Hi Bibhudatta,
- Given the optimization problem and the MATLAB script, it appears that you have encountered a common issue in numerical optimization which is the tolerance of the solution. This discrepancy is likely due to the tolerance settings of the ‘fmincon’ solver.
- You can adjust the tolerance settings of ‘fmincon’ using the ‘optimoptions' function.
Here's how you can adjust the tolerance settings in your script:
% Define the objective function
f = @(x) (x(1)^2 + x(2)^2 + x(3)^2);
% Initial feasible solution
x0 = [0, 0, 0];
% Linear inequality constraints
A = [0, -1, 1; -1, 0, 1];
b = [0; 0];
% Bounds
lb = [0; 0; 0];
ub = [5; 3; 3];
% Nonlinear equality constraint
ceq = @(x) (x(1) - x(2)^2 + x(2)*x(3) - 4);
nlcon = @(x) deal([], ceq(x));
% Create options with a tighter constraint tolerance
options = optimoptions('fmincon', 'ConstraintTolerance', 1e-10);
% Solve the optimization problem with the specified options
[x, fval] = fmincon(f, x0, A, b, [], [], lb, ub, nlcon, options)
Please refer the below documentation for ‘optimoptions’ function:
I Hope it helps!
