Przeglądaj wersję html pliku:

### Matlab Introduction (ang)

An Introduction to Matlab
Version 2.3

David F. Griﬃths
Department of Mathematics The University Dundee DD1 4HN
With additional material by Ulf Carlsson Department of Vehicle Engineering KTH, Stockholm, Sweden

Copyright c 1996 by David F. Griﬃths. Amended October, 1997, August 2001, September 2005. This introduction may be distributed provided that it is not be altered in any way and that its source is properly and completely speciﬁed.

Contents
1 MATLAB 2 Starting Up 2.1 Windows Systems . 2.2 Unix Systems . . . . 2.3 Command Line Help 2.4 Demos . . . . . . . . 3 Matlab as a Calculator 4 Numbers & Formats 5 Variables 5.1 Variable Names . . . . . . . . . . . . 6 Suppressing output 7 Built–In Functions 7.1 Trigonometric Functions . . . . . . . 7.2 Other Elementary Functions . . . . . 8 Vectors 8.1 The Colon Notation . . . . 8.2 Extracting Bits of a Vector 8.3 Column Vectors . . . . . . . 8.4 Transposing . . . . . . . . . 9 Keeping a record 10 Plotting Elementary Functions 10.1 Plotting—Titles & Labels . . 10.2 Grids . . . . . . . . . . . . . . 10.3 Line Styles & Colours . . . . 10.4 Multi–plots . . . . . . . . . . 10.5 Hold . . . . . . . . . . . . . . 10.6 Hard Copy . . . . . . . . . . 10.7 Subplot . . . . . . . . . . . . 10.8 Zooming . . . . . . . . . . . . 10.9 Formatted text on Plots . . . 10.10Controlling Axes . . . . . . . 11 Keyboard Accelerators 2 2 2 2 2 3 3 3 3 3 4

15 Examples in Plotting

13

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

16 Matrices—Two–Dimensional Arrays 13 16.1 Size of a matrix . . . . . . . . . . . . 14 16.2 Transpose of a matrix . . . . . . . . 14 16.3 Special Matrices . . . . . . . . . . . 14 16.4 The Identity Matrix . . . . . . . . . 14 16.5 Diagonal Matrices . . . . . . . . . . 15 16.6 Building Matrices . . . . . . . . . . . 15 16.7 Tabulating Functions . . . . . . . . . 15 16.8 Extracting Bits of Matrices . . . . . 16 16.9 Dot product of matrices (.*) . . . . 16 16.10Matrix–vector products . . . . . . . 16 16.11Matrix–Matrix Products . . . . . . . 17 16.12Sparse Matrices . . . . . . . . . . . . 17 17 Systems of Linear Equations 18 17.1 Overdetermined system of linear equations . . . . . . . . . . . . . . . . . . 18 20

4 4 4 18 Characters, Strings and Text

. . . .

. . . .

. . . .

. . . .

. . . .

20 4 19 Loops 5 20 Logicals 21 5 20.1 While Loops . . . . . . . . . . . . . . 22 5 20.2 if...then...else...end . . . . . . 23 5 6 21 Function m–ﬁles 23 21.1 Examples of functions . . . . . . . . 24 25 25 25 26 26 27 27 27 28 29

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

6 22 Further Built–in Functions 7 22.1 Rounding Numbers . . . . 7 22.2 The sum Function . . . . . 7 22.3 max & min . . . . . . . . . 7 22.4 Random Numbers . . . . 7 22.5 find for vectors . . . . . . 8 22.6 find for matrices . . . . . 8 8 23 Plotting Surfaces 8 9 24 Timing 9 25 On–line Documentation

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

12 Copying to and from Word and other 26 Reading and Writing Data Files 29 applications 10 26.1 Formatted Files . . . . . . . . . . . . 30 12.1 Window Systems . . . . . . . . . . . 10 26.2 Unformatted Files . . . . . . . . . . 30 12.2 Unix Systems . . . . . . . . . . . . . 10 27 Graphic User Interfaces 31 13 Script Files 10 28 Command Summary 32 14 Products, Division & Powers of Vectors 11 14.1 Scalar Product (*) . . . . . . . . . . 11 14.2 Dot Product (.*) . . . . . . . . . . . 11 14.3 Dot Division of Arrays (./) . . . . . 12 14.4 Dot Power of Arrays (.^) . . . . . . 12

1

1

MATLAB
• Matlab is an interactive system for doing numerical computations. • A numerical analyst called Cleve Moler wrote the ﬁrst version of Matlab in the 1970s. It has since evolved into a successful commercial software package.

• from the separate Help window found under the Help menu or • from the Matlab helpdesk stored on disk or on a CD-ROM. Another useful facility is to use the ’lookfor keyword’ command, which searches the help ﬁles for the keyword. See Exercise 16.1 (page 17) for an example of its use.

• Matlab relieves you of a lot of the mundane tasks associated with solving problems nu2.2 Unix Systems merically. This allows you to spend more time thinking, and encourages you to experiment. • You should have a directory reserved for saving ﬁles associated with Matlab. Create such • Matlab makes use of highly respected algoa directory (mkdir) if you do not have one. rithms and hence you can be conﬁdent about Change into this directory (cd). your results. • Start up a new xterm window (do xterm & in • Powerful operations can be performed using the existing xterm window). just one or two commands. • Launch Matlab in one of the xterm windows • You can build up your own set of functions with the command for a particular application. • Excellent graphics facilities are available, and A the pictures can be inserted into L TEX and Word documents. These notes provide only a brief glimpse of the power and ﬂexibility of the Matlab system. For a more comprehensive view we recommend the book Matlab Guide D.J. Higham & N.J. Higham SIAM Philadelphia, 2000, ISBN: 0-89871-469-9. matlab After a short pause, the logo will be shown followed by a window containing the Matlab interface. Should you wish to run Matlab in an xterm window, use the command matlab -nojvm and, following dislpay of the logo, the Matlab prompt >> will appear. Type quit at any time to exit from Matlab.

2
2.1

Starting Up
Windows Systems 2.3

Command Line Help

On Windows systems MATLAB is started by doubleclicking the MATLAB icon on the desktop or by selecting MATLAB from the start menu. The starting procedure takes the user to the Command window where the Command line is indicated with ’>>’. Used in the calculator mode all Matlab commands are entered to the command line from the keyboard. Matlab can be used in a number of diﬀerent ways or modes; as an advanced calculator in the calculator mode, in a high level programming language mode and as a subroutine called from a C-program. More information on the ﬁrst two of these modes is given below. Help and information on Matlab commands can be found in several ways,

Help is available from the command line prompt. Type help help for “help” (which gives a brief synopsis of the help system), help for a list of topics. The ﬁrst few lines of this read
HELP topics: matlab/general matlab/ops matlab/lang matlab/elmat matlab/elfun matlab/specfun General purpose commands. Operators and special char... Programming language const... Elementary matrices and ma... Elementary math functions. Specialized math functions.

(truncated lines are shown with . . . ). Then to obtain help on “Elementary math functions”, for instance, type

• from the command line by using the ’help >> help elfun topic’ command (see below), 2

This gives rather a lot of information so, in order to see the information one screenful at a time, ﬁrst issue the command more on, i.e., >> more on >> help elfun Hit any key to progress to the next page of information.

Command >>format short >>format short e >>format long e >>format short >>format bank

Example of Output 31.4162(4–decimal places) 3.1416e+01 3.141592653589793e+01 31.4162(4–decimal places) 31.42(2–decimal places)

2.4

Demos

Demonstrations are invaluable since they give an indication of Matlabs capabilities. A comprehensive set are available by typing the command >> demo ( Warning: this will clear the values of all current variables.)

format—how Matlab prints numbers—is controlled by the “format” command. Type help format for full list. Should you wish to switch back to the default format then format will suﬃce. The command format compact is also useful in that it suppresses blank lines in the output thus allowing more information to be displayed.

3

Matlab as a Calculator

5

Variables

The basic arithmetic operators are + - * / ^ and these are used in conjunction with brackets: ( ). The symbol ^ is used to get exponents (powers): 2^4=16. You should type in commands shown following the prompt: >>. >> 2 + 3/4*5 ans = 5.7500 >> Is this calculation 2 + 3/(4*5) or 2 + (3/4)*5? Matlab works according to the priorities: 1. quantities in brackets, 2. powers 2 + 3^2 ⇒2 + 9 = 11, 3. * /, working left to right (3*4/5=12/5), 4. + -, working left to right (3+4-5=7-5), Thus, the earlier calculation was for 2 + (3/4)*5 by priority 3.

>> 3-2^4 ans = -13 >> ans*5 ans = -65 The result of the ﬁrst calculation is labelled “ans” by Matlab and is used in the second calculation where its value is changed. We can use our own names to store numbers: >> x = 3-2^4 x = -13 >> y = x*5 y = -65 so that x has the value −13 and y = −65. These can be used in subsequent calculations. These are examples of assignment statements: values are assigned to variables. Each variable must be assigned a value before it may be used on the right of an assignment statement.

4

Numbers & Formats
Examples 1362, −217897 1.234, −10.76 √ 3.21 − 4.3i (i = −1) Inﬁnity (result of dividing by 0) Not a Number, 0/0

Matlab recognizes several diﬀerent kinds of numbers

5.1

Variable Names

Type Integer Real Complex Inf NaN

Legal names consist of any combination of letters and digits, starting with a letter. These are allowable: NetCost, Left2Pay, x3, X3, z25c5 These are not allowable: Net-Cost, 2pay, %x, @sign Use names that reﬂect the values they represent. Special names: you should avoid using eps = 2.2204e-16 = 2−54 (The largest number such that 1 + eps is indistinguishable from 1) and pi = 3.14159... = π. If you wish to do arithmetic with complex numbers,both √ i and j have the value −1 unless you change them

The “e” notation is used for very large or very small numbers: -1.3412e+03 = −1.3412 × 103 = −1341.2 -1.3412e-01 = −1.3412 × 10−1 = −0.13412 All computations in MATLAB are done in double precision, which means about 15 signiﬁcant ﬁgures. The

3

>> i,j, i=3 ans = 0 + 1.0000i ans = 0 + 1.0000i i = 3

6

Suppressing output

One often does not want to see the result of intermediate calculations—terminate the assignment statement or expression with semi–colon >> x=-13; y = 5*x, z = x^2+y y = -65 z = 104 >> the value of x is hidden. Note also we can place several statements on one line, separated by commas or semi– colons. Exercise 6.1 In each case ﬁnd the value of the expression in Matlab and explain precisely the order in which the calculation was performed. i) iii) v) -2^3+9 3*2/3 (2/3^2*5)*(3-4^3)^2 ii) iv) vi)

>> x = 9; >> sqrt(x),exp(x),log(sqrt(x)),log10(x^2+6) ans = 3 ans = 8.1031e+03 ans = 1.0986 ans = 1.9395 exp(x) denotes the exponential function exp(x) = ex and the inverse function is log: >> format long e, exp(log(9)), log(exp(9)) ans = 9.000000000000002e+00 ans = 9 >> format short and we see a tiny rounding error in the ﬁrst calculation. log10 gives logs to the base 10. A more complete list of elementary functions is given in Table 2 on page 32.

8

Vectors

7
7.1

Built–In Functions

These come in two ﬂavours and we shall ﬁrst describe row vectors: they are lists of numbers separated by ei2/3*3 ther commas or spaces. The number of entries is known 3*4-5^2*2-3 3*(3*4-2*5^2-3) as the “length” of the vector and the entries are often referred to as “elements” or “components” of the vector.The entries must be enclosed in square brackets. >> v = [ 1 3, sqrt(5)] v = 1.0000 3.0000 >> length(v) ans = 3

Trigonometric Functions

Those known to Matlab are sin, cos, tan and their arguments should be in radians. e.g. to work out the coordinates of a point on a circle of radius 5 centred at the origin and having an elevation 30o = π/6 radians: >> x = 5*cos(pi/6), y = 5*sin(pi/6) x = 4.3301 y = 2.5000 The inverse trig functions are called asin, acos, atan (as opposed to the usual arcsin or sin−1 etc.). The result is in radians. >> acos(x/5), asin(y/5) ans = 0.5236 ans = 0.5236 >> pi/6 ans = 0.5236

2.2361

Spaces can be vitally important: >> v2 = [3+ 4 5] v2 = 7 5 >> v3 = [3 +4 5] v3 = 3 4 5 We can do certain arithmetic operations with vectors of the same length, such as v and v3 in the previous section. >> v + v3 ans = 4.0000 7.0000 7.2361 >> v4 = 3*v v4 = 3.0000 9.0000 6.7082 >> v5 = 2*v -3*v3 v5 = -7.0000 -6.0000 -10.5279 >> v + v2 ??? Error using ==> + Matrix dimensions must agree.

7.2

Other Elementary Functions

These include sqrt, exp, log, log10

4

i.e. the error is due to v and v2 having diﬀerent lengths. A vector may be multiplied by a scalar (a number— see v4 above), or added/subtracted to another vector of the same length. The operations are carried out elementwise. We can build row vectors from existing ones: >> w = [1 2 3], z = [8 9] >> cd = [2*z,-w], sort(cd) w = 1 2 3 z = 8 9 cd = 16 18 -1 -2 -3 ans = -3 -2 -1 16 18 Notice the last command sort’ed the elements of cd into ascending order. We can also change or look at the value of particular entries >> w(2) = -2, w(3) w = 1 -2 3 ans = 3

>> r5(3:6) ans = 5 -1

-3

-5

To get alternate entries: >> r5(1:2:7) ans = 1 5

-3

-7

What does r5(6:-2:1) give? See help colon for a fuller description.

8.3

Column Vectors

These have similar constructs to row vectors. When deﬁning them, entries are separated by ; or “newlines” >> c = [ 1; 3; sqrt(5)] c = 1.0000 3.0000 2.2361 >> c2 = [3 4 5] c2 = 3 4 5 >> c3 = 2*c - 3*c2 c3 = -7.0000 -6.0000 -10.5279 so column vectors may be added or subtracted provided that they have the same length.

8.1

The Colon Notation

This is a shortcut for producing row vectors: >> 1:4 ans = 1 >> 3:7 ans = 3 >> 1:-1 ans = []

2

3

4

4

5

6

7

8.4

Transposing

More generally a : b : c produces a vector of entries starting with the value a, incrementing by the value b until it gets to c (it will not produce a value beyond c). This is why 1:-1 produced the empty vector []. >> 0.32:0.1:0.6 ans = 0.3200 0.4200 >> -1.4:-0.3:-2 ans = -1.4000 -1.7000

We can convert a row vector into a column vector (and vice versa) by a process called transposing—denoted by ’. >> w, w’, c, c’ w = 1 -2 3 ans = 1 -2 3 c = 1.0000 3.0000 2.2361 ans = 1.0000 3.0000 >> t = w + 2*c’ t = 3.0000 4.0000 >> T = 5*w’-2*c T = 3.0000

0.5200

-2.0000

8.2

Extracting Bits of a Vector

>> r5 = [1:2:6, -1:-2:-7] r5 = 1 3 5 -1 To get the 3rd to 6th entries:

2.2361

-3

-5

-7

7.4721

5

-16.0000 10.5279 If x is a complex vector, then x’ gives the complex conjugate transpose of x: >> x = [1+3i, 2-2i] ans = 1.0000 + 3.0000i >> x’ ans = 1.0000 - 3.0000i 2.0000 + 2.0000i

v2 v3 v4 x y

1 1 1 1 1

by by by by by

2 3 3 1 1

2 3 3 1 1

16 24 24 8 8

Full Full Full Full Full

No No No No No

Grand total is 16 elements using 128 bytes 2.0000 - 2.0000i

10

Plotting Elementary Functions

Note that the components of x were deﬁned without a * operator; this means of deﬁning complex numbers works even when the variable i already has a numeric value. To obtain the plain transpose of a complex number use .’ as in >> x.’ ans = 1.0000 + 3.0000i 2.0000 - 2.0000i

Suppose we wish to plot a graph of y = sin 3πx for 0 ≤ x ≤ 1. We do this by sampling the function at a suﬃciently large number of points and then joining up the points (x, y) by straight lines. Suppose we take N + 1 points equally spaced a distance h apart: >> N = 10; h = 1/N; x = 0:h:1; deﬁnes the set of points x = 0, h, 2h, . . . , 1 − h, 1. Alternately, we may use the command linspace: The general form of the command is linspace (a,b,n) which generates n + 1 equispaced points between a and b, inclusive. So, in this case we would use the command >> x = linspace (0,1,11);

9

Keeping a record

Issuing the command The corresponding y values are computed by >> diary mysession >> y = sin(3*pi*x); will cause all subsequent text that appears on the screen to be saved to the ﬁle mysession located in the directory in which Matlab was invoked. You may use any legal ﬁlename except the names on and off. The record may be terminated by >> diary off The ﬁle mysession may be edited with your favourite editor (the Matlab editor, emacs, or even Word) to remove any mistakes. If you wish to quit Matlab midway through a calculation so as to continue at a later stage: >> save thissession will save the current values of all variables to a ﬁle called thissession.mat. This ﬁle cannot be edited. When you next startup Matlab, type >> load thissession and the computation can be resumed where you left oﬀ. A list of variables used in the current session may be seen with >> whos See help whos and help save. >> whos Name Size Elements Bytes ans 1 by 1 1 8 v 1 by 3 3 24 v1 1 by 2 2 16 Density Complex Full No Full No Full No and ﬁnally, we can plot the points with >> plot(x,y) The result is shown in Figure 1, where it is clear that the value of N is too small.

Figure 1: Graph of y = sin 3πx for 0 ≤ x ≤ 1 using h = 0.1.
On changing the value of N to 100: >> N = 100; h = 1/N; x = 0:h:1; >> y = sin(3*pi*x); plot(x,y) we get the picture shown in Figure 2.

6

The number of available plot symbols is wider than shown in this table. Use help plot to obtain a full list. See also help shapes.

10.4

Multi–plots

Several graphs may be drawn on the same ﬁgure as in >> plot(x,y,’w-’,x,cos(3*pi*x),’g--’) A descriptive legend may be included with >> legend(’Sin curve’,’Cos curve’) which will give a list of line–styles, as they appeared in the plot command, followed by a brief description. Matlab ﬁts the legend in a suitable position, so as not to conceal the graphs whenever possible. For further information do help plot etc. The result of the commands >> >> >> >> >> plot(x,y,’w-’,x,cos(3*pi*x),’g--’) legend(’Sin curve’,’Cos curve’) title(’Multi-plot ’) xlabel(’x axis’), ylabel(’y axis’) grid

Figure 2: Graph of y = sin 3πx for 0 ≤ x ≤ 1 using h = 0.01.

10.1

Plotting—Titles & Labels

To put a title and label the axes, we use >> title(’Graph of y = sin(3pi x)’) >> xlabel(’x axis’) >> ylabel(’y-axis’) The strings enclosed in single quotes, can be anything A of our choosing. Some simple L TEX commands are available for formatting mathematical expressions and Greek characters—see Section 10.9. See also ezplot the “Easy to use function plotter”.

is shown in Figure 3. The legend may be moved manually by dragging it with the mouse.

10.2
>> grid

Grids

A dotted grid may be added by

This can be removed using either grid again, or grid off.

10.3

Line Styles & Colours
Figure 3: Graph of y = sin 3πx and y = cos 3πx for 0 ≤ x ≤ 1 using h = 0.01.

The default is to plot solid lines. A solid white line is produced by >> plot(x,y,’w-’) The third argument is a string whose ﬁrst character speciﬁes the colour(optional) and the second the line style. The options for colours and styles are: Colours yellow magenta cyan red green blue white black Line Styles . point o circle x x-mark + plus solid * star : dotted -. dashdot -- dashed

10.5

Hold

y m c r g b w k

A call to plot clears the graphics window before plotting the current graph. This is not convenient if we wish to add further graphics to the ﬁgure at some later stage. To stop the window being cleared: >> plot(x,y,’w-’), hold on >> plot(x,y,’gx’), hold off “hold on” holds the current picture; “hold off” releases it (but does not clear the window, which can be done with clf). “hold” on its own toggles the hold state.

7

10.6

Hard Copy

To obtain a printed copy select Print from the File menu on the Figure toolbar. Alternatively one can save a ﬁgure to a ﬁle for later printing (or editing). A number of formats is available (use help print to obtain a list). To save a ﬁle in “Encapsulated PostScript” format, issue the Matlab command print -deps fig1 which will save a copy of the image in a ﬁle called fig1.eps.

factor of two. This may be repeated to any desired level. Clicking the right mouse button will zoom out by a factor of two. Holding down the left mouse button and dragging the mouse will cause a rectangle to be outlined. Releasing the button causes the contents of the rectangle to ﬁll the window. zoom off turns oﬀ the zoom capability. Exercise 10.1 Draw graphs of the functions y y = = cos x x

10.7

Subplot

The graphics window may be split into an m × n array of smaller windows into which we may plot one or more graphs. The windows are counted 1 to mn row–wise, starting from the top left. Both hold and grid work on the current subplot. >> >> >> >> >> >> >> >> subplot(221), plot(x,y) xlabel(’x’),ylabel(’sin 3 pi x’) subplot(222), plot(x,cos(3*pi*x)) xlabel(’x’),ylabel(’cos 3 pi x’) subplot(223), plot(x,sin(6*pi*x)) xlabel(’x’),ylabel(’sin 6 pi x’) subplot(224), plot(x,cos(6*pi*x)) xlabel(’x’),ylabel(’cos 6 pi x’)

for 0 ≤ x ≤ 2 on the same window. Use the zoom facility to determine the point of intersection of the two curves (and, hence, the root of x = cos x) to two significant ﬁgures. The command clf clears the current ﬁgure while close 1 will close the window labelled “Figure 1”. To open a new ﬁgure window type figure or, to get a window labelled “Figure 9”, for instance, type figure (9). If “Figure 9” already exists, this command will bring this window to the foreground and the result subsequent plotting commands will be drawn on it.

10.9

Formatted text on Plots

subplot(221) (or subplot(2,2,1)) speciﬁes that the window should be split into a 2 × 2 array and we select the ﬁrst subwindow.

It is possible to change to format of text on plots so as to increase or decrease its size and also to typeset A simple mathematical expressions (in L TEX form). We shall give two illustrations. First we plot the ﬁrst 100 terms in the sequence {xn } 1 n and then graph the function given by xn = 1 + n φ(x) = x3 sin2 (3πx) on the interval −1 ≤ x ≤ 1. The commands >> >> >> >> >> >> >> >> >> >> >> >> >> set(0,’Defaultaxesfontsize’,16); n = 1:100; x = (1+1./n).^n; subplot (211) plot(n,x,’.’,[0 max(n)],exp(1)*[1 1],... ’--’,’markersize’,8) title(’x_n = (1+1/n)^n’,’fontsize’,12) xlabel(’n’), ylabel(’x_n’) legend(’x_n’,’y = e^1 = 2.71828...’,4) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot (212) x = -2:.02:2; y = x.^3.*sin(3*pi*x).^2; plot(x,y,’linewidth’,2) legend(’y = x^3sin^2 3\pi x’,4) xlabel(’x’)

10.8

Zooming

We often need to “zoom in” on some portion of a plot in order to see more detail. Clicking on the “Zoom in” or “Zoom out” button on the Figure window is simplest but one can also use the command >> zoom Pointing the mouse to the relevant position on the plot and clicking the left mouse button will zoom in by a

produce the graph shown below. The salient features of these commands are 1. The ﬁrst line increases the size of the default font size used for the axis labels, legends and titles. 2. The size of the plot symbol “.” is changed from the default (6) to size 8 by the additional string followed by value “’markersize’,8”.

8

3. The strings x_n are formatted as xn to give subscripts while x^3 leads to superscripts x3 . Note also that sin2 3πx translates into the Matlab command sin(3*pi*x).^2—the position of the exponent is diﬀerent.

The axis command has four parameters, the ﬁrst two are the minimum and maximum values of x to use on the axis and the last two are the minimum and maximum values of y. Note the square brackets. The result of these commands is shown in Figure 4. Look at help axis and experiment with the commands axis equal, 4. Greek characters α, β, . . . , ω, Ω are produced by axis verb, axis square, axis normal, axis tight in the strings ’\alpha’, ’\beta’, . . . ,’\omega’, ’\Omega’. any order. the integral symbol: is produced by ’\int’. 5. The thickness of the line used in the lower graph is changed from its default value (0.5) to 2. 6. Use help legend to determine the meaning of the last argument in the legend commands. One can determine the current value of any plot property by ﬁrst obtaining its “handle number” and then using the get command such as >> handle = plot (x,y,’.’) >> get (handle,’markersize’) ans = 6 Experiment also with set (handle) (which will list possible values for each property) and set(handle,’markersize’,12) which will increase the size of the marker (a dot in this case) to 12. Also, all plot properties can be edited from the Figure window by selecting the Tools menu from the toolbar. For instance, to change the linewidth of a graph, ﬁrst select the curve by double clicking (it should then change its appearance) and then select Line Properties. . . from the Tools . This will pop up a dialogue window from which the width, colour, style,. . . of the curve may be changed.

Figure 4: The eﬀect of changing the axes of a plot.

11

Keyboard Accelerators

One can recall previous Matlab commands by using the ↑ and ↓ cursor keys. Repeatedly pressing ↑ will review the previous commands (most recent ﬁrst) and, if you want to re-execute the command, simply press the return key. To recall the most recent command starting with p, say, type p at the prompt followed by ↑. Similarly, typing pr followed by ↑ will recall the most recent command starting with pr. Once a command has been recalled, it may be edited (changed). You can use ← and → to move backwards and forwards through the line, characters may be inserted by typing at the current cursor position or deleted using the Del key. This is most commonly used when long command lines have been mistyped or when you want to re–execute a command that is very similar to one used previously. The following emacs–like commands may also be used:

10.10

Controlling Axes

Once a plot has been created in the graphics window you may wish to change the range of x and y values shown on the picture. >> clf, N = 100; h = 1/N; x = 0:h:1; >> y = sin(3*pi*x); plot(x,y) >> axis([-0.5 1.5 -1.2 1.2]), grid

cntrl cntrl cntrl cntrl cntrl

a e f b d

move to start of line move to end of line move forwards one character move backwards one character delete character under the cursor

Once you have the command in the required form, press return. Exercise 11.1 Type in the commands

9

>> >> >> >>

x = -1:0.1:1; plot(x,sin(pi*x),’w-’) hold on plot(x,cos(pi*x),’r-’)

12.2

Unix Systems

Now use the cursor keys with suitable editing to execute: >> x = -1:0.05:1; >> plot(x,sin(2*pi*x),’w-’) >> plot(x,cos(2*pi*x),’r-.’), hold off

12

Copying to and from Word and other applications

There are many situations where one wants to copy the output resulting from a Matlab command (or commands) into a Windows application such as Word or into a Unix ﬁle editor such as “emacs” or “vi”.

In order to carry out the following exercise, you should have Matlab running in one window and either Emacs or Vi running in another. To copy material from one window to another, (here l means click Left Mouse Button, etc) First select the material to copy by l on the start of the material you want and then either dragging the mouse (with the buttom down) to highlight the text, or r at the end of the material. Next move the mouse into the other window and l at the location you want the text to appear. Finally, click the m . When copying from another application into Matlab you can only copy material to the prompt line. On Unix systems ﬁgures are normally saved in ﬁles (see Section 10.6) which are then imported into other documents.

12.1

Window Systems

13

Script Files

Copying material is made possible on the Windows operating system by using the Windows clipboard. Also, pictures can be exported to ﬁles in a number of alternative formats such as encapsulated postscript format or in jpeg format. Matlab is so frequently used as an analysis tool that many manufacturers of measurement systems and software ﬁnd it convenient to provide interfaces to Matlab which make it possible, for instance, to import measured data directly into a *.mat Matlab ﬁle (see load and save in Section 9). Example 12.1 Copying a ﬁgure into Word. Diagrams prepared in Matlab are easily exported to other Windows applications such as Word. Suppose a plot of the functions sin(2πf t) and sin(2πf t + π/4), with f = 100, is needed in a report written in Word. We create a time vector, t, with 500 points distributed over 5 periods and then evaluate and plot the two function vectors. >> >> >> >> >> >> >> t = [1:1:500]/500/20; f = 100; y1 = sin(2*pi*f*t); y2 = sin(2*pi*f*t+pi/4); plot(t,y1,’-’,t,y2,’--’); axis([0 0.05 -1.5 1.5]); grid

Script ﬁles are normal ASCII (text) ﬁles that contain Matlab commands. It is essential that such ﬁles have names having an extension .m (e.g., Exercise4.m) and, for this reason, they are commonly known as m-ﬁles. The commands in this ﬁle may then be executed using >> Exercise4 Note: the command does not include the ﬁle name extension .m. It is only the output from the commands (and not the commands themselves) that are displayed on the screen. Script ﬁles are created with your favourite editor under Unix while, under Windows, click on the “New Document” icon at the top left of the main Matlab window to pop up a new window showing the “M-ﬁle Editor”. Type in your commands and then save (to a ﬁle with a .m extension). To see the commands in the command window prior to their execution: >> echo on and echo off will turn echoing oﬀ. Any text that follows % on a line is ignored. The main purpose of this facility is to enable comments to be included in the ﬁle to describe its purpose. To see what m-ﬁles you have in your current directory, use >> what Exercise 13.1 1. Type in the commands from §10.7 into a ﬁle called exsub.m. 2. Use what to check that the ﬁle is in the correct area. 3. Use the command type exsub to see the contents of the ﬁle. 4. Execute these commands. See §21 for the related topic of function ﬁles.

In order to copy the plot into a Word document • Select “Copy Figure” under the Edit menu on the ﬁgure windows toolbar. • Switch to the Word application if it is already running, otherwise open a Word document. • Place the cursor in the desired position in the document and select “Paste” under the “Edit” menu in the Word tool bar.

10

14
14.1

Products, Division & Powers of Vectors
Scalar Product (*)

ans = 365 >> v’*z ans = -96

% v & z are column vectors

We shall describe two ways in which a meaning may be attributed to the product of two vectors. In both cases the vectors concerned must have the same length. The ﬁrst product is the standard scalar product. Suppose that u and v are two vectors of length n, u being a row vector and v a column vector: v1  v2  v =  . .  .  . vn

We shall refer to the Euclidean length of a vector as the norm of a vector; it is denoted by the symbol u and deﬁned by
n

u =
i=1

|ui |2 ,

where n is its dimension. This can be computed in Matlab in one of two ways: >> [ sqrt(u*u’), norm(u)] ans = 19.1050 19.1050 where norm is a built–in Matlab function that accepts a vector as input and delivers a scalar as output. It can also be used to compute other norms: help norm. Exercise 14.1 The angle, θ, between two column vectors x and y is deﬁned by 20 −21 −22 cos θ = xy x y .

u = [u1 , u2 , . . . , un ] ,

The scalar product is deﬁned by multiplying the corresponding elements together and adding the results to give a single number (scalar).
n

uv =
i=1

u i vi .

For example, if u = [10, −11, 12], and v = then n = 3 and

Use this formula to determine the cosine of the angle between x = [1, 2, 3] and y = [3, 2, 1] .

u v = 10 × 20 + (−11) × (−21) + 12 × (−22) = 167. We can perform this product in Matlab by >> u = [ 10, -11, 12], v = [20; -21; -22] >> prod = u*v % row times column vector Suppose we also deﬁne a row vector w and a column vector z by >> w = [2, 1, 3], z = [7; 6; 5] w = 2 1 3 z = 7 6 5 and we wish to form the scalar products of u with w and v with z. >> u*w ??? Error using ==> * Inner matrix dimensions must agree. an error results because w is not a column vector. Recall from page 5 that transposing (with ’) turns column vectors into row vectors and vice versa. So, to form the scalar product of two row vectors or two column vectors, >> u*w’ ans = 45 >> u*u’ % u & w are row vectors

Hence ﬁnd the angle in degrees.

14.2

Dot Product (.*)

The second way of forming the product of two vectors of the same length is known as the Hadamard product. It is not often used in Mathematics but is an invaluable Matlab feature. It involves vectors of the same type. If u and v are two vectors of the same type (both row vectors or both column vectors), the mathematical deﬁnition of this product, which we shall call the dot product, is the vector having the components u · v = [u1 v1 , u2 v2 , . . . , un vn ]. The result is a vector of the same length and type as u and v. Thus, we simply multiply the corresponding elements of two vectors. In Matlab, the product is computed with the operator .* and, using the vectors u, v, w, z deﬁned on page 11, >> u.*w ans = 20 -11 36 >> u.*v’ ans = 200 231 -264 >> v.*z, u’.*v ans = 140 -126 -110 ans = 200 231 -264

% u is a row vector

11

Example 14.1 Tabulate the function y = x sin πx for x = 0, 0.25, . . . , 1. It is easier to deal with column vectors so we ﬁrst deﬁne a vector of x-values: (see Transposing: §8.4) >> x = (0:0.25:1)’; To evaluate y we have to multiply each element of the vector x by the corresponding element of the vector sin πx:

Warning: Divide by zero ans = 9 10 NaN 12

13

Here we are warned about 0/0—giving a NaN (Not a Number). Example 14.2 Estimate the limit lim
x→0

x 0 0.2500 0.5000 0.7500 1.0000

× × × × × ×

sin πx 0 0.7071 1.0000 0.7071 0.0000

= = = = = =

x sin πx 0 0.1768 0.5000 0.5303 0.0000

sin πx . x

To carry this out in Matlab: >> y = x.*sin(pi*x) y = 0 0.1768 0.5000 0.5303 0.0000 Note: a) the use of pi, b) x and sin(pi*x) are both column vectors (the sin function is applied to each element of the vector). Thus, the dot product of these is also a column vector.

The idea is to observe the behaviour of the ratio sinxπx for a sequence of values of x that approach zero. Suppose that we choose the sequence deﬁned by the column vector >> x = [0.1; 0.01; 0.001; 0.0001] then >> sin(pi*x)./x ans = 3.0902 3.1411 3.1416 3.1416 which suggests that the values approach π. To get a better impression, we subtract the value of π from each entry in the output and, to display more decimal places, we change the format >> format long >> ans -pi ans = -0.05142270984032 -0.00051674577696 -0.00000516771023 -0.00000005167713 Can you explain the pattern revealed in these numbers? We also need to use ./ to compute a scalar divided by a vector: >> 1/x ??? Error using ==> / Matrix dimensions must agree. >> 1./x ans = 10 100 1000 10000 so 1./x works, but 1/x does not.

14.3

Dot Division of Arrays (./)

There is no mathematical deﬁnition for the division of one vector by another. However, in Matlab, the operator ./ is deﬁned to give element by element division—it is therefore only deﬁned for vectors of the same size and type. >> a = 1:5, b = 6:10, a./b a = 1 2 3 4 5 b = 6 7 8 9 10 ans = 0.1667 0.2857 0.3750 0.4444 >> a./a ans = 1 1 1 1 1 >> c = -2:2, a./c c = -2 -1 0 1 2 Warning: Divide by zero ans = -0.5000 -2.0000 Inf 4.0000

0.5000

14.4

Dot Power of Arrays (.^)

2.5000

To square each of the elements of a vector we could, for example, do u.*u. However, a neater way is to use the .^ operator: >> u.^2 ans = 100 >> u.*u ans = 100

The previous calculation required division by 0—notice the Inf, denoting inﬁnity, in the answer. >> a.*b -24, ans./c ans = -18 -10 0

121

144

12

26

121

144

12

>> u.^4 ans = 10000 14641 20736 >> v.^2 ans = 400 441 484 >> u.*w.^(-2) ans = 2.5000 -11.0000 1.3333 Recall that powers (.^ in this case) are done ﬁrst, before any other arithmetic operation.

Exercise 15.1 Enter the vectors U = [6, 2, 4], V = [3, −2, 3, 0],

3  −4  W = , 2  −6 into Matlab.

3  2  Z= 2  7

15
i) iii)

Examples in Plotting
y= v=
sin x x x2 +1 2 −4 x

1. Which of the products U*V, V*W, U*V’, V*W’, W*Z’, U.*V U’*V, V’*W, W’*Z, U.*W, W.*Z, V.*W is legal? State whether the legal products are row or column vectors and give the values of the legal results. 2. Tabulate the functions y = (x2 + 3) sin πx2 and z = sin2 πx/(x−2 + 3) for x = 0, 0.2, . . . , 10. Hence, tabulate the function (x2 + 3) sin πx2 sin2 πx . w= (x−2 + 3) Plot a graph of w over the range 0 ≤ x ≤ 10.

Example 15.1 Draw graphs of the functions ii) iv) u= w=
1 +x (x−1)2 (10−x)1/3 −2 (4−x2 )1/2

for 0 ≤ x ≤ 10. >> x = 0:0.1:10; >> y = sin(x)./x; >> subplot(221), plot(x,y), title(’(i)’) Warning: Divide by zero >> u = 1./(x-1).^2 + x; >> subplot(222),plot(x,u), title(’(ii)’) Warning: Divide by zero >> v = (x.^2+1)./(x.^2-4); >> subplot(223),plot(x,v),title(’(iii)’) Warning: Divide by zero >> w = ((10-x).^(1/3)-1)./sqrt(4-x.^2); Warning: Divide by zero >> subplot(224),plot(x,w),title(’(iv)’)

16

Matrices—Two–Dimensional Arrays

Row and Column vectors are special cases of matrices. An m × n matrix is a rectangular array of numbers having m rows and n columns. It is usual in a mathematical setting to include the matrix in either round or square brackets—we shall use square ones. For example, when m = 2, n = 3 we have a 2 × 3 matrix such as 5 7 9 A= 1 −3 −7 To enter such an matrix into Matlab we type it in row by row using the same syntax as for vectors: >> A = [5 7 9 1 -3 -7] A = 5 7 9 1 -3 -7 Rows may be separated by semi-colons rather than a new line:

>> B = [-1 2 5; 9 0 5] Note the repeated use of the “dot” operators. B = Experiment by changing the axes (page 9), grids (page 7) -1 2 5 and hold(page 7). 9 0 5 >> subplot(222),axis([0 10 0 10]) >> C = [0, 1; 3, -2; 4, 2] >> grid C = >> grid 0 1 >> hold on 3 -2 >> plot(x,v,’--’), hold off, plot(x,y,’:’) 4 2

13

>> D = [1:5; 6:10; 11:2:20] D = 1 2 3 4 5 6 7 8 9 10 11 13 15 17 19 So A and B are 2 × 3 matrices, C is 3 × 2 and D is 3 × 5. In this context, a row vector is a 1 × n matrix and a column vector a m × 1 matrix.

16.3

Special Matrices

Matlab provides a number of useful built–in matrices of any desired size. ones(m,n) gives an m × n matrix of 1’s, >> P = ones(2,3) P = 1 1 1 1 1 1 zeros(m,n) gives an m × n matrix of 0’s, >> Z = zeros(2,3), zeros(size(P’)) Z = 0 0 0 0 0 0 ans = 0 0 0 0 0 0 The second command illustrates how we can construct a matrix based on the size of an existing one. Try ones(size(D)). An n × n matrix that has the same number of rows and columns and is called a square matrix. A matrix is said to be symmetric if it is equal to its transpose (i.e. it is unchanged by transposition): >> S = [2 -1 0; -1 2 -1; 0 -1 2], S = 2 -1 0 -1 2 -1 0 -1 2 >> St = S’ St = 2 -1 0 -1 2 -1 0 -1 2 >> S-St ans = 0 0 0 0 0 0 0 0 0

16.1

Size of a matrix

We can get the size (dimensions) of a matrix with the command size >> size(A), size(x) ans = 2 3 ans = 3 1 >> size(ans) ans = 1 2 So A is 2 × 3 and x is 3 × 1 (a column vector). The last command size(ans) shows that the value returned by size is itself a 1 × 2 matrix (a row vector). We can save the results for use in subsequent calculations. >> [r c] = size(A’), S = size(A’) r = 3 c = 2 S = 3 2

16.2

Transpose of a matrix

Transposing a vector changes it from a row to a column vector and vice versa (see §8.4). The extension of this idea to matrices is that transposing interchanges rows with the corresponding columns: the 1st row becomes the 1st column, and so on. >> D, D’ D = 1 2 3 6 7 8 11 13 15 ans = 1 6 11 2 7 13 3 8 15 4 9 17 5 10 19 >> size(D), size(D’) ans = 3 5 ans = 5 3

16.4
4 9 17 5 10 19

The Identity Matrix

The n × n identity matrix is a matrix of zeros except for having ones along its leading diagonal (top left to bottom right). This is called eye(n) in Matlab (since mathematically it is usually denoted by I). >> I = eye(3), x = [8; -4; 1], I*x I = 1 0 0 0 1 0 0 0 1 x = 8 -4 1 ans = 8 -4 1

14

Notice that multiplying the 3 × 1 vector x by the 3 × 3 identity I has no eﬀect (it is like multiplying a number by 1).

5 1 B = -1 9 ans = 5 1 -1 9

7 -3 2 0 7 -3 2 0

9 -7 5 5 9 -7 5 5

16.5

Diagonal Matrices

A diagonal matrix is similar to the identity matrix except that its diagonal entries are not necessarily equal to 1. −3 0 0 0 4 0 D= 0 0 2 is a 3 × 3 diagonal matrix. To construct this in Matlab, we could either type it in directly >> D = [-3 0 0; 0 4 0; 0 0 2] D = -3 0 0 0 4 0 0 0 2 but this becomes impractical when the dimension is large (e.g. a 100 × 100 diagonal matrix). We then use the diag function.We ﬁrst deﬁne a vector d, say, containing the values of the diagonal entries (in order) then diag(d) gives the required matrix. >> d = [-3 4 2], D = diag(d) d = -3 4 2 D = -3 0 0 0 4 0 0 0 2 On the other hand, if A is any matrix, the command diag(A) extracts its diagonal entries: >> F = [0 1 8 7; 3 -2 -4 2; 4 2 1 1] F = 0 1 8 7 3 -2 -4 2 4 2 1 1 >> diag(F) ans = 0 -2 1

so we have added an extra column (x) to C in order to form G and have stacked A and B on top of each other to form H. >> J = [1:4; 5:8; 9:12; 20 0 5 4] J = 1 2 3 4 5 6 7 8 9 10 11 12 20 0 5 4 >> K = [ diag(1:4) J; J’ zeros(4,4)] K = 1 0 0 0 1 2 3 4 0 2 0 0 5 6 7 8 0 0 3 0 9 10 11 12 0 0 0 4 20 0 5 4 1 5 9 20 0 0 0 0 2 6 10 0 0 0 0 0 3 7 11 5 0 0 0 0 4 8 12 4 0 0 0 0 The command spy(K) will produce a graphical display of the location of the nonzero entries in K (it will also give a value for nz—the number of nonzero entries): >> spy(K), grid

16.7

Tabulating Functions

This has been addressed in earlier sections but we are now in a position to produce a more suitable table format. Example 16.1 Tabulate the functions y = 4 sin 3x and u = 3 sin 4x for x = 0, 0.1, 0.2, . . . , 0.5.

>> x = 0:0.1:0.5; >> y = 4*sin(3*x); u = 3*sin(4*x); Notice that the matrix does not have to be square. >> [ x’ y’ u’] ans = 0 0 0 16.6 Building Matrices 0.1000 1.1821 1.1683 It is often convenient to build large matrices from smaller 0.2000 2.2586 2.1521 ones: 0.3000 3.1333 2.7961 0.4000 3.7282 2.9987 >> C=[0 1; 3 -2; 4 2]; x=[8;-4;1]; 0.5000 3.9900 2.7279 >> G = [C x] G = 0 1 8 3 -2 -4 4 2 1 >> A, B, H = [A; B] A = Note the use of transpose (’) to get column vectors. (we could replace the last command by [x; y; u;]’) We could also have done this more directly: >> x = (0:0.1:0.5)’; >> [x 4*sin(3*x) 3*sin(4*x)]

15

16.8

Extracting Bits of Matrices

16.9

Dot product of matrices (.*)

We may extract sections from a matrix in much the same way as for a vector (page 5). Each element of a matrix is indexed according to which row and column it belongs to. The entry in the ith row and jth column is denoted mathematically by Ai,j and, in Matlab, by A(i,j). So >> J J = 1 2 3 4 5 6 7 8 9 10 11 12 20 0 5 4 >> J(1,1) ans = 1 >> J(2,3) ans = 7 >> J(4,3) ans = 5 >> J(4,5) ??? Index exceeds matrix dimensions. >> J(4,1) = J(1,1) + 6 J = 1 2 3 4 5 6 7 8 9 10 11 12 7 0 5 4 >> J(1,1) = J(1,1) - 3*J(1,2) J = -5 2 3 4 5 6 7 8 9 10 11 12 7 0 5 4 In the following examples we extract i) the 3rd column, ii) the 2nd and 3rd columns, iii) the 4th row, and iv) the “central” 2 × 2 matrix. See §8.1. >> J(:,3) ans = 3 7 11 5 >> J(:,2:3) ans = 2 3 6 7 10 11 0 5 >> J(4,:) ans = 7 0 >> J(2:3,2:3) ans = 6 7 10 11 % 3rd column

The dot product works as for vectors: corresponding elements are multiplied together—so the matrices involved must have the same size. >> A, B A = 5 7 9 1 -3 -7 B = -1 2 5 9 0 5 >> A.*B ans = -5 14 45 9 0 -35 >> A.*C ??? Error using ==> .* Matrix dimensions must agree. >> A.*C’ ans = 0 21 36 1 6 -14

16.10

Matrix–vector products

We turn next to the deﬁnition of the product of a matrix with a vector. This product is only deﬁned for column vectors that have the same number of entries as the matrix has columns. So, if A is an m × n matrix and x is a column vector of length n, then the matrix–vector Ax is legal. An m × n matrix times an n × 1 matrix ⇒ a m × 1 matrix. We visualise A as being made up of m row vectors stacked on top of each other, then the product corresponds to taking the scalar product (See §14.1) of each row of A with the vector x: The result is a column vector with m entries. 5 1 7 −3 9 −7

Ax

=

8    −4  1

= % columns 2 to 3 =

5 × 8 + 7 × (−4) + 9 × 1 1 × 8 + (−3) × (−4) + (−7) × 1 21 13

It is somewhat easier in Matlab: >> A = [5 7 9; 1 -3 -7] A = 5 7 9 1 -3 -7 >> x = [8; -4; 1] x = 8 -4 1 >> A*x ans =

% 4th row 5 4 % rows 2 to 3 & cols 2 to 3

Thus, : on its own refers to the entire column or row depending on whether it is the ﬁrst or the second index.

16

21 13 (m× n) times (n ×1) ⇒ (m × 1). >> x*A ??? Error using ==> * Inner matrix dimensions must agree. Unlike multiplication in arithmetic, A*x is not the same as x*A.

Exercise 16.1 It is often necessary to factorize a matrix, e.g., A = BC or A = S T XS where the factors are required to have speciﬁc properties. Use the ’lookfor keyword’ command to make a list of factorizations commands in Matlab.

16.12

Sparse Matrices

16.11

Matrix–Matrix Products

Matlab has powerful techniques for handling sparse matrices — these are generally large matrices (to make the extra work involved worthwhile) that have only a very small proportion of non–zero entries. Example 16.2 Create a sparse 5 × 4 matrix S having only 3 non–zero values: S1,2 = 10, S3,3 = 11 and S5,4 = 12. We ﬁrst create 3 vectors containing the i–index, the j– index and the corresponding values of each term and we then use the sparse command. >> i = [1, 3, 5]; j = [2,3,4]; >> v = [10 11 12]; >> S = sparse (i,j,v) S = (1,2) 10 (3,3) 11 (5,4) 12 >> T = full(S) T = 0 10 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 12 The matrix T is a “full” version of the sparse matrix S. Example 16.3 Develop Matlab code to create, for any given value of n, the sparse (tridiagonal) matrix

To form the product of an m × n matrix A and a n × p matrix B, written as AB, we visualise the ﬁrst matrix (A) as being composed of m row vectors of length n stacked on top of each other while the second (B) is visualised as being made up of p column vectors of length n:

      A = m rows     

. . .

   , 

B= 
p

···

 , 

columns

The entry in the ith row and jth column of the product is then the scalarproduct of the ith row of A with the jth column of B. The product is an m × p matrix: (m× n) times (n ×p) ⇒ (m × p). Check that you understand what is meant by working out the following examples by hand and comparing with the Matlab answers. >> A = [5 7 9; 1 -3 -7] A = 5 7 9 1 -3 -7 >> B = [0, 1; 3, -2; 4, 2] B = 0 1 3 -2 4 2 >> C = A*B C = 57 9 -37 -7 >> D = B*A D = 1 -3 -7 13 27 41 22 22 22 >> E = B’*A’ E = 57 -37 9 -7 We see that E = C’ suggesting that (A*B)’ = B’*A’ Why is B ∗ A a 3 × 3 matrix while A ∗ B is 2 × 2?

   B=   

1  −2

n 2 −3

n−1 3 .. .

n−2 .. . −n + 1

. n−1 −n

..

n

      1 

We deﬁne three column vectors, one for each “diagonal” of non–zeros and then assemble the matrix using spdiags (short for sparse diagonals). The vectors are named l, d and u. They must all have the same length and only the ﬁrst n − 1 terms of l are used while the last n − 1 terms of u are used. spdiags places these vectors in the diagonals labelled -1, 0 and 1 (0 defers to the leading diagonal, negatively numbered diagonals lie below the leading diagonal, etc.) >> n = 5; >> l = -(2:n+1)’; d = (1:n )’; u = ((n+1):-1:2)’; >> B = spdiags([l’ d’ u’],-1:1,n,n); >> full(B) ans =

17

1 -2 0 0 0

5 2 -3 0 0

0 4 3 -4 0

0 0 3 4 -5

0 0 0 2 5

and solve the system of equations Ax = b using the three alternative methods: i) x = A−1 b, (the inverse A−1 may be computed in Matlab using inv(A).) ii) x = A \ b, iii) xT = bt AT leading to xT = b’ / A which makes use of the “slash” or “right division” operator “/”. The required solution is then the transpose of the row vector xT. Exercise 17.2 Use the backslash operator to solve the complex system of equations for which A= 2 + 2i −1 0 −1 2 − 2i −1 0 −1 2 , b= 1+i 0 1−i

17

Systems of Linear Equations

Mathematical formulations of engineering problems often lead to sets of simultaneous linear equations. Such is the case, for instance, when using the ﬁnite element method (FEM). A general system of linear equations can be expressed in terms of a coeﬃcient matrix A, a right-hand-side (column) vector b and an unknown (column) vector x as Ax = b or, componentwise, as a1,1 x1 + a1,2 x2 + · · · a1,n xn a2,1 x1 + a2,2 x2 + · · · a2,n xn = b1 = b2 . . . = bn

an,1 x1 + an,2 x2 + · · · an,n xn

When A is non-singular and square (n × n), meaning that the number of independent equations is equal to the number of unknowns, the system has a unique solution given by x = A−1 b where A−1 is the inverse of A. Thus, the solution vector x can, in principle, be calculated by taking the inverse of the coeﬃcient matrix A and multiplying it on the right with the right-hand-side vector b. This approach based on the matrix inverse, though formally correct, is at best ineﬃcient for practical applications (where the number of equations may be extremely large) but may also give rise to large numerical errors unless appropriate techniques are used. These issues are discussed in most courses and texts on numerical methods. Various stable and eﬃcient solution techniques have been developed for solving linear equations and the most appropriate in any situation will depend on the properties of the coeﬃcient matrix A. For instance, on whether or not it is symmetric, or positive deﬁnite or if it has a particular structure (sparse or full). Matlab is equipped with many of these special techniques in its routine library and they are invoked automatically. The standard Matlab routine for solving systems of linear equations is invoked by calling the matrix leftdivision routine, >> x = A \ b where “\” is the matrix left-division operator known as “backslash” (see help backslash). Exercise 17.1 Enter the symmetric coeﬃcient matrix and right–hand–side vector b given by A= 2 1 0 −1 −2 −1 0 1 2 , b= 1 0 1

Exercise 17.3 Find information on the matrix inversion command ’inv’ using each of the methods listed in Section 2 for obtaining help. What kind of matrices are the ’inv’ command applicable to? Obviously problems may occur if the inverted matrix is nearly singular. Suggest a command that can be used to give an indication on whether the matrix is nearly singular or not. [Hint: see the topics referred to by ’help inv’.]

17.1

Overdetermined system of linear equations

An overdetermined system of linear equations is a one with more equations (m) than unknowns (n), i.e., the coeﬃcient matrix has more rows than columns (m > n). Overdetermined systems frequently appear in mathematical modelling when the parameters of a model are determined by ﬁtting to experimental data. Formally the system looks the same as for square systems but the coeﬃcient matrix is rectangular and so it is not possible to compute an inverse. In these cases a solution can be found by requiring that the magnitude of the residual vector r, deﬁned by r = Ax − b, be minimized. The simplest and most frequently used measure of the magnitude of r is require the Euclidean length (or norm—see Section 14.1) which corresponds to the sum of squares of the components of the residual. This approach leads to the least squares solution of the overdetermined system. Hence the least squares solution is deﬁned as the vector x that minimizes rT r. It may be shown that the required solution satisﬁes the so–called “normal equations” Cx = d, where C = AT A and d = AT b. This system is well–known that the solution of this system can be overwhelmed by numerical rounding error in practice unless great care is taken in its solution (a

18

large part of the diﬃculty is inherent in computing the matrix–matrix product AT A). As in the solution of square systems of linear equations, special techniques have been developed to address these issues and they have been incorporated into the Matlab routine library. This means that a direct solution to the problem of overdetermined equations is available in Matlab through its left division operator “\”. When the matrix A is not square, the operation x = A\b automatically gives the least squares solution to Ax = b. This is illustrated in the next example.

The appropriate Matlab commands give (the components of x are all multiplied by 1e-2, i.e., 10−2 , in order to change from cm to m) >> x = [.001 .011 .13 .3 .75]*1e-2; >> A = [x’ (x’).^2] A = 0.0000 0.0000 0.0001 0.0000 0.0013 0.0000 0.0030 0.0000 0.0075 0.0001 >> b = [5 50 500 1000 2000];

Example 17.1 A spring is a mechanical element which, and the least squares solution to this system is given by for the simplest model, is characterized by a linear force>> k = A\b’ deformation relationship k = F = kx, 1.0e+07 * 0.0386 F being the force loading the spring, k the spring con-1.5993 stant or stiﬀness and x the spring deformation. In reality the linear force–deformation relationship is only 0.39 × 106 and the quadratic spring an approximation, valid for small forces and deforma- Thus, k ≈ −16.0 tions. A more accurate relationship, valid for larger force-deformation relationship that optimally ﬁts exdeformations, is obtained if non–linear terms are taken perimental data in the least squares sense is into account. Suppose a spring model with a quadratic F ≈ 38.6 × 104 x − 16.0 × 106 x2 . relationship 2 F = k1 x + k2 x The data and solution may be plotted with the followis to be used and that the model parameters, k1 and ing commands k2 , are to be determined from experimental data. Five independent measurements of the force and the corre- >> plot(x,f,’o’), hold on % plot data points sponding spring deformations are measured and these >> X = (0:.01:1)*max(x); are presented in Table 1. >> plot(X,[X’ (X.^2)’]*k,’-’) % best fit curve

Force F [N] 5 50 500 1000 2000 Table 1: spring.

Deformation x [cm] 0.001 0.011 0.013 0.30 0.75

>> xlabel(’x[m]’), ylabel(’F[N]’) and the results are shown in Figure 5.

Measured force-deformation data for

Using the quadratic force-deformation relationship together with the experimental data yields an overdetermined system of linear equations and the components of the residual are given by r1 r2 r3 r4 r5 = x1 k1 + x2 k2 − F1 1 = x2 k1 + x2 k2 − F2 2 = x3 k1 + x2 k2 − F3 3 = x4 k1 + x2 k2 − F4 4 = x5 k1 + x2 k2 − F5 . 5 x1 x2 1 x2 2 x2 3 x2 4 x2 5

Figure 5: Data for Example 17.1 (circles) and best least squares ﬁt by a quadratic model (solid line).
Matlab has a routine polyfit for data ﬁtting by polynomials: see “help polyfit”. It is not applicable in this example because we require that the force – deformation law passes through the origin (so there is no constant term in the quadratic model that we used).

These lead to the matrix and vector deﬁnitions

 x2  A =  x3  x4
x5

  F2    and b =  F3   F4

F1

     19

F5 .

18

Characters, Strings and TextExample 19.1

The ability to process text in numerical processing is useful for the input and output of data to the screen or to disk-ﬁles. In order to manage text, a new datatype of “character” is introduced. A piece of text is then simply a string (vector) or array of characters. Example 18.1 The assignment, >> t1 = ’A’ assigns the value A to the 1-by-1 character array t1. The assignment, >> t2 = ’BCDE’ assigns the value BCDE to the 1-by-4 character array t2. Strings can be combined by using the operations for array manipulations. The assignment, >> t3 = [t1,t2] assigns a value ABCDE to the 1-by-5 character array t3. The assignment, >> t4 = [t3,’ are the first 5 ’characters in the alphabet.’] ’;...

Draw graphs of sin(nπx) on the interval −1 ≤ x ≤ 1 for n = 1, 2, . . . , 8. We could do this by giving 8 separate plot commands but it is much easier to use a loop. The simplest form would be >> x = -1:.05:1; >> for n = 1:8 subplot(4,2,n), plot(x,sin(n*pi*x)) end

All the commands between the lines starting “for” and “end” are repeated with n being given the value 1 the ﬁrst time through, 2 the second time, and so forth, until n = 8. The subplot constructs a 4 × 2 array of subwindows and, on the nth time through the loop, a picture is drawn in the nth subwindow.

assigns the value ’ABCDE are the first 5 ’ ’characters in the alphabet.’ to the 2-by-27 character array t4. It is essential that the number of characters in both rows of the array t4 is the same, otherwise an error will result. The three dots ... signify that the command is continued on the following line Sometimes it is necessary to convert a character to the corresponding number, or vice versa. These conversions are accomplished by the commands ’str2num’—which converts a string to the corresponding number, and two functions, ’int2str’ and ’num2str’, which convert, respectively, an integer and a real number to the corresponding character string. These commands are useful for producing titles and strings, such as ’The value of pi is 3.1416’. This can be generated by the command [’The value of pi is ’,num2str(pi)]. >> N = 5; h = 1/N; >> [’The value of N is ’,int2str(N),... ’, h = ’,num2str(h)] ans = The value of N is 5, h = 0.2

19

Loops

There are occasions that we want to repeat a segment of code a number of diﬀerent times (such occasions are less frequent than other programming languages because of the : notation).

20

The commands >> x = -1:.05:1; >> for n = 1:2:8 subplot(4,2,n), plot(x,sin(n*pi*x)) subplot(4,2,n+1), plot(x,cos(n*pi*x)) end draw sin nπx and cos nπx for n = 1, 3, 5, 7 alongside each other. We may use any legal variable name as the “loop counter” (n in the above examples) and it can be made to run through all of the values in a given vector (1:8 and 1:2:8 in the examples). We may also use for loops of the type >> for counter = [23 ....... end 11 19 5.4 6]

Example 19.3 Produce a list of the values of the sums S20 S21 . . . S100 = = = 1+ 1+ 1+
1 22 1 22

+ + +

1 32 1 32

+ ··· + + ··· + + ··· +

1 202 1 202

+ +

1 212

1 22

1 32

1 202

1 212

+ ··· +

1 1002

There are a total of 81 sums. The ﬁrst can be computed using sum(1./(1:20).^2) (The function sum with a vector argument sums its components. See §22.2].) A suitable piece of Matlab code might be >> S = zeros(100,1); >> S(20) = sum(1./(1:20).^2); >> for n = 21:100 >> S(n) = S(n-1) + 1/n^2; >> end >> clf; plot(S,’.’,[20 100],[1,1]*pi^2/6,’-’) >> axis([20 100 1.5 1.7]) >> [ (98:100)’ S(98:100)] ans = 98.0000 1.6364 99.0000 1.6365 100.0000 1.6366 where a column vector S was created to hold the answers. The ﬁrst sum was computed directly using the sum command then each succeeding sum was found by adding 1/n2 to its predecessor. The little table at the end shows the values of the last three sums—it appears that they are approaching a limit (the value of the limit is π 2 /6 = 1.64493 . . .). Exercise 19.1 Repeat Example 19.3 to include 181 sums (i.e., the ﬁnal sum should include the term 1/2002 .)

which repeats the code as far as the end with counter=23 the ﬁrst time, counter=11 the second time, and so forth. Example 19.2 The Fibonnaci sequence starts oﬀ with the numbers 0 and 1, then succeeding terms are the sum of its two immediate predecessors. Mathematically, f1 = 0, f2 = 1 and fn = fn−1 + fn−2 , n = 3, 4, 5, . . . .

Test the assertion that the ratio fn−1 /fn of two suc√ cessive values approaches the golden ratio ( 5 − 1)/2 = 0.6180 . . .. >> F(1) = 0; F(2) = 1; >> for i = 3:20 F(i) = F(i-1) + F(i-2); end >> plot(1:19, F(1:19)./F(2:20),’o’ ) >> hold on, xlabel(’n’) >> plot(1:19, F(1:19)./F(2:20),’-’ ) >> legend(’Ratio of terms f_{n-1}/f_n’) >> plot([0 20], (sqrt(5)-1)/2*[1,1],’--’) The last of these commands produces the dashed horizontal line.

20

Logicals

Matlab represents true and false by means of the integers 0 and 1. true = 1, false = 0 If at some point in a calculation a scalar x, say, has been assigned a value, we may make certain logical tests on it: x == 2 is x equal to 2? x ~= 2 is x not equal to 2? x > 2 is x greater than 2? x < 2 is x less than 2? x >= 2 is x greater than or equal to 2? x <= 2 is x less than or equal to 2? Pay particular attention to the fact that the test for equality involves two equal signs ==. >> x = pi x = 3.1416 >> x ~= 3, ans = 1 ans = 0

x ~= pi

21

When x is a vector or a matrix, these tests are performed elementwise: x = -2.0000 3.1416 -1.0000 0 >> x == 0 ans = 0 0 0 0 1 0 >> x > 1, x >=-1 ans = 0 1 1 0 0 0 ans = 0 1 1 1 1 1 >> y = x>=-1, x > y y = 0 1 1 1 1 1 ans = 0 1 1 0 0 0 5.0000 1.0000

so the matrix pos contains just those elements of x that are non–negative. >> x = 0:0.05:6; y = sin(pi*x); Y = (y>=0).*y; >> plot(x,y,’:’,x,Y,’-’ )

20.1

While Loops

We may combine logical tests, as in >> x x = -2.0000 -5.0000 >> x > 3 & ans = 0 0 >> x > 3 | ans = 0 0

3.1416 -3.0000 x < 4 1 0 0 0 x == -3 1 1 1 0

5.0000 -1.0000

There are some occasions when we want to repeat a section of Matlab code until some logical condition is satisﬁed, but we cannot tell in advance how many times we have to go around the loop. This we can do with a while...end construct. Example 20.1 What is the greatest value of n that can be used in the sum 12 + 22 + · · · + n2 and get a value of less than 100? >> S = 1; n = 1; >> while S+ (n+1)^2 < 100 n = n+1; S = S + n^2; end >> [n, S] ans = 6 91 The lines of code between while and end will only be executed if the condition S+ (n+1)^2 < 100 is true. Exercise 20.1 Replace 100 in the previous example by 10 and work through the lines of code by hand. You should get the answers n = 2 and S = 5. Exercise 20.2 Type the code from Example20.1 into a script–ﬁle named WhileSum.m (See §13.)

As one might expect, & represents and and (not so clearly) the vertical bar | means or; also ~ means not as in ~= (not equal), ~(x>0), etc. >> x > 3 | x == -3 | x <= -5 ans = 0 1 1 1 1 0 One of the uses of logical tests is to “mask out” certain elements of a matrix. >> x, L = x >= 0 x = -2.0000 3.1416 -5.0000 -3.0000 L = 0 1 1 0 1 1 >> pos = x.*L pos = 0 3.1416 0 0

5.0000 -1.0000

A more typical example is Example 20.2 Find the approximate value of the root of the equation x = cos x. (See Example 10.1.) We may do this by making a guess x1 = π/4, say, then computing the sequence of values

5.0000 0

xn = cos xn−1 ,

n = 2, 3, 4, . . .

and continuing until the diﬀerence between two successive values |xn − xn−1 | is small enough.

22

Method 1:

>> x = zeros(1,20); x(1) = pi/4; >> n = 1; d = 1; >> while d > 0.001 so that b is assigned a value only if a ≥ c. There is no n = n+1; x(n) = cos(x(n-1)); output so we deduce that a = π e < c = eπ . A more d = abs( x(n) - x(n-1) ); common situation is end n,x >> if a >= c n = b = sqrt(a^2 - c^2) 14 else x = b = 0 Columns 1 through 7 end 0.7854 0.7071 0.7602 0.7247 0.7487 0.7326 0.7435 b = Columns 8 through 14 0 0.7361 0.7411 0.7377 0.7400 0.7385 0.7395 0.7388 Columns 15 through 20 which ensures that b is always assigned a value and 0 0 0 0 0 0 conﬁrming that a < c. There are a number of deﬁciencies with this program. A more extended form is The vector x stores the results of each iteration but we don’t know in advance how many there may be. In any event, we are rarely interested in the intermediate values of x, only the last one. Another problem is that we may never satisfy the condition d ≤ 0.001, in which case the program will run forever—we should place a limit on the maximum number of iterations. Incorporating these improvements leads to >> if a >= c b = sqrt(a^2 - c^2) elseif a^c > c^a b = c^a/a^c else b = a^c/c^a end b = 0.2347 Exercise 20.3 Which of the above statements assigned a value to b? The general form of the if statement is if logical test 1 Commands to be executed if test 1 is true elseif logical test 2 Commands to be executed if test 2 is true but test 1 is false . . . end 0.0001

>> a = pi^exp(1); c = exp(pi); >> if a >= c b = sqrt(a^2 - c^2) end

Method 2:
>> xold = pi/4; n = 1; d = 1; >> while d > 0.001 & n < 20 n = n+1; xnew = cos(xold); d = abs( xnew - xold ); xold = xnew; end >> [n, xnew, d] ans = 14.0000 0.7388 0.0007 We continue around the loop so long as d > 0.001 and n < 20. For greater precision we could use the condition d > 0.0001, and this gives >> [n, xnew, d] ans = 19.0000 0.7391

21

Function m–ﬁles

from which we may judge that the root required is x = 0.739 to 3 decimal places. The general form of while statement is while a logical test Commands to be executed when the condition is true end

These are a combination of the ideas of script m–ﬁles (§7) and mathematical functions. Example 21.1 The area, A, of a triangle with sides of length a, b and c is given by A= s(s − a)(s − b)(s − c),

20.2

if...then...else...end

where s = (a + b + c)/2. Write a Matlab function that will accept the values a, b and c as inputs and return the value of A as output. The main steps to follow when deﬁning a Matlab function are:

This allows us to execute diﬀerent commands depending on the truth or falsity of some logical tests. To test whether or not π e is greater than, or equal to, eπ :

23

1. Decide on a name for the function, making sure that it does not conﬂict with a name that is already used by Matlab. In this example the name of the function is to be area, so its deﬁnition will be saved in a ﬁle called area.m 2. The ﬁrst line of the ﬁle must have the format: function [list of outputs] = function name(list of inputs) For our example, the output (A) is a function of the three variables (inputs) a, b and c so the ﬁrst line should read function [A] = area(a,b,c) 3. Document the function. That is, describe brieﬂy the purpose of the function and how it can be used. These lines should be preceded by % which signify that they are comment lines that will be ignored when the function is evaluated. 4. Finally include the code that deﬁnes the function. This should be interspersed with suﬃcient comments to enable another user to understand the processes involved. The complete ﬁle might look like: function [A] = area(a,b,c) % Compute the area of a triangle whose % sides have length a, b and c. % Inputs: % a,b,c: Lengths of sides % Output: % A: area of triangle % Usage: % Area = area(2,3,4); % Written by dfg, Oct 14, 1996. s = (a+b+c)/2; A = sqrt(s*(s-a)*(s-b)*(s-c)); %%%%%%%%% end of area %%%%%%%%%%% The command >> help area will produce the leading comments from the ﬁle: Compute the area of a triangle whose sides have length a, b and c. Inputs: a,b,c: Lengths of sides Output: A: area of triangle Usage: Area = area(2,3,4); Written by dfg, Oct 14, 1996. To evaluate the area of a triangle with side of length 10, 15, 20: >> Area = area(10,15,20) Area = 72.6184 where the result of the computation is assigned to the variable Area. The variable s used in the deﬁnition of the function above is a “local variable”: its value is local to the function and cannot be used outside:

>> s ??? Undefined function or variable s. If we were to be interested in the value of s as well as A, then the ﬁrst line of the ﬁle should be changed to function [A,s] = area(a,b,c) where there are two output variables. This function can be called in several diﬀerent ways: 1. No outputs assigned >> area(10,15,20) ans = 72.6184 gives only the area (ﬁrst of the output variables from the ﬁle) assigned to ans; the second output is ignored. 2. One output assigned >> Area = area(10,15,20) Area = 72.6184 again the second output is ignored. 3. Two outputs assigned >> [Area, hlen] = area(10,15,20) Area = 72.6184 hlen = 22.5000 Exercise 21.1 In any triangle the sum of the lengths of any two sides cannot exceed the length of the third side. The function area does not check to see if this condition is fulﬁlled (try area(1,2,4)). Modify the ﬁle so that it computes the area only if the sides satisfy this condition.

21.1

Examples of functions

We revisit the problem of computing the Fibonnaci sequence deﬁned by f1 = 0, f2 = 1 and fn = fn−1 + fn−2 , n = 3, 4, 5, . . . .

We want to construct a function that will return the nth number in the Fibonnaci sequence fn . • Input: Integer n • Output: fn We shall describe four possible functions and try to assess which provides the best solution.

Method 1: File Fib1.m
function f = Fib1(n) % Returns the nth number in the % Fibonacci sequence. F=zeros(1,n+1); F(2) = 1; for i = 3:n+1 F(i) = F(i-1) + F(i-2); end f = F(n);

24

This code resembles that given in Example 19.2. We have simply enclosed it in a function m–ﬁle and given it the appropriate header,

Method 2: File Fib2.m
The ﬁrst version was rather wasteful of memory—it saved all the entries in the sequence even though we only required the last one for output. The second version removes the need to use a vector. function f = Fib2(n) % Returns the nth number in the % Fibonacci sequence. if n==1 f = 0; elseif n==2 f = 1; else f1 = 0; f2 = 1; for i = 2:n-1 f = f1 + f2; f1=f2; f2 = f; end end

Method 1 2 3 4

Time 0.0118 0.0157 36.5937 0.0078

It is impractical to use Method 3 for any value of n much larger than 10 since the time taken by method 3 almost doubles whenever n is increased by just 1. When n = 150 Method 1 2 3 4 Time 0.0540 0.0891 — 0.0106

Clearly the 4th method is much the fastest.

22
22.1

Further Built–in Functions
Rounding Numbers

Method 3: File: Fib3.m
This version makes use of an idea called “recursive programming”— the function makes calls to itself. function f = Fib3(n) % Returns the nth number in the % Fibonacci sequence. if n==1 f = 0; elseif n==2 f = 1; else f = Fib3(n-1) + Fib3(n-2); end

There are a variety of ways of rounding and chopping real numbers to give integers. Use the deﬁnitions given in the table in §28 on page 32 in order to understand the output given below: >> x = pi*(-1:3), round(x) x = -3.1416 0 3.1416 6.2832 ans = -3 0 3 6 9 >> fix(x) ans = -3 0 3 6 9 >> floor(x) ans = -4 0 3 6 9 >> ceil(x) ans = -3 0 4 7 10 >> sign(x), rem(x,3) ans = -1 0 1 1 1 ans = -0.1416 0 0.1416 0.2832

9.4248

Method 4: File Fib4.m
The ﬁnal version uses matrix powers. The vector y has fn two components, y = . fn+1 function f = Fib4(n) % Returns the nth number in the % Fibonacci sequence. A = [0 1;1 1]; y = A^n*[1;0]; f=y(1);

0.4248

Do “help round” for help information.

22.2

The sum Function

Assessment: One may think that, on grounds of
style, the 3rd is best (it avoids the use of loops) followed by the second (it avoids the use of a vector). The situation is much diﬀerent when it cames to speed of execution. When n = 20 the time taken by each of the methods is (in seconds)

The “sum” applied to a vector adds up its components (as in sum(1:10)) while, for a matrix, it adds up the components in each column and returns a row vector. sum(sum(A)) then sums all the entries of A. >> A = [1:3; 4:6; 7:9] A = 1 2 3 4 5 6 7 8 9 >> s = sum(A), ss = sum(sum(A))

25

s = 12 ss = 45 >> x = pi/4*(1:3)’; >> A = [sin(x), sin(2*x), sin(3*x)]/sqrt(2) >> A = 0.5000 0.7071 0.5000 0.7071 0.0000 -0.7071 0.5000 -0.7071 0.5000 >> s1 = sum(A.^2), s2 = sum(sum(A.^2)) s1 = 1.0000 1.0000 1.0000 s2 = 3.0000 The sums of squares of the entries in each column of A are equal to 1 and the sum of squares of all the entries is equal to 3. >> A*A’ ans = 1.0000 0 0 >> A’*A ans = 1.0000 0 0 15 18

2.4000 >> [m, j] = max(x) m = 2.3000 j = 4 When we ask for two outputs, the ﬁrst gives us the maximum entry and the second the index of the maximum element. For a matrix, A, max(A) returns a row vector containing the maximum element from each column. Thus to ﬁnd the largest element in A we have to use max(max(A)).

22.4

Random Numbers

The function rand(m,n) produces an m × n matrix of random numbers, each of which is in the range 0 to 1. rand on its own produces a single random number. >> y = rand, Y = rand(2,3) y = 0.9191 Y = 0.6262 0.1575 0.2520 0.7446 0.7764 0.6121 Repeating these commands will lead to diﬀerent answers. Example: Write a function–ﬁle that will simulate n throws of a pair of dice. This requires random numbers that are integers in the range 1 to 6. Multiplying each random number by 6 will give a real number in the range 0 to 6; rounding these to whole numbers will not be correct since it will then be possible to get 0 as an answer. We need to use floor(1 + 6*rand)

0 1.0000 0.0000

0 0.0000 1.0000

0 1.0000 0.0000

0 0.0000 1.0000

It appears that the products AA and A A are both equal to the identity: >> A*A’ - eye(3) ans = 1.0e-15 * -0.2220 0 0 -0.2220 0 0.0555 >> A’*A - eye(3) ans = 1.0e-15 * -0.2220 0 0 -0.2220 0 0.0555

0 0.0555 -0.2220

Recall that floor takes the largest integer that is smaller than a given real number (see Table 2, page 32). File: dice.m function [d] = dice(n) % simulates "n" throws of a pair of dice % Input: n, the number of throws % Output: an n times 2 matrix, each row % referring to one throw. % % Useage: T = dice(3) d = floor(1 + 6*rand(n,2)); %% end of dice >> dice(3) ans = 6 1 2 3 4 1 >> sum(dice(100))/100 ans = 3.8500 3.4300 The last command gives the average value over 100 throws (it should have the value 3.5).

0 0.0555 -0.2220

This is conﬁrmed since the diﬀerences are at round– oﬀ error levels (less than 10−15 ). A matrix with this property is called an orthogonal matrix.

22.3

max & min

These functions act in a similar way to sum. If x is a vector, then max(x) returns the largest element in x >> x = [1.3 -2.4 0 2.3], max(x), max(abs(x)) x = 1.3000 -2.4000 0 2.3000 ans = 2.3000 ans =

26

22.5

find for vectors

The function “find” returns a list of the positions (indices) of the elements of a vector satisfying a given condition. For example,

>> x = -1:.05:1; >> y = sin(3*pi*x).*exp(-x.^2); plot(x,y,’:’) >> k = find(y > 0.2) k = Columns 1 through 12 9 10 11 12 13 22 23 24 25 26 27 36 Thus, n gives a list of the locations of the entries in A Columns 13 through 15 that are ≤ 0 and then A(n) gives us the values of the 37 38 39 elements selected. >> hold on, plot(x(k),y(k),’o’) >> km = find( x>0.5 & y<0) >> m = find( A’ == 0) km = m = 32 33 34 5 >> plot(x(km),y(km),’-’) 11 Since we are dealing with A’, the entries are numbered by rows.

1 2 8 9 >> A(n) ans = -2 0 -1 0

23

Plotting Surfaces

A surface is deﬁned mathematically by a function f (x, y)— corresponding to each value of (x, y) we compute the height of the function by z = f (x, y). In order to plot this we have to decide on the ranges of x and y—suppose 2 ≤ x ≤ 4 and 1 ≤ y ≤ 3. This gives us a square in the (x, y)–plane. Next, we need to choose a grid on this domain; Figure 6 shows the grid with intervals 0.5 in each direction. Finally, we have

22.6

find for matrices

The find–function operates in much the same way for matrices: >> A = [ -2 3 4 4; 0 5 -1 6; 6 8 0 1] A = -2 3 4 4 0 5 -1 6 6 8 0 1 >> k = find(A==0) k = 2 9 Thus, we ﬁnd that A has elements equal to 0 in positions 2 and 9. To interpret this result we have to recognize that “find” ﬁrst reshapes A into a column vector—this is equivalent to numbering the elements of A by columns as in 1 4 7 10 2 5 8 11 3 6 9 12 >> n n = = find(A <= 0)

Figure 6: An example of a 2D grid
to evaluate the function at each point of the grid and “plot” it. Suppose we choose a grid with intervals 0.5 in each direction for illustration. The x– and y–coordinates of the grid lines are x = 2:0.5:4; y = 1:0.5:3;

in Matlab notation. We construct the grid with meshgrid:

27

>> [X,Y] = meshgrid(2:.5:4, 1:.5:3); >> X X = 2.0000 2.5000 3.0000 3.5000 2.0000 2.5000 3.0000 3.5000 2.0000 2.5000 3.0000 3.5000 2.0000 2.5000 3.0000 3.5000 2.0000 2.5000 3.0000 3.5000 >> Y Y = 1.0000 1.0000 1.0000 1.0000 1.5000 1.5000 1.5000 1.5000 2.0000 2.0000 2.0000 2.0000 2.5000 2.5000 2.5000 2.5000 3.0000 3.0000 3.0000 3.0000

4.0000 4.0000 4.0000 4.0000 4.0000

>> >> >> >> >> >>

[X,Y] = meshgrid(-2:.1:2,-2:.2:2); f = -X.*Y.*exp(-2*(X.^2+Y.^2)); figure (1) mesh(X,Y,f), xlabel(’x’), ylabel(’y’), grid figure (2), contour(X,Y,f) xlabel(’x’), ylabel(’y’), grid, hold on

1.0000 1.5000 2.0000 2.5000 3.0000

If we think of the ith point along from the left and the jth point up from the bottom of the grid) as corresponding to the (i, j)th entry in a matrix, then (X(i,j), Y(i,j)) are the coordinates of the point. We then need to evaluate the function f using X and Y in place of x and y, respectively. Example 23.1 Plot the surface deﬁned by the function f (x, y) = (x − 3)2 − (y − 2)2 for 2 ≤ x ≤ 4 and 1 ≤ y ≤ 3. >> >> >> >> [X,Y] = meshgrid(2:.2:4, 1:.2:3); Z = (X-3).^2-(Y-2).^2; mesh(X,Y,Z) title(’Saddle’), xlabel(’x’),ylabel(’y’)

Figure 8: “mesh” and “contour” plots.
To locate the maxima of the “f” values on the grid: >> fmax = max(max(f)) fmax = 0.0886 >> kmax = find(f==fmax) kmax = 323 539 >> Pos = [X(kmax), Y(kmax)] Pos = -0.5000 0.6000 0.5000 -0.6000 >> plot(X(kmax),Y(kmax),’*’) >> text(X(kmax),Y(kmax),’ Maximum’)

Figure 7: Plot of Saddle function.
Exercise 23.1 Repeat the previous example replacing mesh by surf and then by surfl. Consult the help pages to ﬁnd out more about these functions. Example 23.2 Plot the surface deﬁned by the function f = −xye−2(x
2

+y 2 )

24

Timing

on the domain −2 ≤ x ≤ 2, −2 ≤ y ≤ 2. Find the values and locations of the maxima and minima of the function.

Matlab allows the timing of sections of code by providing the functions tic and toc. tic switches on a stopwatch while toc stops it and returns the CPU time

28

http://www.maths.dundee.ac.uk or http://www.maths.dundee.ac.uk/software/ and select Matlab from the array of choices.

26

Direct input of data from keyboard becomes impractical when • the amount of data is large and

Figure 9: contour plot showing maxima.
(Central Processor Unit) in seconds. The timings will vary depending on the model of computer being used and its current load. >> tic,for j=1:1000,x = pi*R(3);end,toc elapsed_time = 0.5110 >> tic,for j=1:1000,x=pi*R(3);end,toc elapsed_time = 0.5017 >> tic,for j=1:1000,x=R(3)/pi;end,toc elapsed_time = 0.5203 >> tic,for j=1:1000,x=pi+R(3);end,toc elapsed_time = 0.5221 >> tic,for j=1:1000,x=pi-R(3);end,toc elapsed_time = 0.5154 >> tic,for j=1:1000,x=pi^R(3);end,toc elapsed_time = 0.6236

• the same data is analysed repeatedly. In these situations input and output is preferably accomplished via data ﬁles. We have already described in Section 9 the use of the commands save and load that, respectively, write and read the values of variables to disk ﬁles. When data are written to or read from a ﬁle it is crucially important that a correct data format is used. The data format is the key to interpreting the contents of a ﬁle and must be known in order to correctly interpret the data in an input ﬁle. There a two types of data ﬁles: formatted and unformatted. Formatted data ﬁles uses format strings to deﬁne exactly how and in what positions of a record the data is stored. Unformatted storage, on the other hand, only speciﬁes the number format. The ﬁles used in this section are available from the web site http://www.maths.dundee.ac.uk/software/#matlab Those that are unformatted are in a satisfactory form for the Windows version on Matlab (version 6.1) but not on Version 5.3 under Unix. Exercise 26.1 Suppose the numeric data is stored in a ﬁle ’table.dat’ in the form of a table, as shown below.

25

On–line Documentation

In addition to the on–line help facility, there is a hypertext browsing system giving details of (most) commands and some examples. This is accessed by >> doc

which brings up the Netscape document previewer (and 100 2256 allows for “surﬁng the internet superhighway”—the World 200 4564 Wide Web (WWW). It is connected to a worldwide sys300 3653 tem which, given the appropriate addresses, will pro400 6798 vide information on almost any topic). 500 6432 Words that are underlined in the browser may be clicked on with LB and lead to either a further subindex or a The three commands, help page. Scroll down the page shown and click on general which >> fid = fopen(’table.dat’,’r’); will take you to “General Purpose Commands”; click on >> a = fscanf(fid,’%3d%4d’); clear. This will describe how you can clear a variable’s >> fclose(fid); value from memory. You may then either click the “Table of Contents” which respectively takes you back to the start, “Index” or the Back button at the lower left corner of the window which will take you back to the previous screen. To access other “home pages”, click on Open at the bottom of the window and, in the “box” that will open up, type 1. open a ﬁle for reading (this is designated by the string ’r’). The variable fid is assigned a unique integer which identiﬁes the ﬁle used (a ﬁle identiﬁer). We use this number in all subsequent references to the ﬁle.

29

2. read pairs of numbers from the ﬁle with ﬁle identiﬁer fid, one with 3 digits and one with 4 digits, and 3. close the ﬁle with ﬁle identiﬁer fid. This produces a column vector a with elements, 100 2256 200 4564 ...500 6432. This vector can be converted to 5 × 2 matrix by the command A = reshape(2,2,5)’;.

26.1

Formatted Files

Some computer codes and measurement instruments produce results in formatted data ﬁles. In order to read these results into Matlab for further analysis the data format of the ﬁles must be known. Formatted ﬁles in ASCII format are written to and read from with the commands fprintf and fscanf. fprintf(fid, ’format’, variables) writes variables an a format speciﬁed in string ’format’ to the ﬁle with identiﬁer ﬁd

Figure 10: Graph of “sound data” from Example 26.1

26.2

Unformatted Files

Unformatted or binary data ﬁles are used when smalla = fscanf(fid, ’format’,size) assigns to varisized ﬁles are required. In order to interpret an unforable a data read from ﬁle with identiﬁer fid unmatted data ﬁle the data precision must be speciﬁed. der format ’format’. The precision is speciﬁed as a string, e.g., ’float32’, Exercise 26.2 Study the available information and help controlling the number of bits read for each value and on fscanf and fprintf commands. What is the mean- the interpretation of those bits as character, integer or ﬂoating point values. Precision ’float32’, for instance, ing of the format string, ’%3d\n’? speciﬁes each value in the data to be stored as a ﬂoating Example 26.1 Suppose a sound pressure measurement point number in 32 memory bits. system produces a record with 512 time – pressure readings stored on a ﬁle ’sound.dat’. Each reading is listed on a separate line according to a data format speciﬁed by the string, ’%8.6f %8.6f’. A set of commands reading time – sound pressure data from ’sound.dat’ is, Step 1: Assign a namestring to a ﬁle identiﬁer. >> fid1 = fopen(’sound.dat’,’r’); The string ’r’ indicates that data is to be read (not written) from the ﬁle. Example 26.2 Suppose a system for vibration measurement stores measured acceleration values as ﬂoating point numbers using 32 memory bits. The data is stored on ﬁle ’vib.dat. The following commands illustrate how the data may be read into Matlab for analysis. Step 1: Assign a ﬁle identiﬁer, fid, to the string specifying the ﬁle name. >> fid = fopen(’vib.dat’,’rb’); The string ’rb’ speciﬁes that binary numbers are to be read from the ﬁle.

Step 2: Read the data to a vector named ’data’ and close Step 2 Read all data stored on ﬁle ’vib.dat’ into a vecthe ﬁle, tor vib. >> data = fscanf(fid1, ’%f %f’); >> fclose(fid1); Step 3: Partition the data in separate time and sound pressure vectors, >> t = data(1:2:length(data)); >> press = data(2:2:length(data)); The pressure signal can be plotted in a lin-lin diagram, >> plot(t, press); The result is shown in Figure 10. >> vib = fread(fid, ’float32’); >> fclose(fid); >> size(vib) ans = 131072 The size(vib) command determines the size, i.e., the number of rows and columns of the vibration data vector. In order to plot the vibration signal with a correct time scale, the sampling frequency (the number of instrument readings taken per second) used by the measurement system must be known. In this case it is known to be 24000 Hz so that there is a time interval of 1/24000 seconds between two samples.

30

Step 3: Create a column vector containing the correct time scale. >> dt = 1/24000; >> t = dt*(1:length(vib))’; Step 4: Plot the vibration signal in a lin-lin diagram >> >> >> >> plot(t,vib); title(’Vibration signal’); xlabel(’Time,[s]’); ylabel(’Acceleration, [m/s^2]’);

27

Graphic User Interfaces

The eﬃciency of programs that are used often and by several diﬀerent people can be improved by simplifying the input and output data management. The use of Graphic User Interfaces (GUI), which provides facilities such as menus, pushbuttons, sliders etc, allow programs to be used without any knowledge of Matlab. They also provides means for eﬃcient data management. A graphic user interface is a Matlab script ﬁle customized for repeated analysis of a speciﬁc type of problem. There are two ways to design a graphic user interface. The simplest method is to use a tool especially designed for the purpose. Matlab provides such a tool and it is invoked by typing ’guide’ at the Matlab prompt. Maximum ﬂexibility and control over the programming is, however, obtained by using the basic user interface commands. The following text demonstrates the use of some basic commands. Example 27.1 Suppose a sound pressure spectrum is to be plotted in a graph. There are four alternative plot formats; lin-lin, lin-log, log-lin and log-log. The graphic user interface below reads the pressure data stored on a binary ﬁle selected by the user, plots it in a lin-lin format as a function of frequency and lets the user switch between the four plot formats. We use two m–ﬁles. The ﬁrst (specplot.m) is the main driver ﬁle which builds the graphics window. It calls the second ﬁle (firstplot.m) which allows the user to select among the possible *.bin ﬁles in the current directory.

% Create pushbuttons for switching between four % different plot formats. Set up the axis stings. X = ’Frequency, [Hz]’; Y = ’Pressure amplitude, [Pa]’; linlinBtn = uicontrol(’style’,’pushbutton’,... ’string’,’lin-lin’,... ’position’,[200,395,40,20],’callback’,... ’plot(fdat,pdat);xlabel(X);ylabel(Y);’); linlogBtn = uicontrol(’style’,’pushbutton’,... ’string’,’lin-log’,... ’position’,[240,395,40,20],... ’callback’,... ’semilogy(fdat,pdat);xlabel(X);ylabel(Y);’); loglinBtn = uicontrol(’style’,’pushbutton’,... ’string’,’log-lin’,... ’position’,[280,395,40,20],... ’callback’,... ’semilogx(fdat,pdat);xlabel(X);ylabel(Y);’); loglogBtn = uicontrol(’style’,’pushbutton’,... ’string’,’log-log’,... ’position’,[320,395,40,20],... ’callback’,... ’loglog(fdat,pdat);xlabel(X); ylabel(Y);’); % Create exit pushbutton with red text. exitBtn = uicontrol(’Style’,’pushbutton’,... ’string’,’EXIT’,’position’,[510,395,40,20],... ’foregroundcolor’,[1 0 0],’callback’,’close;’); % % % % % % % Script file: firstplot.m Brings template for file selection. Reads selected filename and path and plots spectrum in a lin-lin diagram. Output data are frequency and pressure amplitude vectors: ’fdat’ and ’pdat’. Author: U Carlsson, 2001-08-22

function [fdat,pdat] = firstplot % Call Matlab function ’uigetfile’ that % brings file selction template.

[filename,pathname] = uigetfile(’*.bin’,... ’Select binary data-file:’); % Change directory % File: specplot.m cd(pathname); % % Open file for reading binary floating % GUI for plotting a user selected frequency spectrum % point numbers. % in four alternative plot formats, lin-lin, fid = fopen(filename,’rb’); % lin-log, log-lin and log-log. data = fread(fid,’float32’); % % Close file % Author: U Carlsson, 2001-08-22 fclose(fid); % Partition data vector in frequency and % Create figure window for graphs % pressure vectors figWindow = figure(’Name’,’Plot alternatives’); pdat = data(2:2:length(data)); % Create file input selection button fdat = data(1:2:length(data)); fileinpBtn = uicontrol(’Style’,’pushbutton’,... % Plot pressure signal in a lin-lin diagram ’string’,’File’,’position’,[5,395,40,20],... plot(fdat,pdat); ’callback’,’[fdat,pdat] = firstplot;’); % Define suitable axis labels % Press ’File’ calls function ’firstplot’ xlabel(’Frequency, [Hz]’); ylabel(’Pressure amplitude, [Pa]’);

31

Executing this GUI from the command line (>> specplot) brings the following screen.

abs sqrt sign conj imag real angle cos sin tan exp log log10 cosh sinh tanh acos acosh asin asinh atan atan2

Figure 11: Graph of “vibration data” from Example 27.1
Example 27.1 illustrates how the ’callback’ property allows the programmer to deﬁne what actions should result when buttons are pushed etc. These actions may consist of single Matlab commands or complicated sequences of operations deﬁned in various subroutines.

atanh round floor fix Exercise 27.1 Five diﬀerent sound recordings are stored ceil on binary data ﬁles, sound1.bin, sound2.bin, . . . , sound5.bin. rem
The storage precision is ’float32’ and the sounds are recorded with sample frequency 12000 Hz. Write a graphic user interface that, opens an interface window and • lets the user select one of the ﬁve sounds, • plots the selected sound pressure signal as a function of time in a lin-lin diagram, • lets the user listen to the sound by pushing a ’SOUND’ button and ﬁnally • closes the session by pressing a ’CLOSE’ button.

Absolute value Square root function Signum function Conjugate of a complex number Imaginary part of a complex number Real part of a complex number Phase angle of a complex number Cosine function Sine function Tangent function Exponential function Natural logarithm Logarithm base 10 Hyperbolic cosine function Hyperbolic sine function Hyperbolic tangent function Inverse cosine Inverse hyperbolic cosine Inverse sine Inverse hyperbolic sine Inverse tan Two–argument form of inverse tan Inverse hyperbolic tan Round to nearest integer Round towards minus inﬁnity Round towards zero Round towards plus inﬁnity Remainder after division

Table 2: Elementary Functions

28

Command Summary

The command >> help will give a list of categories for which help is available (e.g. matlab/general covers the topics listed in Table 3. Further information regarding the commands listed in this section may then be obtained by using: >> help topic try, for example, >> help help

32

Managing commands and functions. help On-line documentation. doc Load hypertext documentation. what Directory listing of M-, MATand MEX-ﬁles. type List M-ﬁle. lookfor Keyword search through the HELP entries. which Locate functions and ﬁles. demo Run demos. Managing variables and the workspace. who List current variables. whos List current variables, long form. load Retrieve variables from disk. save Save workspace variables to disk. clear Clear variables and functions from memory. size Size of matrix. length Length of vector. disp Display matrix or text. Working with ﬁles and the operating system. cd Change current working directory. dir Directory listing. delete Delete ﬁle. ! Execute operating system command. unix Execute operating system command & return result. diary Save text of MATLAB session. Controlling the command window. cedit Set command line edit/recall facility parameters. clc Clear command window. home Send cursor home. format Set output format. echo Echo commands inside script ﬁles. more Control paged output in command window. Quitting from MATLAB. quit Terminate MATLAB. Table 3: General purpose commands.

Matrix analysis. Matrix condition number. Matrix or vector norm. LINPACK reciprocal condition estimator. rank Number of linearly independent rows or columns. det Determinant. trace Sum of diagonal elements. null Null space. orth Orthogonalization. rref Reduced row echelon form. Linear equations. \ and / Linear equation solution; use “help slash”. chol Cholesky factorization. lu Factors from Gaussian elimination. inv Matrix inverse. qr Orthogonal- triangular decomposition. qrdelete Delete a column from the QR factorization. qrinsert Insert a column in the QR factorization. nnls Non–negative least- squares. pinv Pseudoinverse. lscov Least squares in the presence of known covariance. Eigenvalues and singular values. eig Eigenvalues and eigenvectors. poly Characteristic polynomial. polyeig Polynomial eigenvalue problem. hess Hessenberg form. qz Generalized eigenvalues. rsf2csf Real block diagonal form to complex diagonal form. cdf2rdf Complex diagonal form to real block diagonal form. schur Schur decomposition. balance Diagonal scaling to improve eigenvalue accuracy. svd Singular value decomposition. Matrix functions. expm Matrix exponential. expm1 M- ﬁle implementation of expm. expm2 Matrix exponential via Taylor series. expm3 Matrix exponential via eigenvalues and eigenvectors. logm Matrix logarithm. sqrtm Matrix square root. funm Evaluate general matrix function. cond norm rcond Table 4: Matrix functions—numerical linear algebra.

33

Graphics & plotting. Create Figure (graph window). Clear current ﬁgure. Close ﬁgure. Create axes in tiled positions. Control axis scaling and appearance. hold Hold current graph. figure Create ﬁgure window. text Create text. print Save graph to ﬁle. plot Linear plot. loglog Log-log scale plot. semilogx Semi-log scale plot. semilogy Semi-log scale plot. Specialized X-Y graphs. polar Polar coordinate plot. bar Bar graph. stem Discrete sequence or ”stem” plot. stairs Stairstep plot. errorbar Error bar plot. hist Histogram plot. rose Angle histogram plot. compass Compass plot. feather Feather plot. fplot Plot function. comet Comet-like trajectory. Graph annotation. title Graph title. xlabel X-axis label. ylabel Y-axis label. text Text annotation. gtext Mouse placement of text. grid Grid lines. contour Contour plot. mesh 3-D mesh surface. surf 3-D shaded surface. waterfall Waterfall plot. view 3-D graph viewpoint speciﬁcation. zlabel Z-axis label for 3-D plots. gtext Mouse placement of text. grid Grid lines. figure clf close subplot axis Table 5: Graphics & plot commands.

34

Index
<, 21, 23 <=, 21, 23 ==, 21, 23 >, 21, 23 >=, 21, 23 %, 10, 24 ’, 5 .’, 6 .*, 11 ..., 8 ./, 12 .^, 12 :, 5, 16 ;, 4 abs, 32 accelerators keyboard, 9 and, 22 angle, 32 ans, 3 array, 13 axes, 9, 13 axis, 9 auto, 9 normal, 9 square, 9 browser, 29 ceil, 32 clf, 8 close, 8 colon notation, 5, 16 column vectors, 5 comment (%), 10, 24 complex conjugate transpose, 6 numbers, 6 complex numbers, 3 components of a vector, 4 conj, 32 contour, 28 copying output, 10 cos, 32 CPU, 28 cursor keys, 9 demo, 3 diag, 15 diary, 6 dice, 26 divide dot, 12 documentation, 29 dot divide ./, 12 power .^, 12 product .*, 11, 16 hard copy, 8 help, 2, 24 hold, 7, 13 home page, 29 if statement, 23 imag, 32 keyboard accelerators, 9 labels for plots, 7 legend, 7 length of a vector, 4, 5, 11 line styles, 7 linspace, 6 logical conditions, 21 loops, 20 while, 22 m–ﬁles, 10, 23 matrix, 13 building, 15 diagonal, 15 identity, 14 indexing, 16 n=5;tridiagonal, 17 orthogonal, 26 size, 14 sparse, 17 special, 14 spy, 15 echo, 10 elementary functions, 4 eye, 14 ezplot, 7 false, 21 Fibonnaci, 21, 24 ﬁgure, 8 ﬁle function, 23 script, 10 find, 27 ﬁx, 32 ﬂoor, 32 floor, 26 for loop, 20 format, 3 long, 12 function m–ﬁles, 23 functions elementary, 4 trigonometric, 4 get, 9 graphs, see plotting grid, 7, 13, 28 GUI, 31

35

square, 14 symmetric, 14 zeros, 14 matrix products, 17 matrix–vector products, 16 max, 26, 28 mesh, 28 meshgrid, 27 min, 26, 28 more, 3 multi–plots, 7 Netscape, 29 norm of a vector, 11 not, 21–23 numbers, 3 complex, 3 format, 3 random, 26 rounding, 25 ones, 14 or, 22 plot, 20 plotting, 6, 13, 27 labels, 7 line styles, 7 printing, 8 surfaces, 27 title, 7 power dot, 12 printing plots, 8 priorities in arithmetic, 3 product dot, 11, 16 scalar, 16, 17 quit, 2 rand, 26 random numbers, 26 real, 32 rem, 32 round, 32 rounding error, 4 rounding numbers, 25 save, 6 scalar product, 11, 16, 17 script ﬁles, 10 semi–colon, 4, 13 set, 9 shapes, 7 sign, 32 sin, 32 size, 14 sort, 5 sparse, 17 spdiags, 17

spy, 15 sqrt, 32 strings, 7 subplot, 8, 20 subscripts, 9 sum, 21, 25 superscripts, 9 surﬁng the internet highway, 29 timing, 28 title for plots, 7 toc, 28 transposing, 5 tridiagonal, 17 trigonometric functions, 4 true, 21 type (list contents of m-ﬁle), 10 variable names, 3 vector components, 4 vectors column, 5 row, 4 what, 10 while loops, 22 whos, 6 WWW, 29 xlabel, 7, 28 xterm, 2 ylabel, 7 zeros, 14 zoom, 8

36