Dear experts
How should I represent the expressions in the picture in CVX？The code related to the expressions is shown in the Screenshot 10. The error information is shown in the Screenshot 11.
I am looking forward to your reply!
My matlab code is as follows:
Screenshot 1:
You apparently have still not resolved having a conflicting version of vec in your MATLAB path which is incompatible with the version of vec which CVX expects.
See https://ask.cvxr.com/t/cvx-not-running-quadratic-optimisation/8097/2and other posts in that topic.
Hello, Mark! I use the command ‘which -all vec’ and the output is as follows:
Hello, Mark! I followed the approach suggested by Henry in the link What might be the problem? - #4 by Mark_L_Stone, which was to make a file vec.m in the working folder as
function x = vec(X)
x = X(:);
Although the previous issue has been resolved, a new problem has arisen as follows:
Why didn’t you use the reformulation I provided in your previous topic?
Hello, Mark! When I make a file vec.m in the working folder as
function x = vec(X)
x = X(:);
the code for calculating the expression ‘gammak’ can run correctly. However, the code for calculating the expression ‘Q’ cannot run correctly.
1/gamma(k) can be handled per the link in my previous post.
But u*u' is non-convex quadratic, and doesn’t get better when divided by gamma.
Have you proven this is convex? Maybe the intention is to have a convex relaxation U \succeq u*u', and use U in place of u*u' in Q? But then what happens when dividing by gamma(k)? Or maybe there’s (also) some kind of SCA thing. But it’s your problem, in the sense that perhaps you lifted it from a paper or book, so you need to understand it, and figure out what the convex optimization problem, if any, is.
I use U instead of u*u’ and U is hermitian semidefinite matrix (CVX variable).
I need to calculate the expression ‘Q’ in the code.
What exactly is the convex optimization problem? And where is your proof of its convexity? Forum readers are not supposed to figure that out for you.
Thank you for your reply!
Perhaps I need to use other approximate solutions. And I will check the convexity of the problem.
