Foundation Chap 3

# Foundation Chap 3 - Chapter 3 The Discrete vs The...

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Chapter 3 The Discrete vs. The Continuous § 3.1 Plotting Continuous Functions Connecting Dots § 3.2 Simulating Real Arithmetic Finite Calculation It easy to forget that there is actually a boundary between continuous mathematics and digital computing: Display monitors are an array of dots. However, the dots are so tiny that the depiction of a continuous function like sin( x ) actually looks continuous on the screen. Computer arithmetic is inexact. However, the hardware can support so many digits of numerical precision that there is the appearance of perfect computation. We begin to think that one-third is .333333333333333. In this chapter we build a respect for these illusions and an appreciation for what they hide. Visualization is central to computational science and engineering. In many applications, the volume of numerical data that makes up “the answer” is too much for the human mind to assimilate in tabular form. On the other hand, the visual display of results enables the computational scientist to spot patterns that would otherwise be hidden. To illustrate these points we consider in § 3.1 the practical exercise of plotting a simple function across an interval. Screen granularity and human perception affect how we think about the underlying table of function evaluations. What does it take to make a smooth function look smooth? Similar issues attend the boundary between exact arithmetic and computer arithmetic which we discuss in § 3.2. Thinking of the computer as a kind of telescopic instrument, rounding errors affect its resolution. Just as dish vibrations are taken into account by radio astronomers, so should the aberrations of computer arithmetic be taken into consideration by the computational scientist. 1

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Example 3.1 A Function Plot % Script Eg3_1 % Plots the function f(x) = sin(5x) * exp(x/2) / (1 + x^2) % on the interval [-2,3]. L = -2; % Left endpoint R = 3; % Right endpoint N = 100; % Number of sample points x = linspace(L,R,N); % Array of f-evaluations... y = sin(5*x) .* exp(-x/2) ./ (1 + x.^2); plot(x,y,[L R],[0 0],’:’) title(’The function f(x) = sin(5x) * exp(x/2) / (1 + x^2)’) ylabel(’y = f(x)’) xlabel(’x’,) Output -2 -1 0 1 2 3 -1.5 -1 -0.5 0 0.5 1 The function f(x) = sin(5x) * exp(x/2) / (1 + x 2 ) y = f(x) x 2
3.1. Plotting Continuous Functions 3 3.1 Plotting Continuous Functions Problem Write a script that displays a plot of the function f ( x ) = sin(5 x ) exp( - x/ 2) 1 + x 2 across the interval [ - 2 , 3]. Program Development Let us first consider a much simpler problem: the plotting of the sine function across the interval [0 , 2 π ]. Even more, let us consider how we would approach such a problem “by hand.” First, we would produce a table of values, e.g., x 0.0000 1.5708 3.1416 4.7124 6.2832 sin( x ) 0.0000 1.0000 0.0000 -1.0000 0.0000 We would then connect the five “dots” (0 . 0000 , 0 . 0000) , (1 . 5706 , 1 . 0000) , (3 . 1416 , 0 . 0000) , (4 . 7124 , - 1 . 0000) , (6 . 2832 , 0 . 0000) obtaining the simple plot that is illustrated in Figure 3.1. It is hard to be happy with such -1 0 1 2 3 4 5 6 7 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 sin(x) Figure 3.1: Plot of the Sine Function with 5 Sample Points a coarse depiction of such a smooth function. Five evenly distributed sample points means an x -spacing of π/ 2 and that is just too crude.

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4 Chapter 3. The Discrete vs. The Continuous
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• Summer '07
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