Which optimization tool should be used for solving my convex optimization problem?
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My optimization problem is convex optimization problem and it is given as follows:
The objective function is given as
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1622118/image.jpeg)
And constraints are
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1622123/image.jpeg)
The final optimization problem is given as follows:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1622128/image.jpeg)
It is already verify that above optimization probelm is convex . I am using CVX tool to solve the above problem, but not giving accurate values (means that it is solving the problem).
Please suggest proper tool to write a MATLAB code to get the oprimal power values.
In some research papers , authors were saying that Interior point methods can be utilized. How Can I use this in MATLAB to finish above optimization problem?
Should I use CVX tool or any optimization methods to complete the solution for the given formulated problem?
Thanks in Advance!
5 comentarios
Torsten
el 20 de Feb. de 2024
In the list given, I am using CVX tool with MOSEK solver, But there is a convergence problem using this tool.
You should first be optimistic about the solver used and search for the problem/error in your problem formulation/coding.
Respuestas (2)
William Rose
el 20 de Feb. de 2024
I agree with @Torsten: try fmincon. It allows you to specify linear and nonlinear equality constraints and inequality constraints. The default algoritrhm is interior point method, but you can specify a different algorithm with optimoptions .
Matt J
el 20 de Feb. de 2024
Editada: Matt J
el 20 de Feb. de 2024
If, as you seem to, you have a non-quadratic (and therefore also nonlinear) convex objective function, then you must use fmincon, regardless of the convexity or non-convexity of your constraints. There is no other solver in the Optimization Toolbox that will handle constrained non-quadratic minimization (except maybe fminimax, but you don't have a minimax problem).
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