Consider the symbolic matlab expression
e = (a_1_1 + a_2_2)*(b_1_1 + b_2_2)
Using latex(e) this yields
\left({{a_{1}}}_{1} + {{a_{2}}}_{2}\right)\, \left({{b_{1}}}_{1} + {{b_{2}}}_{2}\right)
Is it possible to [somehow] use comma as separator between the indices, i.e. to get
\left(a_{1,1} + a_{2,2} \right)\,\left(b_{1,1} + b_{2,2}\right)
I'd be interested in an easy way to do it. An ugly way would be:
eqn = latex(e);
eqn1 = regexprep(eqn,'{{','');
eqn2 = regexprep(eqn1,'}}}_{',',');
Related
I'm looking for finding the coefficients from the Taylor's series in Matlab. The way that I'm doing is:
% Declare symbolic expression and function:
syms x;
f = exp(x);
% Calculate the taylor expansions in a concrete point:
T = taylor(f, x, 0.5);
% And finally I simplify the expression:
coefs = simplify(T)
But the returned expression is:
Yes, the expression it's simplified, but actually what I want is:
Where each term is multiplied by his coefficient. How can I this? Options suchs as simplify(f, x, 0.5, 10, where 10 refers to the simplification step,doesn't work in my case. Also I've been seeing this question and the problem is the same:
How get to simplify a symbolic and numeric mixed expression in Matlab
Depending on how many digits you want and in what exact format you want the result, here is an example:
>> c = double(coeffs(T))
c =
Columns 1 through 4
0.999966624859531 1.000395979357109 0.498051217190664 0.171741799031263
Columns 5 through 6
0.034348359806253 0.013739343922501
>> digits 15
>> x.^(0:numel(c)-1) * sym(c,'d').'
ans =
0.0137393439225011*x^5 + 0.0343483598062527*x^4 + 0.171741799031263*x^3 + 0.498051217190664*x^2 + 1.00039597935711*x + 0.999966624859531
EDIT
Note that you could also use vpa( ) instead of converting to double first:
>> c = coeffs(T)
c =
[ (2329*exp(1/2))/3840, (233*exp(1/2))/384, (29*exp(1/2))/96, (5*exp(1/2))/48, exp(1/2)/48, exp(1/2)/120]
>> x.^(0:numel(c)-1) * vpa(c).'
ans =
0.0137393439225011*x^5 + 0.0343483598062527*x^4 + 0.171741799031263*x^3 + 0.498051217190664*x^2 + 1.00039597935711*x + 0.999966624859531
I have an anonymous function with 10 variables
now I want to evaluate it with data in a p=1x10 matrix like this:
answer=func(p(1),p(2),p(3),p(4),p(5),p(6),p(7),p(8),p(9),p(10))
i don't want to use this, i need something like:
answer=func(p(:))
but it generates error
can anyone give me a solution?
You seem to have some basic misunderstandings using anonymous functions and its syntax.
For what I think you want to do, you basically have three options:
Option 1
Define the function with 10 input parameters and provide 10 input values - OR expand an input array as comma separated list using {:} which requires an intermediate num2cell step:
func1 = #(a,b,c,d,e,f,g,h,i,j) a + b + c + d + e + f + g + h + i + j
p = num2cell(p)
answer = func1(p{:})
Option 2
Define the function with 1 input parameters using an array with 10 values and provide this array:
func2 = #(p) p(1) + p(2) + p(3) + p(4) + p(5) + p(6) + p(7) + p(8) + p(9) + p(10)
answer = func2(p)
Option 3
The last option to use varargin is really case dependent and could look like:
func3 = #(varargin) [varargin{:}]
p = num2cell(p)
answer = func3(p{:})
I have a code that produces a vector in MATLAB, for example the following is a three component vector (n=3):
a1_1 - sin((17*a1_1)/60 + a2_1/8 + a3_1/40 - 0.153233)
(15*a1_1)/16 + a2_1/4 + a3_1/32 - sin((17*a1_1)/60 + a2_1/8 + a3_1/40 - 0.0282326)
(3*a1_1)/4 + a2_1/2 + a3_1/8 - sin((17*a1_1)/60 + a2_1/8 + a3_1/40 + 0.846767)
as you can see each component is a non-linear equation. The three component of the vector forms a system of three non-linear equations having it's variables named as a1_1, a1_2and a1_3. I want to solve this system by fsolve.
How do I do that for arbitrary n?
To use fsolve, your function must accept a vector input and return a vector of the same size. In your case you can accomplish this with an anonymous function:
f = #(a)[a(1) - sin(17*a(1)/60 + a(2)/8 + a(3)/40 - 0.153233);...
15*a(1)/16 + a(2)/4 + a(3)/32 - sin(17*a(1)/60 + a(2)/8 + a(3)/40 - 0.0282326);...
3*a(1)/4 + a(2)/2 + a(3)/8 - sin(17*a(1)/60 + a(2)/8 + a(3)/40 + 0.846767)];
n = 3;
a0 = zeros(n,1); % Initial guess
opts = optimoptions('fsolve','Display','iter','TolFun',1e-8);
[a_sol,a_val,exitflag] = fsolve(f,a0,opts)
This returns
a_sol =
-0.002818738864459
-0.687953796565011
9.488284986072076
Of course there may be more than one solution, especially for larger n. You can choose your initial guess to find the others. See the documentation for fsolve and optimoptions for further details on on specifying options.
Did you try using the solve command ?
[y1,...,yN] = solve(eqns,vars) solves the system of equations eqns for the variables vars. The solutions are assigned to the variables y1,...,yN. If you do not specify the variables, solve uses symvar to find the variables to solve for. In this case, the number of variables that symvar finds is equal to the number of output arguments N.
I am building (my first...) MatLab program, it needs to differentiate an equations symbolically and then use this solution many many times (with different numeric inputs).
I do not want it to recalculate the symbolic differentiation every time it needs to put in a new set of numeric values. This would probably greatly add to the time taken to run this program (which - given its nature, a numeric optimiser, will probably already be hours).
My question is how can I structure my program such that it will not recalculate the symbolic differentiation?
The class in question is:
function [ result ] = GradOmega(numX, numY, numZ, numMu)
syms x y z mu
omega = 0.5*(x^2+y^2+z^2) + (1-mu)/((x+mu)^2+y^2+z^2)^0.5 + mu/((x+mu-1)^2+y^2+z^2)^0.5;
symGradient = gradient(omega);
%//Substitute the given numeric values back into the funtion
result = subs(symGradient, {x,y,z,mu}, {numX, numY, numZ, numMu});
end
I know that I could just symbolically calculate the derivative and then copy-paste it into the code e.g.
gradX = x + ((2*mu + 2*x)*(mu - 1))/(2*((mu + x)^2 + y^2 + z^2)^(3/2)) - (mu*(2*mu + 2*x - 2))/(2*((mu + x - 1)^2 + y^2 + z^2)^(3/2));
gradY = y - (mu*y)/((mu + x - 1)^2 + y^2 + z^2)^(3/2) + (y*(mu - 1))/((mu + x)^2 + y^2 + z^2)^(3/2);
gradZ = z - (mu*z)/((mu + x - 1)^2 + y^2 + z^2)^(3/2) + (z*(mu - 1))/((mu + x)^2 + y^2 + z^2)^(3/2);
But then my code is a bit cryptic, which is a problem in a shared project.
There is a related query here: http://uk.mathworks.com/matlabcentral/answers/53542-oop-how-to-avoid-recalculation-on-dependent-properties-i-hope-a-mathwork-developer-could-give-me-a
But I'm afraid I couldn't follow the code.
Also I am much more familiar with Java and Python, if that helps explain anything.
You could wrap your function into some kind of Function-Factory, which does not return numerical results, but a function that can be evaluated:
(I had to replace the call syms with sym('mu'), because for some reason it kept calling a mutools function in line omega = .... I did also change the call to gradient to make sure the arguments are in correct order, and mu will be treated as constant.)
function GradOmega = GradOmegaFactory()
x = sym('x');
y = sym('y');
z = sym('z');
mu = sym('mu');
omega = 0.5*(x^2+y^2+z^2) + (1-mu)/((x+mu)^2+y^2+z^2)^0.5 + mu/((x+mu-1)^2+y^2+z^2)^0.5;
symGradient = gradient(omega,{'x','y','z'});
GradOmega = matlabFunction(symGradient, 'vars', {'x','y','z','mu'});
end
Then you would call it via:
GradOmega = GradOmegaFactory();
result1 = GradOmega(numX1, numY1, numZ1, numMu1);
result2 = GradOmega(numX2, numY2, numZ2, numMu2);
result3 = GradOmega(numX3, numY3, numZ3, numMu3);
...
Even better:
You could go even more fancy and use a wrapper function GradOmega which builds such a function inside and makes it persistent, to get the same interface you had with your initial approach. The first time you call the function GradOmega the symbolic expression is evaluated, but on each consecutive call you will only have to evaluate the generated function handle, which means it should be nearly as fast as if you hard-coded it.
function result = GradOmega(numX, numY, numZ, numMu)
persistent numericalGradOmega;
if isempty(numericalGradOmega)
numericalGradOmega = GradOmegaFactory();
end
result = numericalGradOmega(numX, numY, numZ, numMu);
end
Use this like you would use your original version
result = GradOmega(numX, numY, numZ, numMu);
Just copy and paste both functions into a single GradOmega.m file. (GradOmega should be the first function in the file.)
Another tip: You can even evaluate this function using vectors. Instead of calling GradOmega(1,2,3,4) and GradOmega(5,6,7,8) afterwards, you can save the time overhead via the call GradOmega([1,5], [2,6], [3,7], [4,8]) using row vectors.
Yet another tip: To clean up your code even more, you could also put the first lines into a separate symOmega.m file.
function omega = symOmega()
x = sym('x');
y = sym('y');
z = sym('z');
mu = sym('mu');
omega = 0.5*(x^2+y^2+z^2) + (1-mu)/((x+mu)^2+y^2+z^2)^0.5 + mu/((x+mu-1)^2+y^2+z^2)^0.5;
This way you don't have to have a copy of this symbolic expression in every file you use it. This can be beneficial if you also want to evaluate Omega itself, as you then can make use of the same Factory-approach listed in this answer. You would end up with the following files: symOmega.m, Omega.m and GradOmega.m, where only the file symOmega.m has the actual mathematical formula and the other two files make use of symOmega.m.
I have a function
function y = testf(x,F,phi,M,beta,alpha)
y = -((F+(1 + phi.*cos(2.*pi.*x))).*M.^3.*(cosh((1 + phi.*cos(2.*pi.*x)).*M)+M.*beta.*sinh((1 + phi.*cos(2.*pi.*x)).*M)))./((1 + phi.*cos(2.*pi.*x)).*M.*cosh((1 + phi.*cos(2.*pi.*x)).*M)+(-1+(1 + phi.*cos(2.*pi.*x)).*M.^2.*beta).*sinh((1 + phi.*cos(2.*pi.*x)).*M))- (alpha.*(M.^2.*(F+(1 + phi.*cos(2.*pi.*x))).*(-1+2.*(1 + phi.*cos(2.*pi.*x)).^2.*M.^2+ cosh(2.*(1 + phi.*cos(2.*pi.*x)).*M)-2.*(1 + phi.*cos(2.*pi.*x)).*M.*sinh(2.*(1 + phi.*cos(2.*pi.*x)).*M)))./(8.*((1 + phi.*cos(2.*pi.*x)).*M.*cosh((1 + phi.*cos(2.*pi.*x)).*M)+(-1+(1 + phi.*cos(2.*pi.*x)).*M.^2.*beta).*sinh((1 + phi.*cos(2.*pi.*x)).*M)).^2));
integrating with
q = quad(#(x) testf (x, F, phi,M, beta, alpha), 0, h);
when q = 0 and x,F,phi,M,beta, how do I find alpha and draw the streamline?
It would be great if you gave some numbers, but here is how you would start this. It assumes you are using a version of Matlab that has MuPad as the Symbolic Engine.
First of all, I wouldn't use quad because symbolic expressions will be involved, use int instead.
If I understood you correctly, have the values of x, F, phi, M, beta and you would like to solve for alpha when q = 0
%define the known variables first
syms alpha %defining symbolic object
Now, the following may not work because it's a huge function:
q = int(y,x,0,h)
If it did, all you have to do is solve and evaluate the results for alpha (this may not work as well):
alpha = eval( solve( 'q' , alpha ) )
If the above didn't achieve anything, you might what to look at the 'IgnoreAnalyticConstraints' option.