apply minimizing algorithm in matlab

To apply a minimizing algorithm in MATLAB, you can use the built-in function fmincon. This function is used for finding the minimum of a constrained nonlinear multivariable function. Here is an example code snippet:

main.m
function [xopt, fval] = minimizeFunction()
% Define the objective function
fun = @(x) (x(1)-1)^2 + (x(2)-2.5)^2 + (x(3)-3)^2;
% Define the initial point
x0 = [0,0,0];
% Define the lower and upper bounds
lb = [-10,-10,-10];
ub = [10,10,10];
% Define the non-linear constraints
nonlcon = @(x) x(1)^2 + x(2)^2 - 10;
% Find the minimum
[xopt, fval] = fmincon(fun, x0, [], [], [], [], lb, ub, nonlcon);
end
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In this example, we define the objective function as a sum of the squared differences between the variable values and their desired values. We then define the initial point, lower and upper bounds, and nonlinear constraints. Finally, we use fmincon to find the minimum of the function, subject to the constraints we have defined. The output of the function is the optimal value of x (xopt) and the value of the objective function at the optimal point (fval).