Quadratic Objective with two Quadratic Constraints
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Hii,
My programming knowledge is limited. I would like to solve a quadratic objective function with two quadratic constraints. I read and understood the concepts and examples given in https://in.mathworks.com/help/optim/ug/linear-or-quadratic-problem-with-quadratic-constraints.html?s_tid=mwa_osa_a, but I am having difficulty in implementing this for my problem.
Matrices A, B and C be
symmetric positive semi-definite matrices. How to find the
subject to the constraints
and
? The documentation deals with a single quadratic constraint only. But my problem has two quadratic constraints. How can I code the second constraint as per the documentation in the link above?
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1533152/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1533157/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1533162/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1533167/image.png)
Thank You..
3 Kommentare
Antworten (1)
Torsten
am 8 Nov. 2023
Bearbeitet: Torsten
am 8 Nov. 2023
What about
function [y,yeq,grady,gradyeq] = quadconstr(x,B,C)
y = [];
yeq(1) = x.'*B*x - 1;
yeq(2) = x.'*C*x - 1;
if nargout > 2
grady = [];
gradyeq(:,1) = 2*B*x; % Assumes B is symmetric, otherwise (B+B.')*x
gradyeq(:,2) = 2*C*x; % Assumes C is symmetric, otherwise (C+C.')*x
end
end
10 Kommentare
Torsten
am 9 Nov. 2023
What I meant is I tried to solve it by using eigen value decomposition.
You formulated an optimization problem. Obviously, the problem is not well-posed because the objective function is not real-valued.
What is the underlying problem that you tried to solved via this optimization formulation ? (You again talk of "I tried to solve it", but you don't explain what "it" is).
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