For example, **a** is the variable vector of function **f**. Can i find its extreme values with help of cvx? Can I transform derivative into a maxmize vector **a** of object function **f**.

If f is concave, and all constraints, if any, are convex, you can use CVX to solve it, provided you can follow CVX’s rules.

Yes,It is convex of **f**,but log function can’t be sovled by cvx. How should i apprixmate this log into tractable form?

I have no idea what your optimization problem is.

if the only term in the objective function is `log(convex)`

, it is equivalent to i.e., you can instead use `convex`

for the objective function, i.e., do not take l`og`

.This should work presuming you are minimizing the objective (subject to convex constrains which can be formulated in CVX).

excuse me,I have some difficulties in cvx,while I have found you are good at it. Can I ask you for help?

Thanks a lot. My objective function of the optimization problem is **f** with no constraints, cause this vector **a** is a random vector.

oh,sorry for that, i try to make it more understandable cause function expression is redundant.

Here’s the expression.

where u is the variable with dimesion number 2. The optimal value of omega is the

**inverse**of

**e**.

And we are going to abtain the first order derivative of

**u**in function

**r**.

I don’t see where r_{D,I} appears ion the expression. I also don’t know what is input data vs. optimization variables.

Nevertheless, it is your responsibility to prove that it is it convex (if you are minimizing).

I appreciate your answer, Mr.Stone. Have a nice day!