input vector as point to numeric function

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Elad Goldenberg
Elad Goldenberg 2021-12-30
评论： Matt J 2021-12-31
So I have this function where I pass a multi-variable function, for example lets regards f(x,y) = x^2+y^2.
Inside the function I do not know in advance how many variables will be (I get that number with nargin)
I am trying to get a random point, considef p = rand(1,2) and then get f(p).
Is there a way to generate random points and then get f(p) for multivariable functions?
Most stuff I have seen here is to do save two vectors as x =[....] , y=[...] and then calculate f(x,y), how can I handle this when my function is n-variable because I cant make n vectors of size 1 just to get a point
Thank you

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回答（1 个）

Matt J
Matt J 2021-12-30
编辑：Matt J 2021-12-30
It's not clear from your description where the random generation of p is supposed to happen. If it happens prior to calling f(), then the natural thing would be to not split p up into separate arguments. Just define f() to take a single, vector argument:
p=rand(1,n);
f=@(p) norm(p).^2
  2 个评论
Elad Goldenberg
Elad Goldenberg 2021-12-31
thank you for the answer but this isn't the answer for me :/
I'll explain more, I make a function that gets a multi variable convex function. it can be f(x,y) = x^2+y^2
it can be f(x,y) = x^3-y^10 and also f(x,y,z,t,s)= x +y +z +t +s and etc. inside the function I'm calculating the gradient and then I want to generate points and evaluate them.
I searched and found that if I send the function f = @(x)x(1)^2+x(2)^2+...+x(n)^2 then I can take a vector [1,2,...,n] and use it as a point and calculate f([1,2,...,n]) now the problem is I cant find the gradients of such function nor knowing how many variable it got using the nargin(f) method..
I don't know if this is the correct way of handling this
Matt J
Matt J 2021-12-31
now the problem is I cant find the gradients of such function nor knowing how many variable it got using the nargin(f) method..
Extending my example above,
p=rand(1,n);
function [value,gradient] = f(p)
N=numel(p); %number of elements
value=norm(p)^2/N;
gradient=(2/N)*p;
end

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