# Symbolic Toolbox and Related Functions

The Symbolic Math Toolkit is a Mathworks package that augments Matlab's existing functionality with the core Maple® symbolic kernel. With this package, you can solve and simplify systems of symbolic equations, find symbolic expressions for the inverse of a function, integrate, differentiate, take limits, and perform Taylor expansions, sums, variable precision arithmetic, or linear algebraic operations. If you have the extended symbolic toolkit, (which we do not discuss here), all of Maple's non-graphics packages are available.

## Contents

There is more to this package than we can describe here. For more information, see the online documentation.

## Working with Symbolic Variables

Symbolic variables are treated differently than regular variables in Matlab and must be created using the sym() or syms() functions.

syms A B lambda X Y Z a


Constant symbols can be defined too, which are not evaluated numerically.

r = sym(sqrt(2)/2)
rr = r^2
t = sym(2/3)                        % Need to use sym here, not syms
v = r + t
w = r*2-3/t                         % notice that r is not evaluated
q = sym(22/14 + 18/402)             % add two fractions exactly

r =
2^(1/2)/2
rr =
1/2
t =
2/3
v =
2^(1/2)/2 + 2/3
w =
2^(1/2) - 9/2
q =
758/469


To convert a constant symbolic expression to a regular Matlab double value, use the double() function.

double(r)                           % numerically evaluate r

ans =
0.7071


We can build up more complicated symbolic expressions by defining functions of these variables. Note that these too are symbolic expressions, not function handles.

f = cos(X)
g = exp(X^2 - 2*X)
h = compose(f,g)                   % functional composition: f(g(X))

f =
cos(X)
g =
exp(X^2 - 2*X)
h =
cos(exp(X^2 - 2*X))


Functions of multiple variables can also be created.

f = X^2 + Y^2 - 2*cos(X)
g = sqrt(X^2 + Y^2) + Z^2 - lambda

f =
X^2 - 2*cos(X) + Y^2
g =
(X^2 + Y^2)^(1/2) - lambda + Z^2


The pretty() function tries to display a symbolic expression in a prettier way. It takes a bit of getting used to. Exponents, for example are printed on the line above, trying to mimic how you might write them by hand

pretty(g)


2    2 1/2             2
(X  + Y )    - lambda + Z


You can convert an expression to latex as follows

latex(g)

ans =
\sqrt{X^2 + Y^2} - \mathrm{lambda} + Z^2


You can convert an expression to C code as follows

ccode(g)

ans =
t0 = -lambda+sqrt(X*X+Y*Y)+Z*Z;


Symbolic matrices are created in much the same way numeric matrices are.

syms E F G H I J
mat = [E F G ; H I J]

mat =
[ E, F, G]
[ H, I, J]


We will make use of these in the sections to come.

The subs() function can be used to substitute one value for another, including a numeric value for symbolic one.

subs(f,X,3)             % substitute 3 for X in f

Error using ==> sym.sym>notimplemented at 2621
Function 'subsindex' is not implemented for MuPAD symbolic objects.

Error in ==> sym.sym>sym.subsindex at 1359
notimplemented('subsindex');

Error in ==> symbolic at 66
subs(f,X,3)             % substitute 3 for X in f


Once all of the symbolic variables are numeric, the result is numerically evaluated. We can prevent this by substituting sym(3) and sym(10) instead.

subs(f,{X,Y},{3,10})    % substitute 3 for X, 10 for Y in f

subs(f,X,lambda-Y)      % substitute (lambda - Y) for X in f


Many operations on symbolic expressions are ambiguous unless the independent variable is specified. If this is not given explicitly, Matlab chooses the variable closest in alphabetical order to x, (ties broken in favor of the end of the alphabet). You can see how Matlab will order the variables in an expression with the findsym() function.

findsym(f)


## Basic Algebra

There are a number of functions that we can use to perform basic high school algebra that might be tedious or error prone to do by hand. We have already seen compose(), which belongs on this list.

S = (((2*X + 8*X^2)/(2*X)+(2*X^2 - 2*X + X)*(X+2)-(2*X^2 + 2)/(4*X^2 - 2*X^2)*(X+2))/(X+1))+1;
pretty(S);


We can then factor or expand S.

Sf = factor(S);                  % factor S
pretty(Sf);

Se = expand(S);                  % expand s
pretty(Se);


The simple() and simplify() functions can be used to try and find the simplest representation: simple tends to do better with trigonometric expressions. Surprisingly, we can sometimes get simpler expressions by applying the function multiple times.

T = 1/2*(2*tan(1/2*X)/(1+tan(1/2*X)^2)              *...
(1-tan(1/2*Y)^2)/(1+tan(1/2*Y)^2)-2             *...
(1-tan(1/2*X)^2)/(1+tan(1/2*X)^2)               *...
tan(1/2*Y)/(1+tan(1/2*Y)^2))/tan(1/2*X-1/2*Y)   *...
(1+tan(1/2*X-1/2*Y)^2);
Tsimple = simple(T)
TverySimple = simple(simple(T))


If you call simple() without saving the output, i.e. just simple(T) as opposed to t = simple(T), it displays many equivalent expressions.

The collect() function can be used to collect like terms. Here we collect all of the Y variables together and find we have (3X - 3Z) of them

t = (X + Y)*(2*X-3*Z)+Z+X*Y-(3*X+4);
pretty(t);

t = collect(t,Y);
pretty(t);


## Function Inverse

We can find the inverse of a function, (if it exists) with finverse().

f = 2*sin(cos(3*log(X)/4))+1
finv = finverse(f)


## Solving Symbolic Equations

The solve() function can be used to solve systems of equations, symbolically. Its inputs are either symbolic expressions or strings, with each equation separated by a comma followed by the variable or variables you wish to solve for. The output is a struct with a field for each variable.

S = solve('k = a/(a-b+p)','p = k*(1+k)^2/(2*a + b+ 1)','a','b')

pretty(S.a)

pretty(S.b)


If the equations do not contain an equals sign, they are assumed equal to 0.

pretty(solve('a*X^2+b*X+c','X'))

S = solve('X = 2*Y-1','Y=3*Z','Z=X+Y','X','Y','Z');
S.X
S.Y
S.Z


## Limits

We can take limits of functions, (two-sided, as well as left and right) as they approach specific values, (symbolic or numeric) or as they tend to inf or -inf.

limit((X-1)/(sqrt(X)-1),1)              % limit as X --> 2
limit(X^X/(exp(X)^log(X)),inf)          % limit as X --> inf
limit(cos(X)-2*3^X,X,a)                 % limit as X --> a
limit(cos(X*Y)^X-2*3^Y,Y,0)             % limit as Y --> 0
limit(exp(Y-X),Y,-inf)                  % limit as Y --> -inf

f = 1/(1 + 2^(-1/X));
limit(f,0)                              % two sided limit as X --> 0 (D.N.E.)
limit(f,X,0,'left')                     % left  sided limit: X-->0-
limit(f,X,0,'right')                    % right sided limit: X-->0+


## Symbolic Sums

The symsum() function performs symbolic sums. We specify the summand w.r.t. the indexing variable, followed by the start and end indices. The end index can be inf, in which case an infinite sum is performed.

symsum(2^(-X),0,inf)
symsum((-1)^(X+1)*(1/X),1,inf)
symsum(1/(X^2),1,inf)
symsum((-1)^(X+1)*(1/(2*X-1)),1,inf)
symsum(1/X,1,10)


## Taylor Series Expansions

We can perform a Taylor expansion of a function. We specify the function, and optionally, the degree of the expansion, (default is 6), and a symbol or numeric value about which the expansion is performed.

taylor(sin(X))                      % 6th order Taylor expansion of sin(X)
taylor(exp(X))                      % 6th order Taylor expansion of exp(X)

syms A
pretty(taylor(sin(X),10,A))         % 10th order expansion about the point A

pretty(taylor(int(normpdf(X)),10)); % 10th order expansion of the integral of the normal distribution


## Differentiation

The diff() function performs differentiation.

diff(log(X))               % differentiate with respect to X
diff(log(X),3)             % take the 3rd derivative of log(X) w.r.t. X
diff(Y*sin(X)+2*X^Y,'Y')   % differentiate w.r.t Y


We can combine diff() with solve() to find maxima and minima

solve((diff(sqrt(log(3*X^3-2*X)))))


Here we attempt to find the MLE for the product of N univariate Gaussians. Unfortunately, we have to specify a value for N and proceed via induction.

syms mu s2 pi
normconst = sqrt(2*pi*s2);                                % s2 for sigma^2
NormPDF = (1/normconst)*exp((-(X-mu)^2)/(2*s2));          % single Gaussian distribution
prodNorm = 1;
for i=1:N
syms(['X',num2str(i)]);
prodNorm =  prodNorm*subs(NormPDF,X,['X',num2str(i)]); % take product of N Gaussians
end
muMLE = solve(diff(prodNorm,mu),mu);                      % MLE w.r.t. mu
s2MLE = solve(diff(prodNorm,s2),s2);                      % MLE w.r.t. sigma^2
pretty(muMLE)


We can easily spot the pattern: (1/N)sum(X)

Unfortunately, s2MLE is not quite as pretty.

pretty(s2MLE)


## Integration

The int() function can perform definite and indefinite integration.

int(log(X))                         %indefinite integral w.r.t X
int(sin(X)^2)                       %indefinite integral w.r.t X
int(int(log(X+Y),'X'),'Y')          %indefinite integral w.r.t Y

result = int(2*X*log(X),1,10)       % definite integral
double(result)                      % convert exact to a double


## Multivariate Calculus

Here we take the Jacobian of f with respect to v, where both are vectors.

syms r theta
v = [r theta];
x1 = r*cos(theta);
x2 = r*sin(theta);
f = [x1,x2];
J = jacobian(f,v)


We can calculate the determinant exactly.

detJ = (det(J))


Lets simplify the above expression.

detJ = simplify(detJ)


We can also use this function to compute a gradient, so long as our function f is scalar valued.

syms X Y Z
f = 2*sin(X) + cos(Y)^2 -3*log(Z)
J = jacobian(f,[X,Y,Z]);
pretty(J);


## Linear Algebra

We can create symbolic matrices as we saw above, and perform various operations. Constant symbols can be used to perform computations exactly.

clear
syms a b c d e f g h i j k l m n o p
A = [a b ; c d ; e f];
B = [g h i ; j k l];
C = A*B

D = [a b ; c d];
inv(D)

det(D)

eig(D)

E = sym([1/3 4/15 2/5 ; 2/3 1/3 0; 1/2 1/4 1/4])
E2 = E^2

det(E)

inv(E)


Here we try and compute the SVD exactly but to no avail. We can find a rational approximation to the entries, however, by using the rats() function, which we discuss in more depth below.

[U S V] = svd(E)               % compute the SVD of E
U = rats(double(U))
S = rats(double(S))
V = rats(double(V))


Symbolic and non-symbolic expressions can be combined.

[a b ; c d]*[2 0 ; 3 5]


Here we test the correctness of the Matrix Inversion Lemma. We define A,B,C, and D to be arbitrary square matrices. Unfortunately, their size is fixed and so the test is not perfectly general.

Keep in mind that two symbolic equations are only considered equal if Matlab is able to find identical representations of each. Its searches are not exhaustive and as such, it might claim that two expressions are unequal, when in fact they are, but not conversely.

syms a b c d e f g h i j k l m n o p
A = [ a b ; c d]; B= [e f ; g h];
C = [ i j ; k l]; D = [m n ; o p];

lhs = inv([A B ; C D]);

rhs11 = inv(A) + inv(A)*B*inv(D-C*inv(A)*B)*C*inv(A);
rhs12 = -inv(A)*B*inv(D - C*inv(A)*B);
rhs21 = -inv(D-C*inv(A)*B)*C*inv(A);
rhs22 = inv(D-C*inv(A)*B);
rhs = [rhs11 rhs12 ; rhs21 rhs22];

test = simplify(lhs) == simplify(rhs)


## Variable Precision Arithmetic

The vpa() function lets us perform variable precision arithmetic. The default is 32 digits, which can be changed for the current session with the digits() function.

PI = vpa(pi,80)                         % pi to 80 digits
s = vpa(sin(sqrt(2)/2),40)              % calculate to 40 digits


## Rational Fraction Approximation

As the title suggests, we an approximate any real number as a rational fraction, (whose numerator and denominator are relatively prime). This is actually a Matlab function, (not from the symbolic toolbox).

q = rats((22/14) + (18/402))
e = rats(exp(1))
simplified = rats(940/40)
val = rats(9.352422118484)

clear