# The Historic MATLAB Users’ Guide

In the 1970s and early 1980s, while I was working on the LINPACK and EISPACK projects that I discussed in two previous posts, I was a Professor of Mathematics and then of Computer Science at the University of New Mexico in Albuquerque. I was teaching courses in Linear Algebra and Numerical Analysis. I wanted my students to have easy access to LINPACK and EISPACK without writing Fortran programs. By "easy access" I meant not going through the remote batch processing and the repeated edit-compile-link-load-execute process that was ordinarily required on the campus central mainframe computer.

So, I studied Niklaus Wirth's book Algorithms + Data Structures = Programs and learned how to parse programming languages. Wirth calls his example language PL/0. (A zinger aimed at PL/I, the monolithic, committee-designed language being promoted by IBM and the U.S. Defense Department at the time.) PL/0 was a pedagogical subset of Wirth's Pascal, which, in turn, was his response to Algol. Quoting Wirth, "In the realm of data types, however, PL/0 adheres to the demand of simplicity without compromise: integers are its only data type."

Following Wirth's approach, I wrote the first MATLAB -- an acronym for Matrix Laboratory -- in Fortran, as a dialect of PL/0 with matrix as the only data type. The project was a kind of hobby, a new aspect of programming for me to learn and something for my students to use. There was never any formal outside support and certainly no business plan. MathWorks was a few years in the future.

This first MATLAB was not a programming language; it was just a simple interactive matrix calculator. There were no m-files, no toolboxes, no graphics. And no ODEs or FFTs. This snapshot of the start-up screen shows all the reserved words and functions. There are only 71 of them. If you wanted to add another function, you would get the source code from me, write a Fortran subroutine, add your new name to the parse table, and recompile MATLAB.

Here is the first Users' Guide, in its entirety, from 1981.

### Contents

#### Users' Guide

                         MATLAB Users' Guide
May, 1981
                             Cleve Moler
Department of Computer Science
University of New Mexico
       ABSTRACT.  MATLAB is an  interactive  computer  program
that   serves   as   a   convenient   "laboratory"  for
computations  involving  matrices.   It  provides  easy
access  to matrix software developed by the LINPACK and
EISPACK projects.  The program is  written  in  Fortran
and  is  designed  to  be  readily  installed under any
operating system which permits interactive execution of
Fortran programs.
       MATLAB is an interactive computer program that serves  as  a
convenient  "laboratory" for computations involving matrices.  It
and EISPACK projects [1-3].  The capabilities range from standard
tasks such as solving simultaneous linear equations and inverting
matrices, through symmetric and nonsymmetric eigenvalue problems,
to fairly sophisticated matrix tools such as the  singular  value
decomposition.
       It is expected that one of MATLAB's primary uses will be  in
the  classroom.   It  should be useful in introductory courses in
applied linear algebra, as  well  as  more  advanced  courses  in
numerical analysis, matrix theory, statistics and applications of
matrices to other disciplines.  In nonacademic  settings,  MATLAB
can  serve as a "desk calculator" for the quick solution of small
problems involving matrices.
       The program is written in Fortran  and  is  designed  to  be
readily  installed  under  any  operating  system  which  permits
interactive  execution  of  Fortran  programs.    The   resources
required  are  fairly  modest.  There are less than 7000 lines of
Fortran  source  code,  including   the   LINPACK   and   EISPACK
subroutines  used.   With  proper use of overlays, it is possible
run the system on a minicomputer with only 32K bytes of memory.
       The size of the matrices  that  can  be  handled  in  MATLAB
depends  upon  the  amount  of storage that is set aside when the
system is compiled on a particular machine.  We have  found  that
an  allocation of 5000 words for matrix elements is usually quite
satisfactory.  This provides room for several 20 by 20  matrices,
for  example.   One  implementation  on  a  virtual memory system
provides 100,000 elements.  Since most  of  the  algorithms  used
access  memory  in  a  sequential  fashion,  the  large amount of
allocated storage causes no difficulties.
       In some ways, MATLAB  resembles  SPEAKEASY  [4]  and,  to  a
lesser  extent, APL.  All are interactive terminal languages that
ordinarily accept single-line  commands  or  statements,  process
them  immediately,  and  print  the  results.  All have arrays or
matrices as principal data types.  But for MATLAB, the matrix  is
the  only  data  type  (although  scalars,  vectors  and text are
special cases), the underlying system is  portable  and  requires
fewer resources, and the supporting subroutines are more powerful
and, in some cases, have better numerical properties.
       Together, LINPACK and EISPACK represent the state of the art
in software for matrix computation.  EISPACK is a package of over
70 Fortran subroutines for various matrix eigenvalue computations
that are based for the most part on Algol procedures published by
Wilkinson, Reinsch  and  their  colleagues  [5].   LINPACK  is  a
package  of  40  Fortran subroutines (in each of four data types)
for solving  and  analyzing  simultaneous  linear  equations  and
related matrix problems.  Since MATLAB is not primarily concerned
with either execution time  efficiency  or  storage  savings,  it
ignores  most  of  the special matrix properties that LINPACK and
EISPACK subroutines  use  to  advantage.   Consequently,  only  8
subroutines   from  LINPACK  and  5  from  EISPACK  are  actually
involved.
       In  more  advanced  applications,  MATLAB  can  be  used  in
conjunction  with other programs in several ways.  It is possible
to define new MATLAB functions and add them to the system.   With
most  operating  systems,  it  is  possible to use the local file
system to  pass  matrices  between  MATLAB  and  other  programs.
MATLAB  command  and statement input can be obtained from a local
file  instead  of  from  the  terminal.   The  most   power   and
flexibility  is obtained by using MATLAB as a subroutine which is
called by other programs.
       This document first gives an overview  of  MATLAB  from  the
user's  point  of  view. Several extended examples involving data
fitting, partial differential equations,  eigenvalue  sensitivity
and other topics are included.  A formal definition of the MATLAB
language and an brief description of the parser  and  interpreter
are   given.   The  system  was  designed  and  programmed  using
techniques described by Wirth [6], implemented  in  nonrecursive,
portable  Fortran.   There  is  a brief discussion of some of the
matrix algorithms and of their numerical properties.   The  final
section  describes  how  MATLAB  can be used with other programs.
The appendix includes the HELP documentation available on-line.

#### 1. Elementary operations

       MATLAB works with essentially only one  kind  of  object,  a
rectangular matrix with complex elements.  If the imaginary parts
of the elements are all zero, they  are  not  printed,  but  they
still  occupy  storage.   In  some situations, special meaning is
attached to 1 by 1 matrices, that is scalars, and to 1 by n and m
by 1 matrices, that is row and column vectors.
       Matrices can be introduced into  MATLAB  in  four  different
ways:
--  Explicit list of elements,
--  Use of FOR and WHILE statements,
--  Read from an external file,
--  Execute an external Fortran program.
       The explicit list is surrounded by angle brackets,  '<'  and
'>', and uses the semicolon ';' to indicate the ends of the rows.
For example, the input line
     A = <1 2 3; 4 5 6; 7 8 9>
  will result in the output
     A     =
         1.    2.   3.
4.    5.   6.
7.    8.   9.
  The matrix A  will  be  saved  for  later  use.   The  individual
elements  are separated by commas or blanks and can be any MATLAB
expressions, for example
     x = < -1.3, 4/5, 4*atan(1) >
  results in
     X     =
       -1.3000   0.8000   3.1416
  The elementary functions available include sqrt, log,  exp,  sin,
cos, atan, abs, round, real, imag, and conjg.
       Large matrices can be spread  across  several  input  lines,
with  the  carriage  returns replacing the semicolons.  The above
matrix could also have been produced by
     A = < 1 2 3
4 5 6
7 8 9 >
       Matrices can be input from the local  file  system.   Say  a
file named 'xyz' contains five lines of text,
     A = <
1 2 3
4 5 6
7 8 9
>;
  then the  MATLAB  statement  EXEC('xyz')  reads  the  matrix  and
assigns it to A .
       The FOR statement allows the generation  of  matrices  whose
elements  are  given  by  simple formulas.  Our example matrix  A
could also have been produced by
     for i = 1:3, for j = 1:3, a(i,j) = 3*(i-1)+j;
  The semicolon at the end of the  line  suppresses  the  printing,
which  in  this  case  would  have  been  nine versions of A with
changing elements.
       Several statements may be given  on  a  line,  separated  by
semicolons or commas.
       Two  consecutive  periods  anywhere  on  a   line   indicate
continuation.   The  periods  and  any  following  characters are
deleted, then another line is input  and  concatenated  onto  the
previous line.
       Two  consecutive  slashes  anywhere  on  a  line  cause  the
remainder  of  the  line  to  be  ignored.   This  is  useful for
inserting comments.
       Names of variables are formed by a letter, followed  by  any
number of letters and digits, but only the first 4 characters are
remembered.
       The special character  prime  (')  is  used  to  denote  the
transpose of a matrix, so
     x = x'
  changes the row vector above into the column vector
     X     =
       -1.3000
0.8000
3.1416
       Individual matrix elements may be  referenced  by  enclosing
their  subscripts  in  parentheses.  When any element is changed,
the entire matrix is reprinted.  For  example,  using  the  above
matrix,
a(3,3) = a(1,3) + a(3,1)
  results in
     A     =
         1.    2.    3.
4.    5.    6.
7.    8.   10.
       Addition, subtraction and  multiplication  of  matrices  are
denoted  by  +, -, and * .  The operations are performed whenever
the matrices have the proper dimensions.  For example,  with  the
above  A  and  x,  the  expressions  A  + x and x*A are incorrect
because A is 3 by 3 and x is now 3 by 1.  However,
     b = A*x
  is correct and results in the output
     B     =
        9.7248
17.6496
28.7159
  Note that both upper and lower case letters are allowed for input
(on  those  systems  which  have  both),  but  that lower case is
converted to upper case.
       There are two "matrix division" symbols in MATLAB, \ and / .
(If  your  terminal  does not have a backslash, use $instead, or see CHAR.) If A and B are matrices, then A\B and B/A correspond formally to left and right multiplication of B by the inverse of A , that is inv(A)*B and B*inv(A), but the result is obtained directly without the computation of the inverse. In the scalar case, 3\1 and 1/3 have the same value, namely one-third. In general, A\B denotes the solution X to the equation A*X = B and B/A denotes the solution to X*A = B.  Left division, A\B, is defined whenever B has as many rows as A . If A is square, it is factored using Gaussian elimination. The factors are used to solve the equations A*X(:,j) = B(:,j) where B(:,j) denotes the j-th column of B. The result is a matrix X with the same dimensions as B. If A is nearly singular (according to the LINPACK condition estimator, RCOND), a warning message is printed. If A is not square, it is factored using Householder orthogonalization with column pivoting. The factors are used to solve the under- or overdetermined equations in a least squares sense. The result is an m by n matrix X where m is the number of columns of A and n is the number of columns of B . Each column of X has at most k nonzero components, where k is the effective rank of A .  Right division, B/A, can be defined in terms of left division by B/A = (A'\B')'.  For example, since our vector b was computed as A*x, the statement  y = A\b  results in  Y =  -1.3000 0.8000 3.1416  Of course, y is not exactly equal to x because of the roundoff errors involved in both A*x and A\b , but we are not printing enough digits to see the difference. The result of the statement  e = x - y  depends upon the particular computer being used. In one case it produces  E =  1.0e-15 *  .3053 -.2498 .0000  The quantity 1.0e-15 is a scale factor which multiplies all the components which follow. Thus our vectors x and y actually agree to about 15 decimal places on this computer.  It is also possible to obtain element-by-element multiplicative operations. If A and B have the same dimensions, then A .* B denotes the matrix whose elements are simply the products of the individual elements of A and B . The expressions A ./ B and A .\ B give the quotients of the individual elements.  There are several possible output formats. The statement  long, x  results in  X = -1.300000000000000 .800000000000000 3.141592653589793  The statement  short  restores the original format.  The expression A**p means A to the p-th power. It is defined if A is a square matrix and p is a scalar. If p is an integer greater than one, the power is computed by repeated multiplication. For other values of p the calculation involves the eigenvalues and eigenvectors of A.  Previously defined matrices and matrix expressions can be used inside brackets to generate larger matrices, for example  C = <A, b; <4 2 0>*x, x'>  results in  C =  1.0000 2.0000 3.0000 9.7248 4.0000 5.0000 6.0000 17.6496 7.0000 8.0000 10.0000 28.7159 -3.6000 -1.3000 0.8000 3.1416  There are four predefined variables, EPS, FLOP, RAND and EYE. The variable EPS is used as a tolerance is determining such things as near singularity and rank. Its initial value is the distance from 1.0 to the next largest floating point number on the particular computer being used. The user may reset this to any other value, including zero. EPS is changed by CHOP, which is described in section 12.  The value of RAND is a random variable, with a choice of a uniform or a normal distribution.  The name EYE is used in place of I to denote identity matrices because I is often used as a subscript or as sqrt(-1). The dimensions of EYE are determined by context. For example,  B = A + 3*EYE  adds 3 to the diagonal elements of A and  X = EYE/A  is one of several ways in MATLAB to invert a matrix.  FLOP provides a count of the number of floating point operations, or "flops", required for each calculation.  A statement may consist of an expression alone, in which case a variable named ANS is created and the result stored in ANS for possible future use. Thus  A\A - EYE  is the same as  ANS = A\A - EYE  (Roundoff error usually causes this result to be a matrix of "small" numbers, rather than all zeros.)  All computations are done using either single or double precision real arithmetic, whichever is appropriate for the particular computer. There is no mixed-precision arithmetic. The Fortran COMPLEX data type is not used because many systems create unnecessary underflows and overflows with complex operations and because some systems do not allow double precision complex arithmetic. #### 2. MATLAB functions  Much of MATLAB's computational power comes from the various matrix functions available. The current list includes:  INV(A) - Inverse. DET(A) - Determinant. COND(A) - Condition number. RCOND(A) - A measure of nearness to singularity. EIG(A) - Eigenvalues and eigenvectors. SCHUR(A) - Schur triangular form. HESS(A) - Hessenberg or tridiagonal form. POLY(A) - Characteristic polynomial. SVD(A) - Singular value decomposition. PINV(A,eps) - Pseudoinverse with optional tolerance. RANK(A,eps) - Matrix rank with optional tolerance. LU(A) - Factors from Gaussian elimination. CHOL(A) - Factor from Cholesky factorization. QR(A) - Factors from Householder orthogonalization. RREF(A) - Reduced row echelon form. ORTH(A) - Orthogonal vectors spanning range of A. EXP(A) - e to the A. LOG(A) - Natural logarithm. SQRT(A) - Square root. SIN(A) - Trigonometric sine. COS(A) - Cosine. ATAN(A) - Arctangent. ROUND(A) - Round the elements to nearest integers. ABS(A) - Absolute value of the elements. REAL(A) - Real parts of the elements. IMAG(A) - Imaginary parts of the elements. CONJG(A) - Complex conjugate. SUM(A) - Sum of the elements. PROD(A) - Product of the elements. DIAG(A) - Extract or create diagonal matrices. TRIL(A) - Lower triangular part of A. TRIU(A) - Upper triangular part of A. NORM(A,p) - Norm with p = 1, 2 or 'Infinity'. EYE(m,n) - Portion of identity matrix. RAND(m,n) - Matrix with random elements. ONES(m,n) - Matrix of all ones. MAGIC(n) - Interesting test matrices. HILBERT(n) - Inverse Hilbert matrices. ROOTS(C) - Roots of polynomial with coefficients C. DISPLAY(A,p) - Print base p representation of A. KRON(A,B) - Kronecker tensor product of A and B. PLOT(X,Y) - Plot Y as a function of X . RAT(A) - Find "simple" rational approximation to A. USER(A) - Function defined by external program.  Some of these functions have different interpretations when the argument is a matrix or a vector and some of them have additional optional arguments. Details are given in the HELP document in the appendix.  Several of these functions can be used in a generalized assignment statement with two or three variables on the left hand side. For example  <X,D> = EIG(A)  stores the eigenvectors of A in the matrix X and a diagonal matrix containing the eigenvalues in the matrix D. The statement  EIG(A)  simply computes the eigenvalues and stores them in ANS.  Future versions of MATLAB will probably include additional functions, since they can easily be added to the system. #### 3. Rows, columns and submatrices  Individual elements of a matrix can be accessed by giving their subscripts in parentheses, eg. A(1,2), x(i), TAB(ind(k)+1). An expression used as a subscript is rounded to the nearest integer.  Individual rows and columns can be accessed using a colon ':' (or a '|') for the free subscript. For example, A(1,:) is the first row of A and A(:,j) is the j-th column. Thus  A(i,:) = A(i,:) + c*A(k,:)  adds c times the k-th row of A to the i-th row.  The colon is used in several other ways in MATLAB, but all of the uses are based on the following definition.  j:k is the same as <j, j+1, ..., k> j:k is empty if j > k . j:i:k is the same as <j, j+i, j+2i, ..., k> j:i:k is empty if i > 0 and j > k or if i < 0 and j < k .  The colon is usually used with integers, but it is possible to use arbitrary real scalars as well. Thus  1:4 is the same as <1, 2, 3, 4> 0: 0.1: 0.5 is the same as <0.0, 0.1, 0.2, 0.3, 0.4, 0.5>  In general, a subscript can be a vector. If X and V are vectors, then X(V) is <X(V(1)), X(V(2)), ..., X(V(n))> . This can also be used with matrices. If V has m components and W has n components, then A(V,W) is the m by n matrix formed from the elements of A whose subscripts are the elements of V and W. Combinations of the colon notation and the indirect subscripting allow manipulation of various submatrices. For example,  A(<1,5>,:) = A(<5,1>,:) interchanges rows 1 and 5 of A. A(2:k,1:n) is the submatrix formed from rows 2 through k and columns 1 through n of A . A(:,<3 1 2>) is a permutation of the first three columns.  The notation A(:) has a special meaning. On the right hand side of an assignment statement, it denotes all the elements of A, regarded as a single column. When an expression is assigned to A(:), the current dimensions of A, rather than of the expression, are used. #### 4. FOR, WHILE and IF  The FOR clause allows statements to be repeated a specific number of times. The general form is  FOR variable = expr, statement, ..., statement, END  The END and the comma before it may be omitted. In general, the expression may be a matrix, in which case the columns are stored one at a time in the variable and the following statements, up to the END or the end of the line, are executed. The expression is often of the form j:k, and its "columns" are simply the scalars from j to k. Some examples (assume n has already been assigned a value):  for i = 1:n, for j = 1:n, A(i,j) = 1/(i+j-1);  generates the Hilbert matrix.  for j = 2:n-1, for i = j:n-1, ... A(i,j) = 0; end; A(j,j) = j; end; A  changes all but the "outer edge" of the lower triangle and then prints the final matrix.  for h = 1.0: -0.1: -1.0, (<h, cos(pi*h)>)  prints a table of cosines.  <X,D> = EIG(A); for v = X, v, A*v  displays eigenvectors, one at a time.  The WHILE clause allows statements to be repeated an indefinite number of times. The general form is  WHILE expr relop expr, statement,..., statement, END  where relop is =, <, >, <=, >=, or <> (not equal) . The statements are repeatedly executed as long as the indicated comparison between the real parts of the first components of the two expressions is true. Here are two examples. (Exercise for the reader: What do these segments do?)  eps = 1; while 1 + eps > 1, eps = eps/2; eps = 2*eps  E = 0*A; F = E + EYE; n = 1; while NORM(E+F-E,1) > 0, E = E + F; F = A*F/n; n = n + 1; E  The IF clause allows conditional execution of statements. The general form is  IF expr relop expr, statement, ..., statement, ELSE statement, ..., statement  The first group of statements are executed if the relation is true and the second group are executed if the relation is false. The ELSE and the statements following it may be omitted. For example,  if abs(i-j) = 2, A(i,j) = 0; #### 5. Commands, text, files and macros.  MATLAB has several commands which control the output format and the overall execution of the system.  The HELP command allows on-line access to short portions of text describing various operations, functions and special characters. The entire HELP document is reproduced in an appendix.  Results are usually printed in a scaled fixed point format that shows 4 or 5 significant figures. The commands SHORT, LONG, SHORT E, LONG E and LONG Z alter the output format, but do not alter the precision of the computations or the internal storage.  The WHO, WHAT and WHY commands provide information about the functions and variables that are currently defined.  The CLEAR command erases all variables, except EPS, FLOP, RAND and EYE. The statement A = <> indicates that a "0 by 0" matrix is to be stored in A. This causes A to be erased so that its storage can be used for other variables.  The RETURN and EXIT commands cause return to the underlying operating system through the Fortran RETURN statement.  MATLAB has a limited facility for handling text. Any string of characters delineated by quotes (with two quotes used to allow one quote within the string) is saved as a vector of integer values with '1' = 1, 'A' = 10, ' ' = 36, etc. (The complete list is in the appendix under CHAR.) For example  '2*A + 3' is the same as <2 43 10 36 41 36 3>  It is possible, though seldom very meaningful, to use such strings in matrix operations. More frequently, the text is used as a special argument to various functions.  NORM(A,'inf') computes the infinity norm of A . DISPLAY(T) prints the text stored in T . EXEC('file') obtains MATLAB input from an external file. SAVE('file') stores all the current variables in a file. LOAD('file') retrieves all the variables from a file. PRINT('file',X) prints X on a file. DIARY('file') makes a copy of the complete MATLAB session.  The text can also be used in a limited string substitution macro facility. If a variable, say T, contains the source text for a MATLAB statement or expression, then the construction  > T <  causes T to be executed or evaluated. For example  T = '2*A + 3'; S = 'B = >T< + 5' A = 4; > S <  produces  B =  16.  Some other examples are given under MACRO in the appendix. This facility is useful for fairly short statements and expressions. More complicated MATLAB "programs" should use the EXEC facility.  The operations which access external files cannot be handled in a completely machine-independent manner by portable Fortran code. It is necessary for each particular installation to provide a subroutine which associates external text files with Fortran logical unit numbers. #### 6. Census example  Our first extended example involves predicting the population of the United States in 1980 using extrapolation of various fits to the census data from 1900 through 1970. There are eight observations, so we begin with the MATLAB statement  n = 8  The values of the dependent variable, the population in millions, can be entered with  y = < 75.995 91.972 105.711 123.203 ... 131.669 150.697 179.323 203.212>'  In order to produce a reasonably scaled matrix, the independent variable, time, is transformed from the interval [1900,1970] to [-1.00,0.75]. This can be accomplished directly with  t = -1.0:0.25:0.75  or in a fancier, but perhaps clearer, way with  t = 1900:10:1970; t = (t - 1940*ones(t))/40  Either of these is equivalent to  t = <-1 -.75 -.50 -.25 0 .25 .50 .75>  The interpolating polynomial of degree n-1 involves an Vandermonde matrix of order n with elements that might be generated by  for i = 1:n, for j = 1:n, a(i,j) = t(i)**(j-1);  However, this results in an error caused by 0**0 when i = 5 and j = 1 . The preferable approach is  A = ones(n,n); for i = 1:n, for j = 2:n, a(i,j) = t(i)*a(i,j-1);  Now the statement  cond(A)  produces the output  ANS =  1.1819E+03  which indicates that transformation of the time variable has resulted in a reasonably well conditioned matrix.  The statement  c = A\y  results in  C =  131.6690 41.0406 103.5396 262.4535 -326.0658 -662.0814 341.9022 533.6373  These are the coefficients in the interpolating polynomial  n-1 c + c t + ... + c t 1 2 n  Our transformation of the time variable has resulted in t = 1 corresponding to the year 1980. Consequently, the extrapolated population is simply the sum of the coefficients. This can be computed by  p = sum(c)  The result is  P =  426.0950  which indicates a 1980 population of over 426 million. Clearly, using the seventh degree interpolating polynomial to extrapolate even a fairly short distance beyond the end of the data interval is not a good idea.  The coefficients in least squares fits by polynomials of lower degree can be computed using fewer than n columns of the matrix.  for k = 1:n, c = A(:,1:k)\y, p = sum(c)  would produce the coefficients of these fits, as well as the resulting extrapolated population. If we do not want to print all the coefficients, we can simply generate a small table of populations predicted by polynomials of degrees zero through seven. We also compute the maximum deviation between the fitted and observed values.  for k = 1:n, X = A(:,1:k); c = X\y; ... d(k) = k-1; p(k) = sum(c); e(k) = norm(X*c-y,'inf'); <d, p, e>  The resulting output is  0 132.7227 70.4892 1 211.5101 9.8079 2 227.7744 5.0354 3 241.9574 3.8941 4 234.2814 4.0643 5 189.7310 2.5066 6 118.3025 1.6741 7 426.0950 0.0000  The zeroth degree fit, 132.7 million, is the result of fitting a constant to the data and is simply the average. The results obtained with polynomials of degree one through four all appear reasonable. The maximum deviation of the degree four fit is slightly greater than the degree three, even though the sum of the squares of the deviations is less. The coefficients of the highest powers in the fits of degree five and six turn out to be negative and the predicted populations of less than 200 million are probably unrealistic. The hopefully absurd prediction of the interpolating polynomial concludes the table.  We wish to emphasize that roundoff errors are not significant here. Nearly identical results would be obtained on other computers, or with other algorithms. The results simply indicate the difficulties associated with extrapolation of polynomial fits of even modest degree.  A stabilized fit by a seventh degree polynomial can be obtained using the pseudoinverse, but it requires a fairly delicate choice of a tolerance. The statement  s = svd(A)  produces the singular values  S =  3.4594 2.2121 1.0915 0.4879 0.1759 0.0617 0.0134 0.0029  We see that the last three singular values are less than 0.1 , consequently, A can be approximately by a matrix of rank five with an error less than 0.1 . The Moore-Penrose pseudoinverse of this rank five matrix is obtained from the singular value decomposition with the following statements  c = pinv(A,0.1)*y, p = sum(c), e = norm(a*c-y,'inf')  The output is  C =  134.7972 67.5055 23.5523 9.2834 3.0174 2.6503 -2.8808 3.2467  P =  241.1720  E =  3.9469  The resulting seventh degree polynomial has coefficients which are much smaller than those of the interpolating polynomial given earlier. The predicted population and the maximum deviation are reasonable. Any choice of the tolerance between the fifth and sixth singular values would produce the same results, but choices outside this range result in pseudoinverses of different rank and do not work as well.  The one term exponential approximation  y(t) = k exp(pt)  can be transformed into a linear approximation by taking logarithms.  log(y(t)) = log k + pt  = c + c t 1 2  The following segment makes use of the fact that a function of a vector is the function applied to the individual components.  X = A(:,1:2); c = X\log(y) p = exp(sum(c)) e = norm(exp(X*c)-y,'inf')  The resulting output is  C =  4.9083 0.5407  P =  232.5134  E =  4.9141  The predicted population and maximum deviation appear satisfactory and indicate that the exponential model is a reasonable one to consider.  As a curiousity, we return to the degree six polynomial. Since the coefficient of the high order term is negative and the value of the polynomial at t = 1 is positive, it must have a root at some value of t greater than one. The statements  X = A(:,1:7); c = X\y; c = c(7:-1:1); //reverse the order of the coefficients z = roots(c)  produce  Z =  1.1023- 0.0000*i 0.3021+ 0.7293*i -0.8790+ 0.6536*i -1.2939- 0.0000*i -0.8790- 0.6536*i 0.3021- 0.7293*i  There is only one real, positive root. The corresponding time on the original scale is  1940 + 40*real(z(1))  = 1984.091  We conclude that the United States population should become zero early in February of 1984. #### 7. Partial differential equation example  Our second extended example is a boundary value problem for Laplace's equation. The underlying physical problem involves the conductivity of a medium with cylindrical inclusions and is considered by Keller and Sachs [7].  Find a function u(x,y) satisfying Laplace's equation  u + u = 0 xx yy  The domain is a unit square with a quarter circle of radius rho removed from one corner. There are Neumann conditions on the top and bottom edges and Dirichlet conditions on the remainder of the boundary.  u = 0 n  ------------- | . | . | . | . u = 1 | . | . | . u = 0 | | | | | | | | u = 1 | | | | | | ------------------------  u = 0 n  The effective conductivity of an medium is then given by the integral along the left edge,  1 sigma = integral u (0,y) dy 0 n  It is of interest to study the relation between the radius rho and the conductivity sigma. In particular, as rho approaches one, sigma becomes infinite. Keller and Sachs use a finite difference approximation. The following technique makes use of the fact that the equation is actually Laplace's equation and leads to a much smaller matrix problem to solve.  Consider an approximate solution of the form  n 2j-1 u = sum c r cos(2j-1)t j=1 j  where r,t are polar coordinates (t is theta). The coefficients are to be determined. For any set of coefficients, this function already satisfies the differential equation because the basis functions are harmonic; it satisfies the normal derivative boundary condition on the bottom edge of the domain because we used cos t in preference to sin t ; and it satisfies the boundary condition on the left edge of the domain because we use only odd multiples of t .  The computational task is to find coefficients so that the boundary conditions on the remaining edges are satisfied as well as possible. To accomplish this, pick m points (r,t) on the remaining edges. It is desirable to have m > n and in practice we usually choose m to be two or three times as large as n . Typical values of n are 10 or 20 and of m are 20 to 60. An m by n matrix A is generated. The i,j element is the j-th basis function, or its normal derivative, evaluated at the i-th boundary point. A right hand side with m components is also generated. In this example, the elements of the right hand side are either zero or one. The coefficients are then found by solving the overdetermined set of equations  Ac = b  in a least squares sense.  Once the coefficients have been determined, the approximate solution is defined everywhere on the domain. It is then possible to compute the effective conductivity sigma . In fact, a very simple formula results,  n j-1 sigma = sum (-1) c j=1 j  To use MATLAB for this problem, the following "program" is first stored in the local computer file system, say under the name "PDE".  //Conductivity example. //Parameters --- rho //radius of cylindrical inclusion n //number of terms in solution m //number of boundary points //initialize operation counter flop = <0 0>; //initialize variables m1 = round(m/3); //number of points on each straight edge m2 = m - m1; //number of points with Dirichlet conditions pi = 4*atan(1); //generate points in Cartesian coordinates //right hand edge for i = 1:m1, x(i) = 1; y(i) = (1-rho)*(i-1)/(m1-1); //top edge for i = m2+1:m, x(i) = (1-rho)*(m-i)/(m-m2-1); y(i) = 1; //circular edge for i = m1+1:m2, t = pi/2*(i-m1)/(m2-m1+1); ... x(i) = 1-rho*sin(t); y(i) = 1-rho*cos(t); //convert to polar coordinates for i = 1:m-1, th(i) = atan(y(i)/x(i)); ... r(i) = sqrt(x(i)**2+y(i)**2); th(m) = pi/2; r(m) = 1; //generate matrix //Dirichlet conditions for i = 1:m2, for j = 1:n, k = 2*j-1; ... a(i,j) = r(i)**k*cos(k*th(i)); //Neumann conditions for i = m2+1:m, for j = 1:n, k = 2*j-1; ... a(i,j) = k*r(i)**(k-1)*sin((k-1)*th(i)); //generate right hand side for i = 1:m2, b(i) = 1; for i = m2+1:m, b(i) = 0; //solve for coefficients c = A\b //compute effective conductivity c(2:2:n) = -c(2:2:n); sigma = sum(c) //output total operation count ops = flop(2)  The program can be used within MATLAB by setting the three parameters and then accessing the file. For example,  rho = .9; n = 15; m = 30; exec('PDE')  The resulting output is RHO =  .9000  N =  15.  M =  30.  C =  2.2275 -2.2724 1.1448 0.1455 -0.1678 -0.0005 -0.3785 0.2299 0.3228 -0.2242 -0.1311 0.0924 0.0310 -0.0154 -0.0038  SIGM =  5.0895  OPS =  16204.  A total of 16204 floating point operations were necessary to set up the matrix, solve for the coefficients and compute the conductivity. The operation count is roughly proportional to m*n**2. The results obtained for sigma as a function of rho by this approach are essentially the same as those obtained by the finite difference technique of Keller and Sachs, but the computational effort involved is much less. #### 8. Eigenvalue sensitivity example  In this example, we construct a matrix whose eigenvalues are moderately sensitive to perturbations and then analyze that sensitivity. We begin with the statement  B = <3 0 7; 0 2 0; 0 0 1>  which produces  B =  3. 0. 7. 0. 2. 0. 0. 0. 1.  Obviously, the eigenvalues of B are 1, 2 and 3 . Moreover, since B is not symmetric, these eigenvalues are slightly sensitive to perturbation. (The value b(1,3) = 7 was chosen so that the elements of the matrix A below are less than 1000.)  We now generate a similarity transformation to disguise the eigenvalues and make them more sensitive.  L = <1 0 0; 2 1 0; -3 4 1>, M = L\L'  L =  1. 0. 0. 2. 1. 0. -3. 4. 1.  M =  1.0000 2.0000 -3.0000 -2.0000 -3.0000 10.0000 11.0000 18.0000 -48.0000  The matrix M has determinant equal to 1 and is moderately badly conditioned. The similarity transformation is  A = M*B/M  A =  -64.0000 82.0000 21.0000 144.0000 -178.0000 -46.0000 -771.0000 962.0000 248.0000  Because det(M) = 1 , the elements of A would be exact integers if there were no roundoff. So, A = round(A)  A =  -64. 82. 21. 144. -178. -46. -771. 962. 248.  This, then, is our test matrix. We can now forget how it was generated and analyze its eigenvalues.  <X,D> = eig(A)  D =  3.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 2.0000  X =  -.0891 3.4903 41.8091 .1782 -9.1284 -62.7136 -.9800 46.4473 376.2818  Since A is similar to B, its eigenvalues are also 1, 2 and 3. They happen to be computed in another order by the EISPACK subroutines. The fact that the columns of X, which are the eigenvectors, are so far from being orthonormal is our first indication that the eigenvalues are sensitive. To see this sensitivity, we display more figures of the computed eigenvalues.  long, diag(D)  ANS =  2.999999999973599 1.000000000015625 2.000000000011505  We see that, on this computer, the last five significant figures are contaminated by roundoff error. A somewhat superficial explanation of this is provided by  short, cond(X)  ANS =  3.2216e+05  The condition number of X gives an upper bound for the relative error in the computed eigenvalues. However, this condition number is affected by scaling.  X = X/diag(X(3,:)), cond(X)  X =  .0909 .0751 .1111 -.1818 -.1965 -.1667 1.0000 1.0000 1.0000  ANS =  1.7692e+03  Rescaling the eigenvectors so that their last components are all equal to one has two consequences. The condition of X is decreased by over two orders of magnitude. (This is about the minimum condition that can be obtained by such diagonal scaling.) Moreover, it is now apparent that the three eigenvectors are nearly parallel.  More detailed information on the sensitivity of the individual eigenvalues involves the left eigenvectors.  Y = inv(X'), Y'*A*X  Y =  -511.5000 259.5000 252.0000 616.0000 -346.0000 -270.0000 159.5000 -86.5000 -72.0000  ANS =  3.0000 .0000 .0000 .0000 1.0000 .0000 .0000 .0000 2.0000  We are now in a position to compute the sensitivities of the individual eigenvalues.  for j = 1:3, c(j) = norm(Y(:,j))*norm(X(:,j)); end, C  C =  833.1092 450.7228 383.7564  These three numbers are the reciprocals of the cosines of the angles between the left and right eigenvectors. It can be shown that perturbation of the elements of A can result in a perturbation of the j-th eigenvalue which is c(j) times as large. In this example, the first eigenvalue has the largest sensitivity.  We now proceed to show that A is close to a matrix with a double eigenvalue. The direction of the required perturbation is given by  E = -1.e-6*Y(:,1)*X(:,1)'  E =  1.0e-03 *  .0465 -.0930 .5115 -.0560 .1120 -.6160 -.0145 .0290 -.1595  With some trial and error which we do not show, we bracket the point where two eigenvalues of a perturbed A coalesce and then become complex.  eig(A + .4*E), eig(A + .5*E)  ANS =  1.1500 2.5996 2.2504  ANS =  2.4067 + .1753*i 2.4067 - .1753*i 1.1866 + 0.0000*i  Now, a bisecting search, driven by the imaginary part of one of the eigenvalues, finds the point where two eigenvalues are nearly equal.  r = .4; s = .5;  while s-r > 1.e-14, t = (r+s)/2; d = eig(A+t*E); ... if imag(d(1))=0, r = t; else, s = t;  long, T  T =  .450380734134507  Finally, we display the perturbed matrix, which is obviously close to the original, and its pair of nearly equal eigenvalues. (We have dropped a few digits from the long output.)  A+t*E, eig(A+t*E)  A  -63.999979057 81.999958114 21.000230369 143.999974778 -177.999949557 -46.000277434 -771.000006530 962.000013061 247.999928164  ANS =  2.415741150 2.415740621 1.168517777  The first two eigenvectors of A + t*E are almost indistinguishable indicating that the perturbed matrix is almost defective.  <X,D> = eig(A+t*E); X = X/diag(X(3,:))  X =  .096019578 .096019586 .071608466 -.178329614 -.178329608 -.199190520 1.000000000 1.000000000 1.000000000  short, cond(X)  ANS =  3.3997e+09 #### 9. Syntax diagrams  A formal description of the language acceptable to MATLAB, as well as a flow chart of the MATLAB program, is provided by the syntax diagrams or syntax graphs of Wirth [6]. There are eleven non-terminal symbols in the language:  line, statement, clause, expression, term, factor, number, integer, name, command, text .  The diagrams define each of the non-terminal symbols using the others and the terminal symbols:  letter -- A through Z, digit -- 0 through 9, char -- ( ) ; : + - * / \ = . , < > quote -- '  line  |-----> statement >----| | | |-----> clause >-------| | | -------|-----> expr >---------|------> | | | | | |-----> command >------| | | | | | | |-> > >-> expr >-> < >-| | | | | | | |----------------------| | | | | |-< ; <-| | |--------| |---------| |-< , <-|  statement  |-> name >--------------------------------| | | | | | |--> : >---| | | | | | | | |-> ( >---|-> expr >-|---> ) >-| | | | | -----| |-----< , <----| |--> = >--> expr >---> | | | |--< , <---| | | | | | |-> < >---> name >---> > >----------------|  clause  |---> FOR >---> name >---> = >---> expr >--------------| | | | |-> WHILE >-| | |-| |-> expr >---------------------- | | |-> IF >-| | | | | | | | -----| < <= = <> >= > |----> | | | | | | | | | ----------------------> expr >--| | | |---> ELSE >--------------------------------------------| | | |---> END >--------------------------------------------|  expr  |-> + >-| | | -------|-------|-------> term >----------> | | | | |-> - >-| | |-< + <-| | | | | | |--|-< - <-|--| | | |-< : <-|  term  ---------------------> factor >----------------------> | | | |-< * <-| | | |-------| | | |-------| | |--| |--|-< / <-|--| |--| |-< . <-| | | |-< . <-| |-< \ <-|  factor  |----------------> number >---------------| | | |-> name >--------------------------------| | | | | | |--> : >---| | | | | | | | |-> ( >---|-> expr >-|---> ) >-| | | | | | |-----< , <----| | | | -----|------------> ( >-----> expr >-----> ) >-|-|-------|-----> | | | | | | |--------------| | |-> ' >-| | | | | | | |------------> < >-|---> expr >---|-> > >-| | | | | | | | |--< <---| | | | | | | | | |--< ; <---| | | | | | | | | |--< , <---| | | | | | |------------> > >-----> expr >-----> < >-| | | | | |-----> factor >---> ** >---> factor >----| | | | |------------> ' >-----> text >-----> ' >-------------|  number  |----------| |-> + >-| | | | | -----> int >-----> . >---> int >-----> E >---------> int >----> | | | | | | | | | |-> - >-| | | | | | |---------------------------------------------|  int  ------------> digit >-----------> | | |-----------|  name  |--< letter <--| | | ------> letter >--|--------------|-----> | | |--< digit <--|  command  |--> name >--| | | --------> name >--------|------------|----> | | |--> char >--| | | |---> ' >----|  text  |-> letter >--| | | |-> digit >---| ----------------| |--------------> | |-> char >----| | | | | | | |-> ' >-> ' >-| | | | |---------------------| #### 10. The parser-interpreter  The structure of the parser-interpreter is similar to that of Wirth's compiler [6] for his simple language, PL/0 , except that MATLAB is programmed in Fortran, which does not have explicit recursion. The interrelation of the primary subroutines is shown in the following diagram.  MAIN | MATLAB |--CLAUSE | | | PARSE-----|--EXPR----TERM----FACTOR | | | | | |-------|-------| | | | | | STACK1 STACK2 STACKG | |--STACKP--PRINT | |--COMAND | | | |--CGECO | | | |--CGEFA | | |--MATFN1--|--CGESL | | | |--CGEDI | | | |--CPOFA | | | |--IMTQL2 | | | |--HTRIDI | | |--MATFN2--|--HTRIBK | | | |--CORTH | | | |--COMQR3 | | |--MATFN3-----CSVDC | | | |--CQRDC |--MATFN4--| | |--CQRSL | | | |--FILES |--MATFN5--| |--SAVLOD  Subroutine PARSE controls the interpretation of each statement. It calls subroutines that process the various syntactic quantities such as command, expression, term and factor. A fairly simple program stack mechanism allows these subroutines to recursively "call" each other along the lines allowed by the syntax diagrams. The four STACK subroutines manage the variable memory and perform elementary operations, such as matrix addition and transposition.  The four subroutines MATFN1 though MATFN4 are called whenever "serious" matrix computations are required. They are interface routines which call the various LINPACK and EISPACK subroutines. MATFN5 primarily handles the file access tasks.  Two large real arrays, STKR and STKI, are used to store all the matrices. Four integer arrays are used to store the names, the row and column dimensions, and the pointers into the real stacks. The following diagram illustrates this storage scheme.  TOP IDSTK MSTK NSTK LSTK STKR STKI -- -- -- -- -- -- -- -- -------- -------- | |--->| | | | | | | | | | |----------->| | | | -- -- -- -- -- -- -- -- -------- -------- | | | | | | | | | | | | | | | -- -- -- -- -- -- -- -------- -------- . . . . . . . . . . . . . . . . . . -- -- -- -- -- -- -- -------- -------- BOT | | | | | | | | | | | | | | | -- -- -- -- -- -- -- -- -------- -------- | |--->| X| | | | | 2| | 1| | |----------->| 3.14 | | 0.00 | -- -- -- -- -- -- -- -- -------- -------- | A| | | | | 2| | 2| | |--------- | 0.00 | | 1.00 | -- -- -- -- -- -- -- \ -------- -------- | E| P| S| | | 1| | 1| | |------- ->| 11.00 | | 0.00 | -- -- -- -- -- -- -- \ -------- -------- | F| L| O| P| | 1| | 2| | |------ \ | 21.00 | | 0.00 | -- -- -- -- -- -- -- \ \ -------- -------- | E| Y| E| | |-1| |-1| | |--- \ | | 12.00 | | 0.00 | -- -- -- -- -- -- -- \ | | -------- -------- | R| A| N| D| | 1| | 1| | |- \ | | | 22.00 | | 0.00 | -- -- -- -- -- -- -- \ | \ \ -------- -------- | \ \ ->| 1.E-15 | | 0.00 | \ \ \ -------- -------- \ \ ->| 0.00 | | 0.00 | \ \ -------- -------- \ \ | 0.00 | | 0.00 | \ \ -------- -------- \ ->| 1.00 | | 0.00 | \ -------- -------- --->| URAND | | 0.00 | -------- --------  The top portion of the stack is used for temporary variables and the bottom portion for saved variables. The figure shows the situation after the line A = <11,12; 21,22>, x = <3.14, sqrt(-1)>'  has been processed. The four permanent names, EPS, FLOP, RAND and EYE, occupy the last four positions of the variable stacks. RAND has dimensions 1 by 1, but whenever its value is requested, a random number generator is used instead. EYE has dimensions -1 by -1 to indicate that the actual dimensions must be determined later by context. The two saved variables have dimensions 2 by 2 and 2 by 1 and so take up a total of 6 locations.  Subsequent statements involving A and x will result in temporary copies being made in the top of the stack for use in the actual calculations. Whenever the top of the stack reaches the bottom, a message indicating memory has been exceeded is printed, but the current variables are not affected.  This modular structure makes it possible to implement MATLAB on a system with a limited amount of memory. The object code for the MATFN's and the LINPACK-EISPACK subroutines is rarely needed. Although it is not standard, many Fortran operating systems provide some overlay mechanism so that this code is brought into the main memory only when required. The variables, which occupy a relatively small portion of the memory, remain in place, while the subroutines which process them are loaded a few at a time. #### 11. The numerical algorithms  The algorithms underlying the basic MATLAB functions are described in the LINPACK and EISPACK guides [1-3]. The following list gives the subroutines used by these functions.  INV(A) - CGECO,CGEDI DET(A) - CGECO,CGEDI LU(A) - CGEFA RCOND(A) - CGECO CHOL(A) - CPOFA SVD(A) - CSVDC COND(A) - CSVDC NORM(A,2) - CSVDC PINV(A,eps) - CSVDC RANK(A,eps) - CSVDC QR(A) - CQRDC,CQRSL ORTH(A) - CQRDC,CSQSL A\B and B/A - CGECO,CGESL if A is square. - CQRDC,CQRSL if A is not square. EIG(A) - HTRIDI,IMTQL2,HTRIBK if A is Hermitian. - CORTH,COMQR2 if A is not Hermitian. SCHUR(A) - same as EIG. HESS(A) - same as EIG.  Minor modifications were made to all these subroutines. The LINPACK routines were changed to replace the Fortran complex arithmetic with explicit references to real and imaginary parts. Since most of the floating point arithmetic is concentrated in a few low-level subroutines which perform vector operations (the Basic Linear Algebra Subprograms), this was not an extensive change. It also facilitated implementation of the FLOP and CHOP features which count and optionally truncate each floating point operation.  The EISPACK subroutine COMQR2 was modified to allow access to the Schur triangular form, ordinarily just an intermediate result. IMTQL2 was modified to make computation of the eigenvectors optional. Both subroutines were modified to eliminate the machine-dependent accuracy parameter and all the EISPACK subroutines were changed to include FLOP and CHOP.  The algorithms employed for the POLY and ROOTS functions illustrate an interesting aspect of the modern approach to eigenvalue computation. POLY(A) generates the characteristic polynomial of A and ROOTS(POLY(A)) finds the roots of that polynomial, which are, of course, the eigenvalues of A . But both POLY and ROOTS use EISPACK eigenvalues subroutines, which are based on similarity transformations. So the classical approach which characterizes eigenvalues as roots of the characteristic polynomial is actually reversed.  If A is an n by n matrix, POLY(A) produces the coefficients C(1) through C(n+1), with C(1) = 1, in  DET(z*EYE-A) = C(1)*z**n + ... + C(n)*z + C(n+1) .  The algorithm can be expressed compactly using MATLAB:  Z = EIG(A); C = 0*ONES(n+1,1); C(1) = 1; for j = 1:n, C(2:j+1) = C(2:j+1) - Z(j)*C(1:j); C  This recursion is easily derived by expanding the product  (z - z(1))*(z - z(2))* ... * (z-z(n)) .  It is possible to prove that POLY(A) produces the coefficients in the characteristic polynomial of a matrix within roundoff error of A . This is true even if the eigenvalues of A are badly conditioned. The traditional algorithms for obtaining the characteristic polynomial which do not use the eigenvalues do not have such satisfactory numerical properties.  If C is a vector with n+1 components, ROOTS(C) finds the roots of the polynomial of degree n ,  p(z) = C(1)*z**n + ... + C(n)*z + C(n+1) .  The algorithm simply involves computing the eigenvalues of the companion matrix:  A = 0*ONES(n,n) for j = 1:n, A(1,j) = -C(j+1)/C(1); for i = 2:n, A(i,i-1) = 1; EIG(A)  It is possible to prove that the results produced are the exact eigenvalues of a matrix within roundoff error of the companion matrix A, but this does not mean that they are the exact roots of a polynomial with coefficients within roundoff error of those in C . There are more accurate, more efficient methods for finding polynomial roots, but this approach has the crucial advantage that it does not require very much additional code.  The elementary functions EXP, LOG, SQRT, SIN, COS and ATAN are applied to square matrices by diagonalizing the matrix, applying the functions to the individual eigenvalues and then transforming back. For example, EXP(A) is computed by  <X,D> = EIG(A); for j = 1:n, D(j,j) = EXP(D(j,j)); X*D/X  This is essentially method number 14 out of the 19 'dubious' possibilities described in [8]. It is dubious because it doesn't always work. The matrix of eigenvectors X can be arbitrarily badly conditioned and all accuracy lost in the computation of X*D/X. A warning message is printed if RCOND(X) is very small, but this only catches the extreme cases. An example of a case not detected is when A has a double eigenvalue, but theoretically only one linearly independent eigenvector associated with it. The computed eigenvalues will be separated by something on the order of the square root of the roundoff level. This separation will be reflected in RCOND(X) which will probably not be small enough to trigger the error message. The computed EXP(A) will be accurate to only half precision. Better methods are known for computing EXP(A), but they do not easily extend to the other five functions and would require a considerable amount of additional code.  The expression A**p is evaluated by repeated multiplication if p is an integer greater than 1. Otherwise it is evaluated by  <X,D> = EIG(A); for j = 1:n, D(j,j) = EXP(p*LOG(D(j,j))) X*D/X  This suffers from the same potential loss of accuracy if X is badly conditioned. It was partly for this reason that the case p = 1 is included in the general case. Comparison of A**1 with A gives some idea of the loss of accuracy for other values of p and for the elementary functions.  RREF, the reduced row echelon form, is of some interest in theoretical linear algebra, although it has little computational value. It is included in MATLAB for pedagogical reasons. The algorithm is essentially Gauss-Jordan elimination with detection of negligible columns applied to rectangular matrices.  There are three separate places in MATLAB where the rank of a matrix is implicitly computed: in RREF(A), in A\B for non- square A, and in the pseudoinverse PINV(A). Three different algorithms with three different criteria for negligibility are used and so it is possible that three different values could be produced for the same matrix. With RREF(A), the rank of A is the number of nonzero rows. The elimination algorithm used for RREF is the fastest of the three rank-determining algorithms, but it is the least sophisticated numerically and the least reliable. With A\B, the algorithm is essentially that used by example subroutine SQRST in chapter 9 of the LINPACK guide. With PINV(A), the algorithm is based on the singular value decomposition and is described in chapter 11 of the LINPACK guide. The SVD algorithm is the most time-consuming, but the most reliable and is therefore also used for RANK(A).  The uniformly distributed random numbers in RAND are obtained from the machine-independent random number generator URAND described in [9]. It is possible to switch to normally distributed random numbers, which are obtained using a transformation also described in [9].  The computation of  2 2 sqrt(a + b )  is required in many matrix algorithms, particularly those involving complex arithmetic. A new approach to carrying out this operation is described by Moler and Morrison [10]. It is a cubically convergent algorithm which starts with a and b , rather than with their squares, and thereby avoids destructive arithmetic underflows and overflows. In MATLAB, the algorithm is used for complex modulus, Euclidean vector norm, plane rotations, and the shift calculation in the eigenvalue and singular value iterations. #### 12. FLOP and CHOP  Detailed information about the amount of work involved in matrix calculations and the resulting accuracy is provided by FLOP and CHOP. The basic unit of work is the "flop", or floating point operation. Roughly, one flop is one execution of a Fortran statement like  S = S + X(I)*Y(I)  or  Y(I) = Y(I) + T*X(I)  In other words, it consists of one floating point multiplication, together with one floating point addition and the associated indexing and storage reference operations.  MATLAB will print the number of flops required for a particular statement when the statement is terminated by an extra comma. For example, the line  n = 20; RAND(n)*RAND(n);,  ends with an extra comma. Two 20 by 20 random matrices are generated and multiplied together. The result is assigned to ANS, but the semicolon suppresses its printing. The only output is  8800 flops  This is n**3 + 2*n**2 flops, n**2 for each random matrix and n**3 for the product.  FLOP is a predefined vector with two components. FLOP(1) is the number of flops used by the most recently executed statement, except that statements with zero flops are ignored. For example, after executing the previous statement,  flop(1)/n**3  results in  ANS =  1.1000  FLOP(2) is the cumulative total of all the flops used since the beginning of the MATLAB session. The statement  FLOP = <0 0>  resets the total.  There are several difficulties associated with keeping a precise count of floating point operations. An addition or subtraction that is not paired with a multiplication is usually counted as a flop. The same is true of an isolated multiplication that is not paired with an addition. Each floating point division counts as a flop. But the number of operations required by system dependent library functions such as square root cannot be counted, so most elementary functions are arbitrarily counted as using only one flop.  The biggest difficulty occurs with complex arithmetic. Almost all operations on the real parts of matrices are counted. However, the operations on the complex parts of matrices are counted only when they involve nonzero elements. This means that simple operations on nonreal matrices require only about twice as many flops as the same operations on real matrices. This factor of two is not necessarily an accurate measure of the relative costs of real and complex arithmetic.  The result of each floating point operation may also be "chopped" to simulate a computer with a shorter word length. The details of this chopping operation depend upon the format of the floating point word. Usually, the fraction in the floating point word can be regarded as consisting of several octal or hexadecimal digits. The least significant of these digits can be set to zero by a logical masking operation. Thus the statement  CHOP(p)  causes the p least significant octal or hexadecimal digits in the result of each floating point operation to be set to zero. For example, if the computer being used has an IBM 360 long floating point word with 14 hexadecimal digits in the fraction, then CHOP(8) results in simulation of a computer with only 6 hexadecimal digits in the fraction, i.e. a short floating point word. On a computer such as the CDC 6600 with 16 octal digits, CHOP(8) results in about the same accuracy because the remaining 8 octal digits represent the same number of bits as 6 hexadecimal digits.  Some idea of the effect of CHOP on any particular system can be obtained by executing the following statements.  long, t = 1/10 long z, t = 1/10 chop(8) long, t = 1/10 long z, t = 1/10  The following Fortran subprograms illustrate more details of FLOP and CHOP. The first subprogram is a simplified example of a system-dependent function used within MATLAB itself. The common variable FLP is essentially the first component of the variable FLOP. The common variable CHP is initially zero, but it is set to p by the statement CHOP(p). To shorten the DATA statement, we assume there are only 6 hexadecimal digits. We also assume an extension of Fortran that allows .AND. to be used as a binary operation between two real variables.  REAL FUNCTION FLOP(X) REAL X INTEGER FLP,CHP COMMON FLP,CHP REAL MASK(5) DATA MASK/ZFFFFFFF0,ZFFFFFF00,ZFFFFF000,ZFFFF0000,ZFFF00000/ FLP = FLP + 1 IF (CHP .EQ. 0) FLOP = X IF (CHP .GE. 1 .AND. CHP .LE. 5) FLOP = X .AND. MASK(CHP) IF (CHP .GE. 6) FLOP = 0.0 RETURN END  The following subroutine illustrates a typical use of the previous function within MATLAB. It is a simplified version of the Basic Linear Algebra Subprogram that adds a scalar multiple of one vector to another. We assume here that the vectors are stored with a memory increment of one.  SUBROUTINE SAXPY(N,TR,TI,XR,XI,YR,YI) REAL TR,TI,XR(N),XI(N),YR(N),YI(N),FLOP IF (N .LE. 0) RETURN IF (TR .EQ. 0.0 .AND. TI .EQ. 0.0) RETURN DO 10 I = 1, N YR(I) = FLOP(YR(I) + TR*XR(I) - TI*XI(I)) YI(I) = YI(I) + TR*XI(I) + TI*XR(I) IF (YI(I) .NE. 0.0D0) YI(I) = FLOP(YI(I)) 10 CONTINUE RETURN END  The saxpy operation is perhaps the most fundamental operation within LINPACK. It is used in the computation of the LU, the QR and the SVD factorizations, and in several other places. We see that adding a multiple of one vector with n components to another uses n flops if the vectors are real and between n and 2*n flops if the vectors have nonzero imaginary components.  The permanent MATLAB variable EPS is reset by the statement CHOP(p). Its new value is usually the smallest inverse power of two that satisfies the Fortran logical test  FLOP(1.0+EPS) .GT. 1.0  However, if EPS had been directly reset to a larger value, the old value is retained. #### 13. Communicating with other programs  There are four different ways MATLAB can be used in conjunction with other programs: -- USER, -- EXEC, -- SAVE and LOAD, -- MATZ, CALL and RETURN .  Let us illustrate each of these by the following simple example.  n = 6 for i = 1:n, for j = 1:n, a(i,j) = abs(i-j); A X = inv(A)  The example A could be introduced into MATLAB by writing the following Fortran subroutine.  SUBROUTINE USER(A,M,N,S,T) DOUBLE PRECISION A(1),S,T N = IDINT(A(1)) M = N DO 10 J = 1, N DO 10 I = 1, N K = I + (J-1)*M A(K) = IABS(I-J) 10 CONTINUE RETURN END  This subroutine should be compiled and linked into MATLAB in place of the original version of USER. Then the MATLAB statements  n = 6 A = user(n) X = inv(A)  do the job.  The example A could be generated by storing the following text in a file named, say, EXAMPLE .  for i = 1:n, for j = 1:n, a(i,j) = abs(i-j);  Then the MATLAB statements  n = 6 exec('EXAMPLE',0) X = inv(A)  have the desired effect. The 0 as the optional second parameter of exec indicates that the text in the file should not be printed on the terminal.  The matrices A and X could also be stored in files. Two separate main programs would be involved. The first is:  PROGRAM MAINA DOUBLE PRECISION A(10,10) N = 6 DO 10 J = 1, N DO 10 I = 1, N A(I,J) = IABS(I-J) 10 CONTINUE OPEN(UNIT=1,FILE='A') WRITE(1,101) N,N 101 FORMAT('A ',2I4) DO 20 J = 1, N WRITE(1,102) (A(I,J),I=1,N) 20 CONTINUE 102 FORMAT(4Z18) END  The OPEN statement may take different forms on different systems. It attaches Fortran logical unit number 1 to the file named A. The FORMAT number 102 may also be system dependent. This particular one is appropriate for hexadecimal computers with an 8 byte double precision floating point word. Check, or modify, MATLAB subroutine SAVLOD.  After this program is executed, enter MATLAB and give the following statements:  load('A') X = inv(A) save('X',X)  If all goes according to plan, this will read the matrix A from the file A, invert it, store the inverse in X and then write the matrix X on the file X . The following program can then access X .  PROGRAM MAINX DOUBLE PRECISION X(10,10) OPEN(UNIT=1,FILE='X') REWIND 1 READ (1,101) ID,M,N 101 FORMAT(A4,2I4) DO 10 J = 1, N READ(1,102) (X(I,J),I=1,M) 10 CONTINUE 102 FORMAT(4Z18) ... ...  The most elaborate mechanism involves using MATLAB as a subroutine within another program. Communication with the MATLAB stack is accomplished using subroutine MATZ which is distributed with MATLAB, but which is not used by MATLAB itself. The preample of MATZ is:  SUBROUTINE MATZ(A,LDA,M,N,IDA,JOB,IERR) INTEGER LDA,M,N,IDA(1),JOB,IERR DOUBLE PRECISION A(LDA,N) C C ACCESS MATLAB VARIABLE STACK C A IS AN M BY N MATRIX, STORED IN AN ARRAY WITH C LEADING DIMENSION LDA. C IDA IS THE NAME OF A. C IF IDA IS AN INTEGER K LESS THAN 10, THEN THE NAME IS 'A'K C OTHERWISE, IDA(1:4) IS FOUR CHARACTERS, FORMAT 4A1. C JOB = 0 GET REAL A FROM MATLAB, C = 1 PUT REAL A INTO MATLAB, C = 10 GET IMAG PART OF A FROM MATLAB, C = 11 PUT IMAG PART OF A INTO MATLAB. C RETURN WITH NONZERO IERR AFTER MATLAB ERROR MESSAGE. C C USES MATLAB ROUTINES STACKG, STACKP AND ERROR  The preample of subroutine MATLAB is:  SUBROUTINE MATLAB(INIT) C INIT = 0 FOR FIRST ENTRY, NONZERO FOR SUBSEQUENT ENTRIES  To do our example, write the following program:  DOUBLE PRECISION A(10,10),X(10,10) INTEGER IDA(4),IDX(4) DATA LDA/10/ DATA IDA/'A',' ',' ',' '/, IDX/'X',' ',' ',' '/ CALL MATLAB(0) N = 6 DO 10 J = 1, N DO 10 I = 1, N A(I,J) = IABS(I-J) 10 CONTINUE CALL MATZ(A,LDA,N,N,IDA,1,IERR) IF (IERR .NE. 0) GO TO ... CALL MATLAB(1) CALL MATZ(X,LDA,N,N,IDX,0,IERR) IF (IERR .NE. 0) GO TO ... ... ...  When this program is executed, the call to MATLAB(0) produces the MATLAB greeting, then waits for input. The command  return  sends control back to our example program. The matrix A is generated by the program and sent to the stack by the first call to MATZ. The call to MATLAB(1) produces the MATLAB prompt. Then the statements  X = inv(A) return  will invert our matrix, put the result on the stack and go back to our program. The second call to MATZ will retrieve X .  By the way, this matrix X is interesting. Take a look at round(2*(n-1)*X).  References  [1] J. J. Dongarra, J. R. Bunch, C. B. Moler and G. W. Stewart, LINPACK Users' Guide, Society for Industrial and Applied Mathematics, Philadelphia, 1979.  [2] B. T. Smith, J. M. Boyle, J. J. Dongarra, B. S. Garbow, Y. Ikebe, V. C. Klema, C. B. Moler, Matrix Eigensystem Routines -- EISPACK Guide, Lecture Notes in Computer Science, volume 6, second edition, Springer-Verlag, 1976.  [3] B. S. Garbow, J. M. Boyle, J. J. Dongarra, C. B. Moler, Matrix Eigensystem Routines -- EISPACK Guide Extension, Lecture Notes in Computer Science, volume 51, Springer- Verlag, 1977. [4] S. Cohen and S. Piper, SPEAKEASY III Reference Manual, Speakeasy Computing Corp., Chicago, Ill., 1979.  [5] J. H. Wilkinson and C. Reinsch, Handbook for Automatic Computation, volume II, Linear Algebra, Springer-Verlag, 1971.  [6] Niklaus Wirth, Algorithms + Data Structures = Programs, Prentice-Hall, 1976.  [7] H. B. Keller and D. Sachs, "Calculations of the Conductivity of a Medium Containing Cylindrical Inclusions", J. Applied Physics 35, 537-538, 1964.  [8] C. B. Moler and C. F. Van Loan, Nineteen Dubious Ways to Compute the Exponential of a Matrix, SIAM Review 20, 801- 836, 1979.  [9] G. E. Forsythe, M. A. Malcolm and C. B. Moler, Computer Methods for Mathematical Computations, Prentice-Hall, 1977.  [10] C. B. Moler and D. R. Morrison, "Replacing square roots by Pythagorean sums", University of New Mexico, Computer Science Department, technical report, submitted for publication, 1980. #### Appendix. The HELP document  NEWS MATLAB NEWS dated May, 1981. This describes recent or local changes. The new features added since the November, 1980, printing of the Users' Guide include DIARY, EDIT, KRON, MACRO, PLOT, RAT, TRIL, TRIU and six element-by-element operations: .* ./ .\ .*. ./. .\. Some additional capabilities have been added to EXIT, RANDOM, RCOND, SIZE and SVD.  INTRO Welcome to MATLAB.  Here are a few sample statements:  A = <1 2; 3 4> b = <5 6>' x = A\b <V,D> = eig(A), norm(A-V*D/V) help \ , help eig exec('demo',7)  For more information, see the MATLAB Users' Guide which is contained in file ... or may be obtained from ... .  HELP HELP gives assistance. HELP HELP obviously prints this message. To see all the HELP messages, list the file ... .  < < > Brackets used in forming vectors and matrices. <6.9 9.64 SQRT(-1)> is a vector with three elements separated by blanks. <6.9, 9.64, sqrt(-1)> is the same thing. <1+I 2-I 3> and <1 +I 2 -I 3> are not the same. The first has three elements, the second has five. <11 12 13; 21 22 23> is a 2 by 3 matrix . The semicolon ends the first row.  Vectors and matrices can be used inside < > brackets. <A B; C> is allowed if the number of rows of A equals the number of rows of B and the number of columns of A plus the number of columns of B equals the number of columns of C . This rule generalizes in a hopefully obvious way to allow fairly complicated constructions.  A = < > stores an empty matrix in A , thereby removing it from the list of current variables.  For the use of < and > on the left of the = in multiple assignment statements, see LU, EIG, SVD and so on.  In WHILE and IF clauses, <> means less than or greater than, i.e. not equal, < means less than, > means greater than, <= means less than or equal, >= means greater than or equal.  For the use of > and < to delineate macros, see MACRO.  > See < . Also see MACRO.  ( ( ) Used to indicate precedence in arithmetic expressions in the usual way. Used to enclose arguments of functions in the usual way. Used to enclose subscripts of vectors and matrices in a manner somewhat more general than the usual way. If X and V are vectors, then X(V) is <X(V(1)), X(V(2)), ..., X(V(N))> . The components of V are rounded to nearest integers and used as subscripts. An error occurs if any such subscript is less than 1 or greater than the dimension of X . Some examples: X(3) is the third element of X . X(<1 2 3>) is the first three elements of X . So is X(<SQRT(2), SQRT(3), 4*ATAN(1)>) . If X has N components, X(N:-1:1) reverses them. The same indirect subscripting is used in matrices. If V has M components and W has N components, then A(V,W) is the M by N matrix formed from the elements of A whose subscripts are the elements of V and W . For example... A(<1,5>,:) = A(<5,1>,:) interchanges rows 1 and 5 of A .  ) See ( .  = Used in assignment statements and to mean equality in WHILE and IF clauses.  . Decimal point. 314/100, 3.14 and .314E1 are all the same.  Element-by-element multiplicative operations are obtained using .* , ./ , or .\ . For example, C = A ./ B is the matrix with elements c(i,j) = a(i,j)/b(i,j) .  Kronecker tensor products and quotients are obtained with .*. , ./. and .\. . See KRON.  Two or more points at the end of the line indicate continuation. The total line length limit is 1024 characters.  , Used to separate matrix subscripts and function arguments. Used at the end of FOR, WHILE and IF clauses. Used to separate statements in multi-statement lines. In this situation, it may be replaced by semicolon to suppress printing.  ; Used inside brackets to end rows. Used after an expression or statement to suppress printing. See SEMI. \ Backslash or matrix left division. A\B is roughly the same as INV(A)*B , except it is computed in a different way. If A is an N by N matrix and B is a column vector with N components, or a matrix with several such columns, then X = A\B is the solution to the equation A*X = B computed by Gaussian elimination. A warning message is printed if A is badly scaled or nearly singular. A\EYE produces the inverse of A .  If A is an M by N matrix with M < or > N and B is a column vector with M components, or a matrix with several such columns, then X = A\B is the solution in the least squares sense to the under- or overdetermined system of equations A*X = B . The effective rank, K, of A is determined from the QR decomposition with pivoting. A solution X is computed which has at most K nonzero components per column. If K < N this will usually not be the same solution as PINV(A)*B . A\EYE produces a generalized inverse of A .  If A and B have the same dimensions, then A .\ B has elements a(i,j)\b(i,j) .  Also, see EDIT.  / Slash or matrix right division. B/A is roughly the same as B*INV(A) . More precisely, B/A = (A'\B')' . See \ .  IF A and B have the same dimensions, then A ./ B has elements a(i,j)/b(i,j) .  Two or more slashes together on a line indicate a logical end of line. Any following text is ignored.  ' Transpose. X' is the complex conjugate transpose of X . Quote. 'ANY TEXT' is a vector whose components are the MATLAB internal codes for the characters. A quote within the text is indicated by two quotes. See DISP and FILE .  + Addition. X + Y . X and Y must have the same dimensions.  - Subtraction. X - Y . X and Y must have the same dimensions.  * Matrix multiplication, X*Y . Any scalar (1 by 1 matrix) may multiply anything. Otherwise, the number of columns of X must equal the number of rows of Y .  Element-by-element multiplication is obtained with X .* Y .  The Kronecker tensor product is denoted by X .*. Y .  Powers. X**p is X to the p power. p must be a scalar. If X is a matrix, see FUN .  : Colon. Used in subscripts, FOR iterations and possibly elsewhere. J:K is the same as <J, J+1, ..., K> J:K is empty if J > K . J:I:K is the same as <J, J+I, J+2I, ..., K> J:I:K is empty if I > 0 and J > K or if I < 0 and J < K . The colon notation can be used to pick out selected rows, columns and elements of vectors and matrices. A(:) is all the elements of A, regarded as a single column. A(:,J) is the J-th column of A A(J:K) is A(J),A(J+1),...,A(K) A(:,J:K) is A(:,J),A(:,J+1),...,A(:,K) and so on. For the use of the colon in the FOR statement, See FOR .  ABS ABS(X) is the absolute value, or complex modulus, of the elements of X .  ANS Variable created automatically when expressions are not assigned to anything else.  ATAN ATAN(X) is the arctangent of X . See FUN .  BASE BASE(X,B) is a vector containing the base B representation of X . This is often used in conjunction with DISPLAY. DISPLAY(X,B) is the same as DISPLAY(BASE(X,B)). For example, DISP(4*ATAN(1),16) prints the hexadecimal representation of pi.  CHAR CHAR(K) requests an input line containing a single character to replace MATLAB character number K in the following table. For example, CHAR(45) replaces backslash. CHAR(-K) replaces the alternate character number K.  K character alternate name 0 - 9 0 - 9 0 - 9 digits 10 - 35 A - Z a - z letters 36 blank 37 ( ( lparen 38 ) ) rparen 39 ; ; semi 40 : | colon 41 + + plus 42 - - minus 43 * * star 44 / / slash 45 \$     backslash
46       =        =     equal
47       .        .     dot
48       ,        ,     comma
49       '        "     quote
50       <        [     less
51       >        ]     great
  CHOL  Cholesky factorization.  CHOL(X)  uses  only  the  diagonal
and upper triangle of  X .  The lower triangular is assumed
to be the (complex conjugate) transpose of the  upper.   If
X   is  positive  definite,  then  R = CHOL(X)  produces an
upper triangular  R  so that  R'*R = X .   If   X   is  not
positive definite, an error message is printed.
  CHOP  Truncate arithmetic.  CHOP(P) causes P places to be chopped
off   after   each   arithmetic   operation  in  subsequent
computations.  This means  P  hexadecimal  digits  on  some
computers  and  P octal digits on others.  CHOP(0) restores
full precision.
  CLEAR Erases all variables, except EPS, FLOP, EYE and RAND.
X = <>  erases only variable  X .  So does CLEAR X .
  COND  Condition number in 2-norm.  COND(X) is the  ratio  of  the
largest singular value of  X  to the smallest.
  CONJG CONJG(X)  is the complex conjugate of  X .
  COS   COS(X)  is the cosine of  X .  See FUN .
  DET   DET(X)  is the determinant of the square matrix  X .
  DIAG  If  V  is  a  row  or  column  vector  with  N  components,
DIAG(V,K)   is a square matrix of order  N+ABS(K)  with the
elements of  V  on the K-th diagonal.  K = 0  is  the  main
diagonal,  K  >  0  is above the main diagonal and K < 0 is
below the main diagonal.  DIAG(V)  simply puts  V   on  the
main diagonal.
eg. DIAG(-M:M) + DIAG(ONES(2*M,1),1) + DIAG(ONES(2*M,1),-1)
produces a tridiagonal matrix of order 2*M+1 .
IF  X  is a matrix,  DIAG(X,K)  is a column  vector  formed
from the elements of the K-th diagonal of  X .
DIAG(X)  is the main diagonal of  X .
DIAG(DIAG(X))  is a diagonal matrix .
  DIARY DIARY('file') causes a  copy  of  all  subsequent  terminal
input and most of the resulting output to be written on the
file. DIARY(0) turns it off.  See FILE.
  DISP  DISPLAY(X) prints X  in  a  compact  format.   If  all  the
elements  of  X  are  integers  between 0 and 51, then X is
interpreted  as  MATLAB  text  and   printed   accordingly.
Otherwise,  +  ,  -   and  blank  are printed for positive,
negative and zero elements.  Imaginary parts are ignored.
DISP(X,B) is the same as DISP(BASE(X,B)).
  EDIT  There  are  no   editing   features   available   on   most
installations and EDIT is not a command.  However, on a few
systems a command line consisting of a single  backslash  \
will  cause  the local file editor to be called with a copy
of the  previous  input  line.   When  the  editor  returns
control to MATLAB, it will execute the line again.
  EIG   Eigenvalues and eigenvectors.
EIG(X) is a vector containing the eigenvalues of  a  square
matrix  X .
<V,D>  =  EIG(X)   produces  a  diagonal  matrix    D    of
eigenvalues  and  a  full  matrix  V  whose columns are the
corresponding eigenvectors so that  X*V = V*D .
  ELSE  Used with IF .
  END   Terminates the scope  of  FOR,  WHILE  and  IF  statements.
Without  END's,  FOR  and WHILE repeat all statements up to
the end of the line.  Each END is paired with  the  closest
previous  unpaired FOR or WHILE and serves to terminate its
scope.  The line
FOR I=1:N, FOR J=1:N, A(I,J)=1/(I+J-1); A
would cause A to be printed  N**2  times, once for each new
element.  On the other hand, the line
FOR I=1:N, FOR J=1:N, A(I,J)=1/(I+J-1); END, END, A
will lead to only the final printing of  A .
Similar considerations apply to WHILE.
EXIT terminates execution of loops or of MATLAB itself.
  EPS   Floating point relative  accuracy.   A  permanent  variable
whose  value is initially the distance from 1.0 to the next
largest floating point number.  The  value  is  changed  by
CHOP,  and  other values may be assigned.  EPS is used as a
default tolerance by PINV and RANK.
  EXEC  EXEC('file',k) obtains  subsequent  MATLAB  input  from  an
external  file.  The printing of input is controlled by the
optional parameter k .
If k = 1 , the input is echoed.
If k = 2 , the MATLAB prompt <> is printed.
If k = 4 , MATLAB pauses before each prompt and waits for a
null line to continue.
If k = 0 , there is no echo, prompt or pause.  This is  the
default if the exec command is followed by a semicolon.
If k = 7 , there will be echos, prompts and pauses. This is
useful for demonstrations on video terminals.
If k = 3 , there will be echos and prompts, but no  pauses.
This is the the default if the exec command is not followed
by a semicolon.
EXEC(0) causes subsequent input to  be  obtained  from  the
terminal. An end-of-file has the same effect.
EXEC's may be nested, i.e. the text in the file may contain
EXEC of another file.  EXEC's may also be driven by FOR and
WHILE loops.
EXIT  Causes termination of a FOR or WHILE loop.
If not in a loop, terminates execution of MATLAB.
  EXP   EXP(X)  is the exponential of  X ,  e  to the X .  See  FUN
.
  EYE   Identity matrix.  EYE(N) is the N  by  N  identity  matrix.
EYE(M,N)   is an M by N matrix with 1's on the diagonal and
zeros elsewhere.  EYE(A)  is the same size  as   A  .   EYE
with  no  arguments is an identity matrix of whatever order
is appropriate in the context.   For  example,  A  +  3*EYE
adds  3  to each diagonal element of  A .
  FILE  The EXEC, SAVE, LOAD,  PRINT  and  DIARY  functions  access
files.   The  'file'  parameter  takes  different forms for
different operating systems.  On most systems,  'file'  may
be a string of up to 32 characters in quotes.  For example,
SAVE('A') or EXEC('matlab/demo.exec') .  The string will be
used as the name of a file in the local operating system.
On all systems, 'file' may be a positive integer   k   less
than  10  which  will  be  used  as  a FORTRAN logical unit
number. Some systems then automatically access a file  with
a  name  like  FORT.k  or FORk.DAT. Other systems require a
file with a name like FT0kF001 to be assigned  to  unit   k
before  MATLAB  is  executed. Check your local installation
for details.
  FLOPS Count of floating point operations.
FLOPS  is  a  permanently  defined  row  vector  with   two
elements.    FLOPS(1)  is  the  number  of  floating  point
operations counted during the previous statement.  FLOPS(2)
is  a  cumulative total.  FLOPS can be used in the same way
as any other vector.  FLOPS(2) = 0  resets  the  cumulative
total.   In  addition,  FLOPS(1) will be printed whenever a
statement is terminated by an extra comma.  For example,
X = INV(A);,
or
COND(A),   (as the last statement on the line).
HELP FLPS gives more details.
  FLPS  More detail on FLOPS.
It is not feasible to count absolutely all  floating  point
operations,  but  most  of  the important ones are counted.
Each multiply and add in a real vector operation such as  a
dot  product  or  a 'saxpy' counts one flop.  Each multiply
and add in a complex vector  operation  counts  two  flops.
Other additions, subtractions and multiplications count one
flop each if the result is real and two flops if it is not.
Real  divisions  count one and complex divisions count two.
Elementary functions count one if real and two if  complex.
Some examples.  If A and B are real N by N matrices, then
A + B  counts N**2 flops,
A*B    counts N**3 flops,
A**100 counts 99*N**3 flops,
LU(A)  counts roughly (1/3)*N**3 flops.
  FOR   Repeat statements a specific number of times.
FOR variable = expr, statement, ..., statement, END
The END at the end of a line may  be  omitted.   The  comma
before  the  END  may  also be omitted.  The columns of the
expression are stored one at a time  in  the  variable  and
then the following statements, up to the END, are executed.
The expression is often of the form X:Y, in which case  its
columns  are  simply  scalars.  Some examples (assume N has
FOR I = 1:N, FOR J = 1:N, A(I,J) = 1/(I+J-1);
FOR J = 2:N-1, A(J,J) = J; END; A
FOR S = 1.0: -0.1: 0.0, ...  steps S with increments of -0.1 .
FOR E = EYE(N), ...   sets  E  to the unit N-vectors.
FOR V = A, ...   has the same effect as
FOR J = 1:N, V = A(:,J); ...  except J is also set here.
  FUN   For matrix arguments  X , the  functions  SIN,  COS,  ATAN,
SQRT,  LOG,  EXP and X**p are computed using eigenvalues  D
and eigenvectors  V .  If  <V,D> =  EIG(X)   then   f(X)  =
V*f(D)/V  .   This method may give inaccurate results if  V
is badly conditioned.  Some idea of  the  accuracy  can  be
obtained by comparing  X**1  with  X .
For vector arguments,  the  function  is  applied  to  each
component.
  HESS  Hessenberg form.  The Hessenberg form of a matrix  is  zero
below the first subdiagonal.  If the matrix is symmetric or
Hermitian,  the  form  is  tridiagonal.   <P,H>  =  HESS(A)
produces  a  unitary  matrix P and a Hessenberg matrix H so
that A = P*H*P'.  By itself, HESS(A) returns H.
  HILB  Inverse Hilbert matrix.  HILB(N)  is the inverse of  the  N
by  N   matrix  with elements  1/(i+j-1), which is a famous
example of a badly conditioned matrix.  The result is exact
for  N  less than about 15, depending upon the computer.
  IF    Conditionally execute statements.  Simple form...
IF expression rop expression, statements
where rop is =, <, >, <=, >=, or  <>  (not  equal)  .   The
statements  are  executed  once if the indicated comparison
between the real parts of the first components of  the  two
expressions  is true, otherwise the statements are skipped.
Example.
IF ABS(I-J) = 1, A(I,J) = -1;
More complicated forms use END in the same way it  is  used
with FOR and WHILE and use ELSE as an abbreviation for END,
IF expression not rop expression .  Example
FOR I = 1:N, FOR J = 1:N, ...
IF I = J, A(I,J) = 2; ELSE IF ABS(I-J) = 1, A(I,J) = -1; ...
ELSE A(I,J) = 0;
An easier way to accomplish the same thing is
A = 2*EYE(N);
FOR I = 1:N-1, A(I,I+1) = -1; A(I+1,I) = -1;
  IMAG  IMAG(X)  is the imaginary part of  X .
  INV   INV(X)  is the inverse of the square matrix  X .  A warning
message  is  printed  if   X   is  badly  scaled  or nearly
singular.
  KRON  KRON(X,Y) is the Kronecker tensor product of X and Y  .  It
is  also  denoted by X .*. Y . The result is a large matrix
formed by taking all possible products between the elements
of  X  and  those  of Y . For example, if X is 2 by 3, then
X .*. Y is
              < x(1,1)*Y  x(1,2)*Y  x(1,3)*Y
x(2,1)*Y  x(2,2)*Y  x(2,3)*Y >
        The five-point discrete Laplacian for an n-by-n grid can be
generated by
              T = diag(ones(n-1,1),1);  T = T + T';  I = EYE(T);
A = T.*.I + I.*.T - 4*EYE;
        Just  in  case  they  might  be  useful,  MATLAB   includes
constructions called Kronecker tensor quotients, denoted by
X ./. Y and X .\. Y .  They are obtained by  replacing  the
elementwise multiplications in X .*. Y with divisions.
  LINES An internal count is kept of the number of lines of  output
since  the  last  input.   Whenever this count approaches a
limit, the  user  is  asked  whether  or  not  to  suppress
printing  until the next input.  Initially the limit is 25.
LINES(N) resets the limit to N .
  LOAD  LOAD('file') retrieves all the variables from  the  file  .
See  FILE  and  SAVE for more details.  To prepare your own
given under SAVE.
  LOG   LOG(X)  is the  natural  logarithm  of   X  .   See  FUN  .
Complex results are produced if  X  is not positive, or has
nonpositive eigenvalues.
  LONG  Determine output format.   All  computations  are  done  in
complex arithmetic and double precision if it is available.
SHORT and  LONG  merely  switch  between  different  output
formats.
SHORT    Scaled fixed point format with about 5 digits.
LONG     Scaled fixed point format with about 15 digits.
SHORT E  Floating point format with about 5 digits.
LONG E   Floating point format with about 15 digits.
LONG Z   System dependent format, often hexadecimal.
  LU    Factors from Gaussian elimination.  <L,U> = LU(X)  stores a
upper triangular matrix in  U  and a 'psychologically lower
triangular matrix', i.e. a product of lower triangular  and
permutation matrices, in L , so that  X = L*U .  By itself,
LU(X) returns the output from CGEFA .
  MACRO The macro facility involves text and inward pointing  angle
brackets.  If  STRING  is  the  source  text for any MATLAB
expression or statement, then
t = 'STRING';
encodes the text as a vector of integers  and  stores  that
vector in  t .  DISP(t) will print the text and
>t<
causes the text to be interpreted, either as a statement or
as a factor in an expression.  For example
t = '1/(i+j-1)';
disp(t)
for i = 1:n, for j = 1:n, a(i,j) = >t<;
generates the Hilbert matrix of order n.
Another example showing indexed text,
S = <'x = 3            '
'y = 4            '
'z = sqrt(x*x+y*y)'>
for k = 1:3, >S(k,:)<
It is necessary that the strings making up  the  "rows"  of
the "matrix"  S  have the same lengths.
  MAGIC Magic square.  MAGIC(N) is an N  by  N  matrix  constructed
from  the integers 1 through N**2 with equal row and column
sums.
  NORM  For matrices..
NORM(X)  is the largest singular value of  X .
NORM(X,1)  is the 1-norm of  X .
NORM(X,2)  is the same as NORM(X) .
NORM(X,'INF')  is the infinity norm of  X .
NORM(X,'FRO')  is the F-norm, i.e.  SQRT(SUM(DIAG(X'*X))) .
For vectors..
NORM(V,P) = (SUM(V(I)**P))**(1/P) .
NORM(V) = NORM(V,2) .
NORM(V,'INF') = MAX(ABS(V(I))) .
  ONES  All ones.  ONES(N)  is an N by N matrix of ones.  ONES(M,N)
is an M by N matrix of ones .  ONES(A)  is the same size as
A  and all ones .
  ORTH  Orthogonalization.   Q  =  ORTH(X)   is   a   matrix   with
orthonormal  columns,  i.e. Q'*Q = EYE, which span the same
space as the columns of  X .
  PINV  Pseudoinverse.  X = PINV(A) produces a matrix   X   of  the
same  dimensions as  A' so that  A*X*A = A , X*A*X = X  and
AX  and  XA  are Hermitian .  The computation is  based  on
SVD(A)  and  any  singular values less than a tolerance are
treated   as    zero.     The    default    tolerance    is
NORM(SIZE(A),'inf')*NORM(A)*EPS.   This  tolerance  may  be
overridden with X = PINV(A,tol).  See RANK.
  PLOT  PLOT(X,Y) produces a plot of  the  elements  of  Y  against
those  of X . PLOT(Y) is the same as PLOT(1:n,Y) where n is
the  number  of   elements   in   Y   .    PLOT(X,Y,P)   or
PLOT(X,Y,p1,...,pk)  passes the optional parameter vector P
or scalars p1 through pk to the plot routine.  The  default
plot  routine  is a crude printer-plot. It is hoped that an
interface to local graphics equipment can be provided.
An interesting example is
t = 0:50;
PLOT( t.*cos(t), t.*sin(t) )
  POLY  Characteristic polynomial.
If  A  is an N by N matrix, POLY(A) is a column vector with
N+1   elements   which   are   the   coefficients   of  the
characteristic polynomial,  DET(lambda*EYE - A) .
If V is a vector, POLY(V) is a vector  whose  elements  are
the  coefficients  of  the  polynomial  whose roots are the
elements of V .  For vectors, ROOTS and  POLY  are  inverse
functions  of  each  other,  up  to  ordering, scaling, and
roundoff error.
ROOTS(POLY(1:20)) generates Wilkinson's famous example.
  PRINT PRINT('file',X) prints X on  the  file  using  the  current
format determined by SHORT, LONG Z, etc.  See FILE.
  PROD  PROD(X)  is the product of all the elements of  X .
  QR    Orthogonal-triangular decomposition.
<Q,R> = QR(X)  produces an upper triangular  matrix   R  of
the  same dimension as  X  and a unitary matrix  Q  so that
X = Q*R .
<Q,R,E> = QR(X)  produces a  permutation  matrix   E  ,  an
upper  triangular  R  with decreasing diagonal elements and
a unitary  Q  so that  X*E = Q*R .
By itself, QR(X) returns the output of CQRDC .  TRIU(QR(X))
is R .
  RAND  Random numbers and matrices.  RAND(N)  is an N by N  matrix
with  random  entries.  RAND(M,N)  is an M by N matrix with
random entries.  RAND(A)  is the same size as   A  .   RAND
with no arguments is a scalar whose value changes each time
it is referenced.
Ordinarily,  random numbers are  uniformly  distributed  in
the  interval  (0.0,1.0)  .   RAND('NORMAL')  switches to a
normal distribution  with  mean  0.0  and  variance  1.0  .
RAND('UNIFORM')  switches back to the uniform distribution.
RAND('SEED') returns the current value of the seed for  the
generator.    RAND('SEED',n)   sets   the   seed   to  n  .
RAND('SEED',0) resets the seed to 0, its value when  MATLAB
is first entered.
  RANK  Rank.  K = RANK(X) is the number of singular values  of   X
that are larger than NORM(SIZE(X),'inf')*NORM(X)*EPS.
K = RANK(X,tol) is the number of singular values of  X that
are larger than tol .
  RCOND RCOND(X)   is  an  estimate  for  the  reciprocal  of   the
condition  of   X   in  the  1-norm obtained by the LINPACK
condition estimator.  If  X  is well conditioned,  RCOND(X)
is  near  1.0  .   If  X  is badly conditioned, RCOND(X) is
near 0.0 .
<R, Z> = RCOND(A) sets  R  to RCOND(A) and also produces  a
vector  Z so that
NORM(A*Z,1) = R*NORM(A,1)*NORM(Z,1)
So, if RCOND(A) is small, then  Z  is an  approximate  null
vector.
  RAT   An experimental  function  which  attempts  to  remove  the
roundoff   error  from  results  that  should  be  "simple"
rational numbers.
RAT(X) approximates each  element  of   X  by  a  continued
fraction of the form
                  a/b = d1 + 1/(d2 + 1/(d3 + ... + 1/dk))
        with k <= len, integer di and abs(di) <= max .  The default
values of the parameters are len = 5 and max = 100.
RAT(len,max) changes the default values.  Increasing either
len or max increases the number of possible fractions.
<A,B> = RAT(X) produces integer matrices A and B so that
                  A ./ B  =  RAT(X)
        Some examples:
              long
T = hilb(6), X = inv(T)
<A,B> = rat(X)
H = A ./ B, S = inv(H)
              short e
d = 1:8,  e = ones(d),  A = abs(d'*e - e'*d)
X = inv(A)
rat(X)
display(ans)
  REAL  REAL(X)  is the real part of  X .
  RETURN  From the terminal, causes return to the operating  system
or  other  program  which  invoked  MATLAB.  From inside an
EXEC, causes  return  to  the  invoking  EXEC,  or  to  the
terminal.
  RREF  RREF(A) is the reduced row echelon form of the  rectangular
matrix.  RREF(A,B) is the same as RREF(<A,B>) .
  ROOTS Find polynomial roots.  ROOTS(C)  computes the roots of the
polynomial  whose  coefficients  are  the  elements  of the
vector  C .  If  C  has  N+1  components, the polynomial is
C(1)*X**N + ... + C(N)*X + C(N+1) .  See POLY.
  ROUND ROUND(X)  rounds  the  elements  of   X   to  the   nearest
integers.
  SAVE  SAVE('file') stores all the current variables in a file.
SAVE('file',X) saves only X .  See FILE .
The variables may be retrieved later by LOAD('file') or  by
your  own program using the following code for each matrix.
The lines involving XIMAG may be eliminated  if  everything
is known to be real.
              attach lunit to 'file'
REAL or DOUBLE PRECISION XREAL(MMAX,NMAX)
REAL or DOUBLE PRECISION XIMAG(MMAX,NMAX)
DO 10 J = 1, N
IF (IMG .NE. 0) READ(lunit,102) (XIMAG(I,J),I=1,M)
10 CONTINUE
        The formats used are system dependent.  The  following  are
typical.     See    SUBROUTINE   SAVLOD   in   your   local
implementation of MATLAB.
          101 FORMAT(4A1,3I4)
102 FORMAT(4Z18)
102 FORMAT(4O20)
102 FORMAT(4D25.18)
  SCHUR Schur decomposition.  <U,T> = SCHUR(X)  produces  an  upper
triangular  matrix   T , with the eigenvalues of  X  on the
diagonal, and a unitary matrix  U so that  X =  U*T*U'  and
U'*U = EYE .  By itself, SCHUR(X) returns  T .
  SHORT See LONG .
  SEMI  Semicolons at the end of  lines  will  cause,  rather  than
suppress,  printing.   A  second  SEMI restores the initial
interpretation.
  SIN   SIN(X)  is the sine of  X .  See FUN .
SIZE  If X is an M by N matrix, then SIZE(X) is <M, N> .
Can also be used with a multiple assignment,
<M, N> = SIZE(X) .
  SQRT  SQRT(X)  is the square root of  X .   See  FUN  .   Complex
results  are  produced  if   X   is  not  positive,  or has
nonpositive eigenvalues.
  STOP  Use EXIT instead.
  SUM   SUM(X)   is  the  sum  of  all  the  elements   of    X   .
SUM(DIAG(X))  is the trace of  X .
  SVD   Singular value decomposition.  <U,S,V> = SVD(X)  produces a
diagonal  matrix  S , of the same dimension as  X  and with
nonnegative diagonal  elements  in  decreasing  order,  and
unitary matrices  U  and  V  so that  X = U*S*V' .
By itself, SVD(X) returns a vector containing the  singular
values.
<U,S,V>   =   SVD(X,0)   produces   the   "economy    size"
decomposition.   If  X  is m by n with m > n, then only the
first n columns of U are computed and S is n by n .
  TRIL  Lower triangle.  TRIL(X) is the lower triangular part of X.
TRIL(X,K) is the elements on and below the K-th diagonal of
X.  K = 0 is the main diagonal, K > 0  is  above  the  main
diagonal and K < 0 is below the main diagonal.
  TRIU  Upper triangle.  TRIU(X) is the upper triangular part of X.
TRIU(X,K) is the elements on and above the K-th diagonal of
X.  K = 0 is the main diagonal, K > 0  is  above  the  main
diagonal and K < 0 is below the main diagonal.
  USER  Allows personal  Fortran  subroutines  to  be  linked  into
MATLAB .  The subroutine should have the heading
                 SUBROUTINE USER(A,M,N,S,T)
REAL or DOUBLE PRECISION A(M,N),S,T
        The MATLAB statement  Y = USER(X,s,t)  results in a call to
the  subroutine with a copy of the matrix  X  stored in the
argument  A , its column and row dimensions in  M  and  N ,
and  the scalar parameters  s  and  t  stored in  S  and  T
. If  s and t  are omitted, they are set to  0.0  .   After
the  return,   A  is stored in  Y .  The dimensions  M  and
N  may be reset within the subroutine.  The statement  Y  =
USER(K)  results in a call with M = 1, N = 1  and  A(1,1) =
FLOAT(K) .  After the subroutine has been written, it  must
be compiled and linked to the MATLAB object code within the
local operating system.
  WHAT  Lists commands and functions currently available.
  WHILE Repeat statements an indefinite number of times.
WHILE expr rop expr, statement, ..., statement, END
where rop is =, <, >, <=, >=, or <> (not equal) .  The  END
at  the end of a line may be omitted.  The comma before the
END may also be omitted.  The commas  may  be  replaced  by
semicolons   to   avoid   printing.    The  statements  are
repeatedly executed as long  as  the  indicated  comparison
between  the  real parts of the first components of the two
expressions is true.   Example  (assume  a  matrix   A   is
E = 0*A; F = E + EYE; N = 1;
WHILE NORM(E+F-E,1) > 0, E = E + F; F = A*F/N; N = N + 1;
E
  WHO   Lists current variables.
  WHY   Provides succinct answers to any questions.
  //

Published with MATLAB® R2018a

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