
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10/14/2002 | Jason Sullivan kindly presented problem #5 of workshop
#4. This is a problem which caused some difficulties. We then went over some problems from chapter 14 in preparation for the exam. Ther instructor managed to use up lots of time by suggesting that students do a problem from the Chain Rule section, translating the Laplacian (sum of the "pure" second partials in x and y) from one coordinate system to another. | ||||||||||||||||||||||
10/10/2002 | Ah, well, the last real class before our first exam wasn't a
total disaster but I didn't quite do what I wanted. Let me first tell
what I thought I did, and then comment on what I might additionally
have wished to do. I summarized looking for extreme values for functions of 1 variable. Here goes:
The simplest situation is 2 variables. Things already get complicated enough. We try to "bootstrap" from what we already know: that is we will try to use the second derivative test from one variable to allow us to get information for two variables. Here are the starting assumptions: we have a function z=f(x,y) with a critical point at (a,b). So we know that grad f=0 at (a,b). If we now "slice" the graph in R^{3} of z=f(x,y) by planes perpendicular to the (x,y)-plane which go through the point (a,b), in each case we get a curve which has a critical point above (a,b). Let me try to describe these slices. Choose a two-dimensional direction: that is, a two-dimensional unit vector. We're lucky that such vectors have a simple description: u=cos(theta)i+sin(theta)j. A two-dimensional straight line through (a,b) in the direction of u is just (a+cos)theta)t,b+sin(theta)t). And f's values on that straight line are exactly f(a+cos(theta)t,b+sin(theta)t). What's the derivative of this function with respect to t? We will compute this, but first some discussion of it: this is the directional derivative of f in the u direction. It is also the derivative of the curve obtained by slicing the graph z=f(x,y) by the plane through (a,b,0), perpendicular to the (x,y)-plane, in the direction of u. So now: (d/dt)f(a+cos(theta)t,b+sin(theta)t)= D_{1}f(a+cos(theta)t,b+sin(theta)t)cos(theta) +D_{2}f(a+cos(theta)t,b+sin(theta)t)sin(theta).This computation uses the Chain Rule. It is, of course, exactly D_{u}f, which was defined last time. Since (a,b) is a critical point, both D_{1}f and D_{2}f are 0 at (a,b). So the first directional derivative is 0. What we now need to do is take the t derivative again. We must be careful. Each of D_{1}f and D_{2}f are functions of 2 variables, and that each of the two entries in each of these two functions has some dependence on t: the Chain Rule needs to be used with care. So if we d/dt what's above (in this computation theta and a and b are constant), we will get: d/dt(D_{1}f(a+cos(theta)t,b+sin(theta)t)cos(theta) +D_{2}f(a+cos(theta)t,b+sin(theta)t)sin(theta))= D_{1}D_{1}f(a+cos(theta)t,b+sin(theta)t)(cos(theta))^{2} +D_{2}D_{1}f(a+cos(theta)t,b+sin(theta)t)(cos(theta)sin(theta)) +D_{1}D_{2}f(a+cos(theta)t,b+sin(theta)t)(sin(theta)cos(theta)) +D_{2}D_{2}f(a+cos(theta)t,b+sin(theta)t)(sin(theta))^{2}and now "plug in" t=0. Remember that cross-partials are equal (f_{xy}=f_{yx}) and call A=f_{xx}(a,b) and B=f_{xy}(a,b) and C=f_{yy}(a,b). Then what we have is A(cos(theta))^{2}+2Bcos(theta)sin(theta)+C(sin(theta))^{2}We could apply the second derivative test in one variable to this if we knew appropriate information about every slice. Thus we could conclude that the critical point (a,b) was a minimum if for every theta, the expression above was positive. If theta=0 this means that A=f_{xx}(a,b) should be positive. But even more needs to be true. Suppose we divide the expression above by (cos(theta))^{2} and we call w=tan(theta) (this is weird!). Then what is written above is just A+2Bw+Cw^{2} and we would like to know what conditions on A and B and C would imply that this is always positive. We did something like this in the lecture 9/9. This positivity will occur exactly when the quadratic has no real reals. So (2B)^{2}-4AC should be negative. (The parabola A+2Bw+Cw^{2} will then never intersect the w-axis and since A>0 it must always be positive.!) This somewhat subtle reasoning leads to what is called the Second Derivative Test for functions of two variables. First, define the Hessian, H, to be | f_{xx} f_{xy} | H=det | _{ }| | f_{xy} f_{yy} |and suppose that (a,b) is a critical point of f.
This should give you some idea of the complexity of critical point behavior in more than 1 variable. Although much is known already, this is actually an object of current research. | ||||||||||||||||||||||
10/9/2002 | I finally got around to something I should have discussed
several lectures ago. First, a mean thing done to Maple: I
typed z:=x^2*arctan(y*exp(y)); and then I asked
Maple to compute diff(diff(z,y$30),x$3); which is
the 30^{th} derivative with respect to y of z followed by 3
derivatives with respect to x. The result was 0, and the computation
time needed was 5.539 seconds on the PC in my office. Of course what
Maple did was compute 30 derivatives of a complicated
function, and these derivatives get very big in terms of data
(this is called "expression swell"), and take time and storage space
to manipulate. Then there were 3 x derivatives, and the result was
0. On the other hand, when I instructed Maple to do the x
derivatives first and then the y derivatives, the program reported no
elapsed time used (that means, less than a thousandth of a
second). What's going on? The result needed here is called in your text Clairaut's Theorem, and loosely states that the order of taking "mixed" partial derivatives (derivatives with respect to different variables) doesn't matter. More precisely, the result is: if f is a function of two variables, x and y, and if both f_{xy} and f_{yx} exist and are continuous, then they must be equal. I tried to motivate this with a brief allusion to spreadsheets (!) and then remarked that an actual proof is in appendix F of the text, and uses the Mean Value Theorem 4 times, in much the same manner as we've already seen (the lecture on 10/3). I didn't have the time or desire to give the proof. Here, though, is an example to show that some hypotheses are needed to guarantee the equality of the mixed partials:
The temperature of the point in the nebula outside the rocket ship at time t is T(x(t),y(t), z(t)). How does the temperature change? This is, of course, (d/dt)T=T_{x}(dx/dt)+T_{y}(dy/dt)+T_{z}(dz/dt). Some contemplation of this formula allows a certain "decoupling" to take place, that is, a separation of the influence of the rocket ship and of the temperature. We recognize that the velocity vector is V(t)=(dx/dt)i+(dy/dt)j+(dz/dt)k. And then we see that (d/dt)T is really a dot product of V(t) with another vector, namely T_{x}i+T_{y}j+T_{z}k. This vector is called the gradient of T, written sometimes as (upside down triangle)T and sometimes called grad T. I'll call it grad T here because of typographical limitations. There were some questions about grad T, so I invented a rather simple example, something like the following: if T(x,y,z)=3x^{2}+4xy^{3}+z^{4}, then T(2,-1,1)=5. And grad T=6xi+12xy^{2}+4z^{3}. At p=(2,-1,2), grad T is 12i+24j+4k. grad T is a vector function of position, while T itself is a scalar function. Then (d/dt)T turns out to be the dot product of grad T with V(t). Observation 1 We have separated the effects of temperature and the rocket. In fact, all that matters in the computation of (d/dt)T from the rocket is the velocity. If two rocket ships go through the same point with the same velocities, then (d/dt)T will be the same. All that matters is the velocity: the tangent direction to the flight path (a curve) and the speed (the lenght of V). Observation 2 How can we make (d/dt)T big? If it isn't 0, we could always make the rocket ship move faster. If we increase the speed by a factor of M>0, then (d/dt)T will multiply by M. This is because (d/dt)T is ||V|| ||grad T|| cos(angle between V and grad T). Multiplication of V by M results in a multiplication of ||V|| by M, and nothing else changes. So (d/dt)T gets bigger. Observation 3 But really the answer to the previous question was a bit silly. We should try to look at different directions. What direction will cause the derivative to be larger? So we defined the directional derivative of T with respect to a unit vector u: D_{u}T. This was the rate of change of T with respect to a "rocket ship" moving with unit speed in the direction of u, and it is just u· grad T. Since this is ||u|| ||grad T|| cos(angle between them) and we've restricted ||u|| to be a unit vector, we see the only ingredient we've got to vary is the angle. Indeed, immediately we have: I went back to the example and found the directional derivative of that T in various directions, including the maximizing direction and the minimizing one. Observation 4 Isothermals are collections of points where the temperature is constant. If T(2,-1,1) is 5, then (2,-1,1) is in the isothermal (the level set) associated to the temperature 5. If the rocket ship flies in an isothermal, then the rate of change of the temperature perceived by the rocket ship is 0. So (d/dt)T=0 and by the decoupling we've already seen, that means V(t)·grad T=0. So the velocity vector of such a flight is always perpendicular (normal) to the gradient vector. From this evidence and the knowledge that grad T at (2,-1,1) is 12i+24j+4k we were able to deduce that the plane tangent to the surface T(x,y,z)=9 is 12(x-2)+24(z+1)+4(z-1)=0. Directions normal to the gradient have zero first order change. Here is a command to graph a chunk of the surface T(x,y,z)=5 and store the graph in the variable A: A:=implicitplot3d(3*x^2+4*x*y^3+z^4=5,x=1..3,y=-2..0,z=0..2, grid=[40,40,40],axes=normal,color=green): Here is a command to graph and store as a variable B a piece of the candidate for the tangent plane which we computed: B:=implicitplot3d(8*(x-2)+24*(y+1)+4*(z-1)=0,x=1..3,y=-2..0,z=0..2, grid=[40,40,40],axes=normal,color=red): And finally the command display3d(A,B) displays both graphs together. The result is shown below. Note that the tangent plane actually cuts through the surface (a saddle-more about this next time!). I sketched some level curves for the function 10-2x^{2}-y^{2} and related the gradient of the function to the level curves: again the gradient is perpendicular to the level curves, and points in the direction of increasing function value. I gave out and discussed the review problems. | ||||||||||||||||||||||
10/7/2002 | We discussed the chain rule using some examples of functions
with given data points. What I wanted people to get out of this
was a general philosophy that
differentiation somehow measures first-order or linear
effects. Therefore when computing the derivative or partial
derivative, "kick" the designated variable and follow through the
change using the various definitions of derviative and partial
derivative. At the end, examine the compounded change and identify
the first-order effect: that's the desired derivative. Each of the questions on the data sheet involved what seemed to be a slightly different type of composition. They can all be analyzed in the same way, though. For the club example, the chain rule is S'(t)=h'(k(t))k'(t). For diamond, the equation W'(t)=f_{x}(h(t),k(t))h'(t)+f_{y}(h(t),k(t))k'(t) applies. And heart is a consequence of Q_{x}(x,y)=f_{x}(h(x),g(x,y))h'(x)+f_{y}((h(x),g(x,y))g_{x}(x,y) and Q_{y}(x,y)=f_{y}((h(x),g(x,y))g_{y}(x,y). There is the potential for great confusion in the varied appearances of x and y, and in where the derivatives are evaluated. Finally, spade follows from C'(t)=h'(f(t,2-3t))(f_{x}(t,2-3t)+f_{y}(t,2-3t)(-3)). Then I tried to indicate how the chain rule can result in results that are notationally irritating when working with implicit functions. These examples are discussed in the text. If F(x,y)=Constant implicitly defines y as a function of x, then applying d/dx to this equation gives (F_{x)}+(F_{y})(dy/dx)=0 or dy/dx=-F_{x}/F_{y} where certainly the idea of treating derivatives like fractions doesn't make sense! And in 3 variables the situation gets even worse. If F(x,y,z)=Constant implicitly defines z as a function of x and y, then the partial derivative of z as a function of x (with y held constant) is -F_{x}/F_{z}. Symmetrically working with each variable in turn we see that (z_{x})(x_{y})(y_{z})=-1. Then I gave out workshop #5 and it was noted that the due date was wrong. This has since been corrected on the web. | ||||||||||||||||||||||
10/3/2002 | A function of one variable, f(x), is differentiable if
f(x+h) can be written in the following way:
Parenthetical remark: I mentioned that if we knew a function of 1 variable was equal to its Taylor series, then the error term could be written as a sum of an infinite tail of the series, so that's what "Error" could be. Of course, many functions don't have a Taylor series and/or don't have a readily computable Taylor series, so the value of that observation is unclear! I want to define something similar for functions of two variables. Preliminarily, f(x,y) will be differentiable if f(x+h,y+k) can be written in the following way: This is a complicated statement and needs some investigation. First, the equation is true for any selections of h and k. So we can look at any special values we care to. For example, if k=0 and h is not 0, we can rewrite the equation to become ((f(x+h,y)-f(x,y))/h)=A+Error_{1}. But as h-->0, the right-hand side-->A (since the Error terms go to 0). That means the left-hand side has a limit. So if f is differentiable, then f_{x} exists and equals A. Similarly (set h=0 and let k-->0) if f is differentiable, then f_{y} exists and is B. Therefore, The converse of a simple implication reverses the hypothesis and the conclusion. For example, the converse of "If Fred is a frog, then Fred hops" is "If Fred hops, then Fred is a frog." Even this simple a statement shows that a converse need not be true. The function we looked at last time, defined by f(x,y)=(xy)/sqrt(x^{2}+y^{2}) if (x,y) is not (0,0) and by 0 if x and y are both 0 has some interesting properties. First, f has partial derivatives at every point. Why? Away from (0,0), the partial derivatives can be computed by the standard algorithms of 1-variable calculus. At (0,0), we notice that f is 0 on both the x and y axes, so the partials both exist and are 0. But consider f(w,w) where w is a small positive number. Direct computation in the formula shows that f(w,w,) is (1/sqrt(2))w. But if f were differentiable at (0,0) the wonderfully complicated definition above would apply. The right-hand side of the formula is f(x,y)+f_{x}(0,0)h+f_{y}(0,0)k+Error_{1}(f,x,y,h,k)h+Error_{2}(f,x,y,h,k)k but f(0,0) and f_{x}(0,0) and f_{y}(0,0) are all 0. So f(w,w)=Error_{1}(f,x,y,w,w)w+Error_{2}(f,x,y,w,w)w. If we remember that f(w,w) is (1/sqrt(2))w and divide the equation by w, we get (1/sqrt(2))=Error_{1}+Error_{2} and both of the errors-->0 as w-->0. So the right-hand side-->0 but the left-hand side does not. This rather intricate contradiction shows that this f is not differentiable at (0,0). Several things still may not (probably are not!) clear to a student right now. What are the reasons for defining "differentiable" in this intricate manner? How could one check that specific functions defined by formulas are differentiable? Here is one answer to the second question. The first will be answered several times during the rest of this course. Suppose we want to compare f(x+h, y+k) and f(x,y). Then consider: The 1-variable Mean Value Theorem shows that f(x,y+k)-f(x,y)=f_{y}(x,y+beta_{k})k where |beta_{k}|<|k|. Also f(x+h,y+k)-f(x,y+k)=f_{x}(x+alpha_{h},y+k)k where |alpha_{h}|<|h|. Here's the big hypothesis coming: if the partial derivatives are continuous, then the difference between f_{y}(x,y+beta_{k}) and f_{y}(x,y) approaches 0 as k-->0. And the difference between f_{x}(x+alpha_{h},y+k) and f_{x}(x,y) also goes to 0 as (h,k)-->(0,0). These differences both get put into the error terms, and so we see
Then I went through problem #19 of section 14.4, a rather straightforward application of the linear approximation (that's the constant term plus the first degree terms) to an incremented value of the function. The numbers worked out nicely, and, in this example, the errors were fairly small. I concluded by finding a tangent plane to a graph, I think the graph of z=5x^{2}+2y^{4}, a sort of a cup, at the point (2,1,22). I did this by examining the section of the graph where y=1. This gives a curve with equation z=5x^{2}+2 whose derivative at (2,22) was 10x, or 20. Therefore 1i+0j+20k was tangent to the surface at (2,1,22). A similar analysis when x=2 shows that 0i+1j+8k was also tangent to the surface at that point. Therefore the cross product of these vectors would be perpendicular to the tangent plane. We computed the cross-product and it was -20i-8j+k (generally it will be -f_{x}i-f_{y}j+k) and the plane went through the point (2,1,22), so the desired tangent line is -20(x-2)-8(y-1)+(z-22)=0. I remarked that on an exam I would rather not have such an answer "simplified" which brought up the question of
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10/2/2002 | Matthew Gurkovich presented a solution to problem 3 in
workshop 3. I thank him for this. I think the most difficult part of
this problem is recognizing that the letter "t" serves two different
purposes in the problem. I analyzed the idea of differentiability for functions of 1 variable. What is the derivative? Some ideas are i) rate of change, ii) slope of the tangent line to the graph of the function, iii) a certain limit, and iv) velocity or acceleration. And certainly there are others. As a limit, f'(x)=lim_{h-->0}(f(x+h)-f(x))/h. Of course this limit is paradoxical contrast to the basic "limit reason" for looking at continuous functions: those limits can be evaluated just by "plugging in", and here such an approach results in the forbidden 0/0. Removal of the "lim_{h-->}" prefix to the defining equation above yields a statement which is generally false: there is an error involved. Also the division and minus signs are a complicating feature. So we transform the equation above into the following: f(x+h)=f(x)+f'(x)h+Error(f,x,h)h where Error(f,x,h) is something (possibly [probably!]) very complicated depending on f and x and h with the property that (with f and x held fixed) that Error(f,x,h)-->0 as h-->0. With perspective that further study of calculus gives, we know that if f is equal to its Taylor series, then the Error(f,x,h)h is just a sum of terms involving powers of h higher than 1, so at least one power of h can be factored out. The result is something that goes to 0 "faster" than h: order higher than first order. So a perturbation in the argument, x, to a function f(x), that is, x changes to x+h (h may be either positive or negative) yields f(x)+f'(x)h+Error(f,x,h)h. The first term is the old valuye, the second term is directly proportional to the change, h, with constant of proportionality f'(x), while the third term -->0 with more than first order in h. In particular, this shows easily that if h-->0 then f(x+h)-->f(x), so differentiable functions (which are those having such a "decomposition") must be continuous. But more than continuity is involved. The simple example 2x if x>0 f(x)= 0 if x=0 (-1/3)x if x<0shows that the same multiplier is needed on both sides of 0: 2 can't be deformed into -1/3 by concealing things in the error term. So this function is not differentiable. The aim is to define a similar "good" decomposition for functions of more than 1 variable: We computed some partial derivatives for functions which looked like 5x^{2}y^{5} and xe^{xy2} and arctan(y/x). The routine differentiation algorithms (rules for derivatives) work here. More suspicious examples were considered. The first is f(x,y)=0 if xy=0 and 1 otherwise. This function had the following properties: away from the coordinate axes (where xy=0) both f_{x} and f_{y} are 0. On the y-axis, f_{y} is 0 and on the x-axis, f_{x} is 0. At (0,0), both f_{x} and f_{y} exist and are 0. The limits for the other partial derivatives don't exist. But notice that f(0+h,0+k) compared to f(0,0)+f_{x}(0,0)h+f_{y}(0,0)k+ERROR where somehow ERROR should go to 0 faster than first order. But that means f(h,k)=(three zero terms)+ERROR. Since if both h and k are small positive numbers, we get 1=ERROR, apparently the decomposition is impossible! So (when the correct definition is given!) this f has partial derivatives at (0,0) but is not differentiable at (0,0)! Then I looked at the function f(x,y)=r cos theta sin theta with x and y in polar coordinates. A more mysterious formula for f(x,y) results if we use x=r cos theta and y=r sin theta: f(x,y)=(xy)/sqrt(x^{2}+y^{2}). The graph of this function includes the x and y axes: f's values are zero there. But then the partial derivatives of f at (0,0) both must exist and are 0. But f(w,w)=.5w, so the change along the line y=x is certainly first order, but cannot be accounted for in the formula f(x+h,y+k)=f(x,y)+Ah+Bk+ERROR. If we take k=0, then a limit manipulation shows that A must be f_{x}(x,y) and B must be f_{y}(x,y). For this function, both partial derivatives exist at all points, and both are 0 at (0,0). Therefore f(0+h,0+k)=0+0+0+ERROR. But f(w,w) is .5w=ERROR, and the right-hand side is higher than first order as w-->0 and the left-hand side is not. More will follow about this tomorrow. Here's the result of the Maple command | ||||||||||||||||||||||
9/30/2002 | I began by considering how continuity is defined. In one
variable, a function f is continuous at x_{0} if
lim_{x-->x0} f(x)
exists and equals f(x_{0}). Combining this with the official
definition of limit given last time, we see:
First we looked at the function n(x,y), defined last time. I asked where n was continuous: that is, for which (x_{0},y_{0}) does the limit of n(x,y) as (x,y)-->(x_{0},y_{0}) exist and is it equal to n(x_{0},y_{0})? First we approached the question "emotionally": where is n continuous? After some discussion, it was decided that n would be continuous "off" the parabola, that is, for (x_{0},y_{0}) where y_{0} is not equal to (x_{0})^{2}. Away from the parabola, the graph of the function is quite flat (always 0). So if (x,y) is close enough, then n(x,y) is really 0 around (x_{0},y_{0}). So it should be continuous. If (x_{0},y_{0}) is on the parabola, though, the limit won't exist. I then said that I wanted to work with the definition, and verify that: n(x,y) is continuous at (0,1) and n(x,y) is not continuous at (0,0). How to verify that n(x,y) is continuous at (0,1): if we take delta to be, say, 1/2, then ||(a,b)-(0,1)||<1/2 means that (a,b) is not on the parabola (the 1/2 is actually chosen for this reason!), so that n(a,b)=0 and n(0,1)=0 also, and therefore |n(a,b)-n(0,1)|<eps for any positive eps. The choice of delta here is rather easy and almost straightforward. In general, the choice of delta likely will depend on (x_{0},y_{0}) and on eps. Then we went on to try to show that n is not continuous at (0,0). Here we need to verify the negation of the continuity statement. Negations of complicated logical statements can be quite annoying to state. In this case, we need to do the following:
What are the simplest functions usually considered to be continuous> Thank goodness the suggestion was made that polynomials are continuous because I had prepared an analysis of the continuity of the polynomial f(x,y,z)=x^{2}y^{2}-4yz. We expect that this polynomial (and, indeed, all polynomials) are actually continuous at every point. So the limits should be evaluated just by "plugging in". In fact, the limit of f(x,y,z) as (x,y,z)-->(3,1,2) should just be f(3,1,2) which is 1. I verified the definition for this limit statement. The verification used entirely elementary methods, but was quite intricate is spite of that. We looked at: |f(x,y,z)-1|=|x^{2}y^{2}-4yz-1|=|(x^{2}y^{2}-4yz)-(3^{2}1^{2}-4·1·2)|. Then the triangle inequality was used, so that the last term is less than or equal to |x^{2}y^{2}-3^{2}1^{2}|+|-4yz+4·1·2|. The second of these expressions seems a bit easier to handle, so: |-4yz+4·1·2|<|4|·|yz-1·2|. Here I tried to suggest that too much was changing. This is handled by making the difference equal to several one variable differences. So: |4|·|yz-1·2|=4|yz + 0 -1·2|= 4|yz-y2+y2-1·2|=4(|(yz-y2)+(y2-1·2)|). And again split up by the triangle inequality: 4|yz-y2|+4|y2-1·2|. The second term looks easiest to handle. Get a "bound" on 4|y2-1·2|=8|y-1| by making |y-1| sufficiently small. Since we had split up the original difference into two parts and then split the part we were considering into two parts, I guessed that it would be good enough to make this less than eps/4. So we would need |y-1|<eps/32. Now we considered 4|yz-y2|=4|y|·|z-2|. To get this less than eps/4 we would make |z-2| small. But to control the product we needed to control the size of |y|. Well, if (pulled out of the air!) |y-1|<1 then I knew that 0<y<2, so |y|<2. Therefore 4|y|·|z-2|<4·2·|z-2|. This will be less than eps/4 if |z-2|<eps/32. As I mentioned in class, the coincidence of the 32's made me uneasy. Now we are half done. We still need to estimate the difference: |x^{2}y^{2}-3^{2}1^{2}|. Again we write it as a succession of differences of one variable: |x^{2}y^{2}-3^{2}1^{2}|=|x^{2}y^{2}-x^{2}1^{2}+x^{2}1^{2}-3^{2}1^{2}|. And again the triangle inequality leaves us with estimation of two pieces:|x^{2}y^{2}-x^{2}1^{2}| and |x^{2}1^{2}-3^{2}1^{2}|. We do the second part first. |x^{2}1^{2}-3^{2}1^{2}|=|x^{2}-3^{2}|=|x+3|·|x-3|. If, say, |x-3|<1 then x is between 2 and 4 so |x+3| is between 5 and 7, and therefore |x^{2}-3^{2}|=|x+3|·|x-3|<7|x-3|. This will be less than eps/4 if we require |x-3|<eps/28. (Somehow the numbers came out differently in class!) The final piece to handle is |x^{2}y^{2}-x^{2}1^{2}|=|x^{2}|·|y-1|. Since we have already controlled |x| (it is less than 4) we know that |x^{2}| is less than 16. Therefore |x^{2}|·|y-1|<16|y-1|. This will be less than eps/4 if we require |y-1|<eps/64 If we collect all the restrictions on the variables, we see that the implication "if ||(x,y,z,)-(3,1,2)||<delta then |f(x,y,z)-f(3,1,2)|<eps" will be true when delta is chosen to be the minimum of all the blue restrictions. Therefore choose delta to be the minimum of 1, eps/32, eps/28, and eps/64. The technique I outlined is a bit painful, but it does work and it is "elementary". A picture of what f(x,y,z) looks like doesn't seem to help very much. Here, for example, are three views of the output of the Maple command implicitplot3d(x^2*y^2-4*y*z=1,x=1..8,y=-1..3,z=-2..4,grid=[20,20,20], color=green,axes=normal);. The procedure implicitplot3d is loaded by with(plots); and plots implicitly defined surfaces, just as implicitplot itself plots implicitly defined curves. The option grid=[20,20,20] alters the sampling rate Maple uses. The default is [10,10,10], which makes quite a rough picture. On the other hand, one can ask for [50,50,50] which will take about 125 (5^{3}) as much time as the default. I just experimented at home. Sketching a sphere with the default grid took .070 seconds, and the [50,50,50] grid took 7.719 seconds. Indeed: in practical applications, the tradeoff between time and picture detail can be interesting.
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9/26/2002 | The instructor rudely began by filling some boards with
remarks about limits and continuity in 1 dimension. Consider
f(x)=x^{2}. A sketch was drawn. What happens to f(x) as x-->4?
Clearly lim_{x-->4}x^{2}=16. What does this mean?
This is a limit statement, and the official definition of limit is as
follows:lim_{x-->a}f(x)=b means given any eps>0 there is a delta>0 so that if 0<|x-a|<delta, then |f(x)-b|<eps. (This is in bold because it is important in the history and theory of the subject!)Here due to the limitations of html, "eps" will be written in place of the usually used Greek letter "epsilon" and "delta", in place of the usually used Greek letter "delta". Here this means: given eps>0, find delta>0 so that if |x-4|<delta, then |x^{2}-4|<eps. In order to verify that this implication is correct, some connection between |x-4| and |x^{2}-16|. But in fact |x^{2}-16|=|x-4| |x+4|. In order to really be convinced that this is true, we need to show that when |x-4| is small, |x+4| is controlled. That is, it does no good to verify that a product of two factors is small by showing that one factor of a product is small, if the size of the other product can't be controlled. Here is a small "computation": if |x-4|<1, then -1<x-4<1, so 3<x<5 so that 7<x+4<9 and consequently |x+4|<9. Now we can do the proof.
Then I began analyzing functions in R^{2}. We began by looking at f(x,y)=x^{2}+y^{2}. I drew a graph of this: the graph was a collection of points in R^{3}. I also commented on the contour lines. I strongly recommended the Maple procedures plot3d and contourplot and contourplot3d. You need to type the command with(plots); before using these procedures.
We considered the function g(x,y) defined piecewise by g(x,y)=1 if (x,y) is NOT (0,0) and which is 0 if (x,y)=(0,0). The graph is a plane parallel to the (x,y)-plane, 1 unit "up" in the z direction, except for the origin, which is back at (0,0,0). I asked when the limit as (x,y)-->(x_{0},y_{0}) existed and what the value was. Some discussion followed, and the somewhat disconcerting truth was told: the limit always exists and it always is 1. This example can be done in one variable, though. The piecewise function h(x,y) defined by = 1 if x>0 h(x,y)=37 if x=0 = 2 if x<0has lim_{(x,y)-->(x0,y0)}h(x,y) existing if x is NOT 0, and for x_{0}>0 the limit is 1 while for x_{0}<0 the limit is 2. A much more subtle example is provided by m(x,y)=(x^{2}-y^{2})/(x^{2}+y^{2}). This function is "fine" (continuous) away from (0,0). The limit along rays through the origin varies with the ray. Along the positive and negative x-axis the limit is 1, but along the positive and negative y-axis the limit is -1. Along y=Mx, the limit is (1-M^{2})/(1+M^{2}). I tried to show this surface with a demonstration in class. It is interesting to view the surface using Maple. The procedure contourplot3d gave the "best" picture for me. I finally looked at n(x,y). This is peculiar piecewise-defined function. Its value is 1 if y=x^{2} and 0 otherwise. It has the property that limits along any straight line through (0,0) exist, and all these limits are 0 BUT the limit as (x,y)-->(0,0) does NOT exist. I tried to explain this. The text gives an example of a rational function: (xy^{2})/(x^{2}+y^{4}) (see p.890 in section 14.2) with similar properties, which maybe is harder to understand. As a pop quiz, I asked students to create a function so that the limit as (x,y)-->(x_{0},y_{0}) did not exist if x^{2}+y^{2}=1 but did exist for all other (x,y). I urged students to begin reading chapter 14. | ||||||||||||||||||||||
9/25/2002 | Hardly any "progress" was made. We did more and more problems
from chapter 13. Attempts were made by valiant students to really get
me to explain what CURVATURE and TORSION:
Google reports only about 16,700 links with
information about both of these. Curvature, I tried to insist, referred to how much a curve bends. I gave another interpretation using the idea of the osculating circle. If a circle agrees "up to second order" (passes through a point, and first and second derivatives agree) with the graph of a function, then 1/(the radius of the circle) turns out to be the curvature. The circle is called the osculating circle. My online dictionary states: 1. [Math.] (of a curve or surface) have contact of at least the second order with; have two branches with a common tangent, with each branch extending in both directions of the tangent. 2. v.intr. & tr. kiss.The osculating circle is a second order analog of a tangent line. The tangent line agrees with a curve up to first order (value and first derivative of the curve and tangent line should agree). The osculating circle does the same up to second order. So where the closest circle is small, the curve bends a lot. Torsion is weirder. In a picture on this link an attempt is being made to show "high torsion when there is rapid departure from a plane." In my Google search I found web pages dealing with the relationship of curvature and torsion to coronary arteries and blood flow, concrete, plasma flow, how birds and gnats and flies fly, "carbon nanotubes" (thin filaments), models of molecules, motion of robots and octopuses and cilia and flagella ... and so on. I found Maple routines for computation of curvature and torsion: lots of stuff, most of it quite technical in both its applications and its mathematics. Lots of stuff! I tried to argue that Problem #5 on Workshop #3 showed that curvature could be concealed easily. According to Einstein, "the Lord is subtle but not mean" (approximately) and that knowing that the structure of a curve means dealing with its curvature and torsion sometimes may make life easier. Problem #5 has exquisitely disguised simple curves: in a), a circle, and in b), a straight line. The next few weeks would see an effort to analyze functions whose domain is in R^{2} or R^{3} or R^{n} and whose range was R. We will look at the concepts of limit, continuity, and derivative, and try to understand the real conceptual subtleties which occur with such functions. | ||||||||||||||||||||||
9/23/2002 | A valiant and not completely successful attempt to review
all questions students had about textbook homework problems for
most of the first two chapters. I'll need to spend time doing a few
more problems on Wednesday. I mentioned that one reason to consider an abstract version of vectors and inner products and lengths is that strong results involving other important examples can be learned. I suggested the following setup: a vector would correspond to a function on [0,1]. Vector addition and scalar multiplication would correspond to addition of functions and multiplication of functions by a constant. The dot product of two functions f and g would be defined by the integral from 0 to 1 of f(x) times g(x), so that the "length" of f would be the square root of the integral of f(x)^2 from 0 to 1. First, all the results we have proved about lengths and dot product remain correct. For example, the integral from 0 to 1 of exp(x)sin(x) will be bounded by the square root of the integral of exp(x)^2 multiplied by the square root of the integral of sin(x)^2. (Maple tells me that the first one is approximately .909 while the second is approximately .933.) So we have been "efficient" in learning how to organize our thoughts. Second, it turns out that this method of measuring the "size" of functions is essentially the same as the method of least squares, a widely used technique for estimating errors. I just learned today that there is a web page with detailed solutions for many of the odd-numbered problems in the textbook. Sigh. You may want to look at www.hotmath.org | ||||||||||||||||||||||
9/19/2002 | We began with a problem for students: compute the curvature
of the plane curve defined by
x(t)=integral from 0 to t cos(w^2/2)dw y(t)=integral from 0 to t sin(w^2/2)dwMost students were able to successfully see that this curve had curvature k=t, curvature which increased directly proportionately with travel along the curve. The integrals involved are called Fresnel integrals, and the curve resulting is called the Cornu spiral. The curve (and the integrals) arise in diffraction, and one link with a Java applet illustrating this is given. The spiral winds more and more tightly as the parameter increases. Today is devoted to an investigation of space curves. The geometry of these curves, as seen from the point of view of calculus (called "differential geometry of space curves") is a subject which originated in the 1800's. The material presented here was stated in about 1850-1870. It has within the last few decades become very useful in a number of applications: robotics, material science (structure of fibers), and biochemistry (the geometry of big molecules such as DNA). I'll carry along is a right circular helix as a basic example. x(t)=a cos(t) y(t)=a sin(t) z(t)=b tThe quantities a and b are supposed to be positive real numbers. This helix has the z-axis as axis of symmetry. It lies "above" the circle with radius a and center (0,0) in the (x,y)-plane. The distance between two loops of the helix is 2Pi b. If r(t)=x(t)i+y(t)j+z(t)k (the position vector), then r'(t)=x'(t)i+y'(t)j+z'(t)k=(ds/dt)T(t) is called the velocity vector. Here T(t) is called the unit tangent vector and is a unit vector in the direction of r'(t). ds/dt is the speed, and is sqrt(x'(t)^{2}+y'(t)^{2}+z'(t)^{2}), the length of r'(t). We use ds/dt also to convert derivatives with respect to t to derivatives with respect to s, as last time (the Chain Rule). Since T(t)·T(t)=1 differentiation together with commutativity of dot product gives 2T'(t)·T(t)=0, so T'(t) and T(t) are perpendicular. In fact, we are interested in dT/ds, which is the same as (1/(ds/dt))T'(t) (it is usually easier to compute T'(t) directly, however, and "compensate" by multiplying by the factor 1/(ds/dt)). Any non-zero(!) vector is equal to the product of its magnitude times a unit vector in its direction. For dT/ds, the magnitude is defined to be the curvature, and the unit vector is defined to be the unit normal N(t). This essentially coincides with what was done last time, when curvature was defined to be d(theta)/ds but, as a student remarked and I tried uncomfortably to acknowledge, there could be problems if dT/ds is zero or if d(theta)/ds was negative (example: look at how T and N change for y=x^{3} as x goes from less than 0 to greater than 0). For the helix, we computed ds/dt (sqrt(a^{2}+b^{2})) and T(t) (1/sqrt(a^{2}+b^{2}))(-a sint(t)i +a cos(t)j +bk) and also N(t) (-cos(t)i-sin(t)j, always pointing directly towards the axis of symmetry) and k, which was a/(a^{2}+b^{2}). I strongly suggested "checking" this computation by looking at what the formula "says" when a and b are large and small, and comparing this to the curves. We "complete" T and N to what is called a 3-dimensional frame by defining the binormal B(t) to be the cross-product of T(t) and N(t). Since T(t) and N(t) are orthogonal unit vectors, B(t) is a unit vector orthogonal to both of them. (This needs some thinking about, using properties of cross-product!). How does B(t) change? Since B(t)·B(t)=1, differentiation results in 2 B'(t)·B(t)=0, so B'(t) is orthogonal to B(t). But differentiation of B(t)=T(t)xN(T) results in B'(t)=T'(t)xN(t)+T(t)xN'(t). Since T'(t) is parallel to N(t), the first product is 0 (another property of cross-product!) so that B'(t) is a cross-product of T(t) with something. Therefore B'(t) is also perpendicular to T(t). Well: B'(t) is perpendicular to both T(t) and B(t), and therefore, since only one direction is left, B'(t) must be a scalar multiple of N(t). The final important definition here for space curves is: dB/ds is a product of a scalar and N(t). The scalar is - t. That is supposed to be the Greek letter tau, and the minus sign is put there so that examples (the most important is coming up!) will work out better. This quantity is called torsion, and is a measure of "twisting", how much a curve twists out of a plane. If a space curve does lie in a plane, and if everything is nice and continuous, then B will always point in one direction (there are only two choices for B, "up" and "down" relative to the plane, and by continuity only one will be used) so that the torsion is 0 since B doesn't change. The converse implication (not verified here!) is also true: if torsion is always 0, then the curve must lie in a plane! For our example, we computed B(t) by directly computing the cross-product of T(t) and N(t). We got (I think!) (1/(1/sqrt(a^{2}+b^{2}))(b sin(t)i-a cos(t)j+a k) for B(t). This can be "checked" in several ways. First, that the candidate for B(t) has unit length, and then, that B(t) is orthogonal to both T(t) and N(t). This candidate passes those tests. Then we took d/dt of this B(t) and multiplied it by 1/(ds/dt)=(1/sqrt(a^{2}+b^{2})). The result was (b/(a^{2}+b^{2}))(cos(t)i+sin(t)j). Checking all the minus signs (one in the definition of torsion and one in the result of N(t)) shows that here torsion is (b/(a^{2}+b^{2})). Looking at the extreme values of a and b in this expression (a, b separately big and small) is not as revealing and/or as useful as with curvature, since a "feeling" for torsion isn't as immediate. Then I looked at dN/ds, using the expression N=BxT. The result, after using the product rule carefully (remember that this product is not commutative!) is (dB/ds)xT+Bx(dT/ds) which, by the earlier equations, is -tNxT+kBxN which is tB-kT. So we have the following equations, called the Frenet-Serret equations (also called Darboux equations in mechanics): dT/ds= 0 + kN + 0 dN/ds=-kT+ 0 + tB db/ds= 0 - tN + 0This is a collection of 3 3-dimensional vector equations, or a collection of 9 scalar differential equations. The remarkable fact is that if an initial point is specified for the curve, and an initial "frame" for the Frenet frame of T, N, and B, and if the curvature and torsion are specified, then the solutions to the differential equations above give exactly one curve. All the information about the curve is contained in the equations. So, for example, the motion of an airplane or a robot arm or the (geometric) structure of a long molecule are, in some sense, completely specified by k and t. Of course, this doesn't tell you really how to effectively control something so it moves or twists the way it is "supposed" to. The idea that the Frenet frame "evolves" in time, governed by the differential equations above, is useful. Here are some pictures of various helices produced by Maple (the plural of "helix" is "helices"). The pictures below were produced using the command spacecurve([a*cos(t),a*sin(t),5*t],t=0..6*Pi,axes=normal,color=black,thickness=2, scaling=constrained); where a is 1 and 10 and 100 respectively. The procedure spacecurve is loaded as part of plots using the command with(plots);. I used the option scaling=constrained in order to "force" Maple to display the three curves with similar spacing on the axes. Otherwise the x and y variables would be much altered in each image. I hope that these pictures give some idea of what the curvature and torsion represent.
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9/18/2002 | I wrote some simple vector differentiation "rules", dealing
with how to differentiate A(t)+B(t) and f(t)A(t) and A(t)·B(t)
and A(t)xB(t) if A(t) and B(t) are differentiable vector
functions of t and f(t) is a differentiable scalar function of t. I
miswrote one of these simple (!) rules, so was condemned to write out
a proof until I found the error. I am sorry. Then I tried to analyze the idea of how a curve bends. Curvature will be a measure of this bending.(Today a plane curve, in R^{2}, and tomorrow a space curve, in R^{3}.) I began an analysis that was apparently first done by Euler in about 1750. A curve is a parametric curve, where a point's position at "time" t is given by a pair of functions, (x(t),y(t)). Equivalently, we study a position vector, r(t)=x(t)i+y(t)j. Here x(t) and y(t) will be functions which I will feel free to differentiate as much as I want. There are special test cases which I will want to keep in mind. A straight line does NOT bend, so it should have curvature 0. A circle should have constant curvature, since each little piece of a circle of radius R>0 is congruent to each other little piece, and, in fact, the curvature should get large with R gets small (R is positive), and should get small when R gets large (and looks more like a line locally). I also suggested that even y=x^{2} might be a good test to keep in mind, since there the curvature should be an even (symmetric with respect to the y-axis) function of x, and should be bell-shaped, with max at 0 and limits 0 as x goes to +/- infinity. The problem is to somehow extract the geometric information from the parameterized curve. That is, if a particle moves faster, say, along a curve, it could seem like the same curve bends more. So what can we do? We looked at theta, the angle that the velocity vector r'(t) makes with respect to the x-axis. How does theta change? After some discussion it was suggested that we look at the rate of change with respect to arclength along the curve: that is the same as asking for the rate of change with respect to travel along the curve at unit speed, and therefore somehow the kinetic information will not intrude on the geometry. Arc length on a curve is computable with a definite integral: sqrt(x'^{2}+y'^{2}) integrated from t_{0} to t with dt is the arc length. This is rarely exactly computable with antidifferentiation using the usual family of functions. But ds/dt is just sqrt(x'^{2}+y'^{2}) by the Fundamental Theorem of Calculus. And the Chain Rule suggests that d*/ds(ds/dt)=d*/dt if * is some quantity of interest, such as theta. By drawing a triangle we see that theta is arctan of y'/x'. Differentiation with respect to t shows that d(theta)/dt must be (y''x'-x''y')/(x'^{2}+y^{2})^{2} (this uses the formula for the derivative of arctan, the Chain Rule, and the quotient rule. Then the previous results say that Then we saw that this formula for a straight line gave 0, and this formula for a circle was 1/R, where R is the radius of the circle. We used y=mx+b for the line (so x=t and y=mt+b) and x=Rcos(t) and y=Rsin(t) for the circle. This fit well with the examples suggested earlier. And, in fact, on the curve y=x^{2}, with the parameterization x=t and y=t^{2}, the d(theta)/ds gave 4/(1+4x^2)^{3/2}, also consistent with earlier considerations. d(theta)/ds is curvature, usually called k (Greek letter kappa). I defined the unit tangent vector, T, to be a unit vector in the direction of r'(t). Therefore r'(t)=(ds/dt)T, where ds/dt is the length of r'(t), and this is the speed. I differentiated the formula for r'(t) using one of the product rules we had stated earlier. Therefore I got r''(t)=(d^{2}s/dt^{2}) T+ (ds/dt)d/dt(T). But T is cos(theta)i+sin(theta)j, and differentiation with respect to t is the same as differentiation with respect to s multiplied by ds/dt. But differentiation with respect to s gives (-sin(theta)i+cos(theta)j) multiplied by the derivative of theta with respect to s, and this is k. All this put together is: We have decomposed acceleration into the normal and tangential directions. I used this to show that notion in a straight line (k= 0) had no normal component, and therefore a particle moving in a straight line had no force needed transverse to its motion. On the other hand, in our circular situation, the curvature was a positive number, and as long as the particle was moving (ds/dt not equal to 0) a force was needed to keep it one the circle. This is because the curvature k was non-zero, and so were the other terms. This is not at all "intuitively clear" to me. | ||||||||||||||||||||||
9/16/2002 | I'll go to a lecture which will finish at about 7:30 PM,
tomorrow, Tuesday. I will go to Hill 304 and I will be
available for questions from my arrival until 9:00 PM. I
reserve the right to go home, however, if no one wants to talk to me.
We continued with the problem from last time: p=(3,2,-1) and q=(2,0,1) and r=(1,1,2) are three points in space. Can I describe a simple way to tell if the point (x,y,z) is on the plane determined by these three points? Here is an method. Suppose v is the vector from p to q (so v is -i-2j+2k) and w is the vector from p to r (so w is -2i-j+3k) Then vxw is -4i-j-3k, a vector normal (perpendicular, orthogonal) to the plane. If a=(x,y,z), then a is on the plane determined by p and q and r if the vector from p to a is orthogonal to vxw=-4i-j-3k. This means that (x-3)(-4)+(y-2)(-1)+(z--1)(-3)=0, which simplifies to -4x-y-3z+11=0. All the steps of this process are reversible, so (x,y,z) is on the plane exactly when that equation is satisfied. More generally, the points whose coordinates (x,y,z) satisfy Ax+By+Cz+D=0 (with N=Ai+Bj+Ck NOT zero) form a plane, with normal vector N. We easily checked by direct substitution that (1,2,3) is not on the plane. Another parametric description of this plane is obtained by adding the vector from 0 to p to scalar multiples of the vectors v and w: the result must be on the plane. So if s and t are any numbers, then the vector 3i+2j-k (from 0 to p) +tv+sw is on the plane. This means (looking at components) if x and y and z satisfy: x=3+-t+-2s y=2+-2t+-1s z=-1+2t+3sfor some real numbers s and t, they must be on the plane. I substituted this into the equation -4x-y-3z+11=0 and checked that everything canceled. What is the distance of Fred=(1,2,3) to the plane described above? We found two ways to do this. First, take a point on the plane: we took p=(3,2,-1). Then I drew a picture to convince people that the distance would be the "projection" of the line segment from Fred to p on a vector normal to the plane. That is, we would need to multiply the distance from Fred to p by the cosine of the angle between the Fred-to-p vector and a normal. We have such a normal (it is inherent in the equation of the plane), and we had such a vector. Then distance can then be computed with a dot product multiplied by the distance from Fred to p. We computed this. Here's an alternative way to get the distance: find the point (which we called Walter) on the plane which is closest to Fred. How could we find Walter? The vector from Fred to Walter is parallel to a normal to the plane, so the vector from Fred to Walter is a scalar multiple of any normal vector. We therefore got the equations (assuming now that Walter has coordinates (x,y,z)): x-1=-4t y-2=-1t z-3=-3twhere t is the scalar. Then substituting x=-4t+1 and y=-t+2 and z=-3t+3 into the equation of the plane (-4x-y-3z+11=0) got one value of t, and this value of t gave the coordinates of Walter. And the distance from Fred to Walter is the distance from the plane to the point. More generally now I started talking about vector functions of a scalar variable. Here R(t)=x(t)i+y(t)j+z(t)k. This describes the geometry (the path) and kinematics (movement) of a particle. I illustrated this by playing around with the equations x(t)=-4t+1 and y(t)=-1t+2 and z(t)=-3t+3. What is the geometric object described by: x(t)=-4t+1 and y(t)=-1t+2 and z(t)=-3t+3. a line x(t)=-8t+1 and y(t)=-2t+2 and z(t)=-6t+3. the same line x(t)=4t+1 and y(t)=1t+2 and z(t)=3t+3. again the same line! x(t)=-4t^{2}+1 and y(t)=-1t^{2}+2 and z(t)=-3t^{2}+3. a closed half-line (a ray) x(t)=-4(sin(t))+1 and y(t)=-1(sin(t))+2 and z(t)=-3(sin(t))+3. a closed line segment The motion on the second line is in the direction of the first but twice as fast. The third line's motion is opposite the first. The t2 in the fourth gives an up and down effect from infinity to (1,2,3). The last example just oscillates back and forth on an interval. I recommended that students use the Maple procedure spacecurve to "see" what curves can look like. So motion can be complicated. I very briefly discussed what it means for a vector function to be differentiable: this works out to be the same as differentiability "in parallel" for each of the components. The same for integration. Then I began to discuss what R'(t), usually called the velocity vector, really means in terms of particle motion: the magnitude is the speed, and the direction is tangent to the curve. I needed to give some mechanical illustration of what a tangent vector to a curve might be. We are currently skipping 12.6 and are jumping right into 13.1 and 13.2. | ||||||||||||||||||||||
9/12/2002 | The Maple field trip. Students worked through several pages of problems designed to give them some familiarity with Maple. | ||||||||||||||||||||||
9/11/2002 | We returned to considering the 3-dimensional vectors v=3i+j-k
and w=4i+2j+3k. Writing a vector as a sum of perpendicular and
parallel parts (compared to another vector) was vaguely (!) motivated
by a picture of a block sliding on an inclined plane. We were able to
write v as a sum: v_{perp} + v_{parallel}, where v_{perp} was perpendicular
to w and v_{parallel} was parallel to w. We did this by finding
v_{parallel} first: its direction was the direction of w, so a unit
vector in w's direction was created by writing (1/|w|)w. The magnitude
of v_{parallel} was obtained by looking at a triangle in the plane of v
and w: the magnitude was |v|cos theta, where theta was the angle
between v and w. Luckily we know cos theta from previous work with the
dot product. So the magnitude is v · w /|w|. We computed all
this and got v_{parallel}. v_{perp} was obtained by writing v_{perp} = v -
v_{parallel}. A simple check was suggested: w· v_{perp} was to be
0, since the vectors are supposed to be perpendicular. Indeed (thank
goodness!) this dot product was 0. My personal success rate with hand
computation of this kind is not high. A new definition: vectors are
orthogonal if their dot product is 0. I introduced a new product, called the cross product or the vector product. There are dot products in every dimension. Dot products, however, give scalars as the result. The cross product is more-or-less unique to 3 dimensions and involves making a choice of "handedness". Some people feel this is rather important to physical reality. Related to this is the concept of chirality, important in chemistry and physics as well as mathematics. But enough diversions! What's vxw? Here is what the text calls the physics definition:
I stated further properties of x:
I applied those results to compute vxw, where v and w were the vectors we used earlier in the lecture. We distributed addition across x and also let the scalars "float" to the front. The multiplication table written above was used, and we finally got a result. More generally, a convenient algebraic method of computing vxw was stated using determinants. The determinant of a 2-by-2 array (a matrix) | a b | | c d |is ad-bc, while the determinant of a 3-by-3 array | a b c | | c d e | | f g h |is a(det I) - b (det II) +c (det III) where I II III || || || | d e | | c e | | c d | | g h | | f h | | f g |(these are called the minors of the larger matrix). There are many minus signs involved and ample opportunity for error. A course in linear algebra (Math 250 here) will explain why these formulas are interesting, but right now all I want are the definitions. If v=ai+bj+ck and w=di+ej+fk, then vxw= the determinant of | i j k | | a b c | | d e f |and this follows from the linearity in each factor and the entries of the multiplication table for x. We checked that this works for the specific v and w we started with. I began a geometric application of · and x which I'll finish next time. Students should read 12.3, 12.4, and begin 12.5. Class tomorrow is a Maple field trip, to ARC 118. | ||||||||||||||||||||||
9/9/2002 |
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9/5/2002 | The instructor asked students to work on problem 40 of
section 12.1, in groups or individually. The work was collected. We introduced vectors: directed line segments, "arrows" going from a tail to a head, quantities with magnitude and direction. We gave some simple physical interpretations (force, velocity, etc.). Two vector "representations" will be the same if the arrows are parallel and have the same length. Vector addition is motivated by simple physical experiments. It is defined by looking at the heads and tails of the summands in the correct order. The vector sum of two vectors is another vector. Then vector addition is commutative and associative, and these "facts" can be verified (at least in R^{3}) geometrically. The zero vector, 0, is special, with head=tail. It is an additive identity: any vector+0=the vector. And vectors have additive inverses, defined by reversing the assignment of head and tail. The sum of a vector and its additive inverse is the zero vector. "Scalar" multiplication was discussed. The word scalar is a somewhat antique usage, and here will mean a real number. If v is a vector and c is a scalar, then cv is another vector. If c=0, cv is the 0 vector. If c>0, then cv is a vector in the direction of v, whose length is v's multiplied by c. And if c<0, then cv is a vector whose direction is opposite v's direction, with length is v's multiplied by -c. Scalar multiplication satisfies various properties (all in section 12.2 of the text). We discussed the norm or length or magnitude of a vector, v: |v|. It is the distance from the head to the tail. It is non-negative and satisfies various properties. The most subtle is |v+w|=<|v|+|w|, the vector version of the triangle inequality. And |cv|=|c| |v|. Then we began to use this to give over- and under-estimates of various vector sums. This will be continued next time. Some time was devoted to discussing ideas about the first problem of the workshop set, and just a very small amount of time devoted to the second problem. | ||||||||||||||||||||||
9/4/2002 | The instructor discussed the course and collected student
information. Brief explanation of why the study of calculus in
R^{n} rather than just R^{2} or R^{3} might be
interesting was given. Presentation of rectangular coordinate systems (how to locate points) in R^{1} (the real line), R^{2} (the plane), and R^{3} (space), followed by generalization to R^{n}. Distance introduced: this is a nonnegative real number. On the line, the distance between p and q, if these have coordinates a and b, say, is |a-b| or sqrt((a-b)^{2}). "Simple" properties of this distance were given:
A distance formula in R^{2} was suggested using the Pythagorean Theorem: the square root of the sum of the squares of the differences in coordinates of the points. Verification of the first two properties of distance for the plane suggested by those of the line was immediate. A rather lengthy algebraic verification of the third property was given. A sequence of reversible algebraic steps was applied to the inequality suggested until a statement about squares being nonnegative was obtained. Generalization of the formula to R^{n} was given. A name was given to the third property of distance: the triangle inequality, and an appropriate picture was drawn: geometrically, the length of one side of a triangle is "clearly" less than or equal to the sum of the lengths of the other two sides. |
Maintained by greenfie@math.rutgers.edu and last modified 10/15/2002.