Using a cell array as an argument of a function handle

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Hi all,
Is it possible to use a cell array as arguments in a function handle?
For example:
a(1) = {@(x) x(1) + 2};
a(2) = {@(x) x(2) + 3};
a
a = 1×2 cell array
{@(x)x(1)+2} {@(x)x(2)+3}
b = @(y) y(1) + y(2)
g = function_handle with value:
@(y)y(1)+y(2)
c = b(a)
Operator '+' is not supported for operands of type 'cell'.

Error in solution>@(y)y(1)+y(2) (line 4)
g = @(y) y(1) + y(2)
I would like to get c = a(1) + a(2) or c = @(x) x(1) + 2 + x(2) + 3;
Thanks!

Answers (1)

Angelo Yeo
Angelo Yeo on 19 Jul 2023
a(1) = {@(x) x(1) + 2};
a(2) = {@(x) x(2) + 3};
b = @(x) a{1}(x) + a{2}(x); % merging function handles
my_input = [5, 8]; % for example
c = b(my_input)
c = 18
my_input(1) + 2 + my_input(2) + 3 % to test
ans = 18
  3 Comments
Angelo Yeo
Angelo Yeo on 20 Jul 2023
Edited: Angelo Yeo on 20 Jul 2023
I don't actually understand your intention. Why do you want to make two function handles to input "u"? The code below would work with the same result. This uses function handle but only once. You don't have to put "u" as a input for a function handle. You can use element-wise multiplication (.*).
mu = [2e4 12 0.04 2e10 9.82e-4 1e11]; % Mean
sigma = [1.4e3 0.12 4.8e-3 1.2e9 5.9852e-5 6e9]; % Standard deviation
dist = ["normal", "normal", "normal", "normal", "normal","normal"]; % Distribution
numVar = length(mu);
u = zeros(1, 6);
U = mu + sigma.*u;
gx = @(x) 0.03 - (x(1) * x(2) ^ 2) / 2 * (3.81 / (x(3) * x(4)) + 1.13 / (x(5) * x(6))); % Limit state equation
gx(U)
ans = 0.0066
Geovane Gomes
Geovane Gomes on 20 Jul 2023
Actually I need to evaluate the gradient of this:
mu = [2e4 12 0.04 2e10 9.82e-4 1e11]; % Mean
sigma = [1.4e3 0.12 4.8e-3 1.2e9 5.9852e-5 6e9]; % Standard deviation
dist = ["normal", "normal", "normal", "normal", "normal","normal"]; % Distribution
numVar = length(mu);
% Using symbolic
x = sym("x", [1 numVar]);
u = sym("u",[1 numVar]);
gx = 0.03 - (x(1) * x(2) ^ 2) / 2 * (3.81 / (x(3) * x(4)) + 1.13 / (x(5) * x(6))); % Limit state equation
for i = 1:numVar
U(i) = mu(i) + sigma(i) * u(i);
end
gy = subs(gx,x,U);
nablagy = gradient(gy)
nablagy = 
y = zeros(1, 6);
nablaGy = double(subs(nablagy,u,y))
nablaGy = 6×1
-0.0016 -0.0005 0.0008 0.0004 0.0010 0.0010
clear
mu = [2e4 12 0.04 2e10 9.82e-4 1e11]; % Mean
sigma = [1.4e3 0.12 4.8e-3 1.2e9 5.9852e-5 6e9]; % Standard deviation
dist = ["normal", "normal", "normal", "normal", "normal","normal"]; % Distribution
numVar = length(mu);
u = zeros(1, 6);
U = mu + sigma.*u;
gx = @(x) 0.03 - (x(1) * x(2) ^ 2) / 2 * (3.81 / (x(3) * x(4)) + 1.13 / (x(5) * x(6))); % Limit state equation
gx(U)
ans = 0.0066
nablagx = @(x) gradient(gx(x))
nablagx = function_handle with value:
@(x)gradient(gx(x))
nablaGx = nablagx(U)
nablaGx = 0

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