# Minimize log(1+1/x) where 0<x<inf

#1

Hi

I am regenerating the results of a paper published in IEEE transactions in wireless communications where they used CVX to solve a convex optimization problem. They are minimizing the following function

Minimize

log_2 (1+\frac{\alpha}{b^H X b +1})+ \lambda. tr(X)
S.T.:
X is a postive semi-definite matrix (covariance matrix).

where \alpha and \lambda are constant scalars. and b is a constant vector.

The above problem is convex. Since X is positive semi-definite, therefore b^H X b > 0.

I tried many ways to formulate this problem using CVX. However, none of them succeeded. e.g.

cvx_begin
variable X(N,N) complex semidefinite
expression summation
summation=summation+log(1+alphainv_pos(b’Xb+1))/log(2)
minimize(summation+lmda
trace(X) )
cvx_end

But I obtain the following error

Error using cvx/log (line 64)
Disciplined convex programming error:
Illegal operation: log( {convex} ).

The authors mentioned that they used CVX to solve this problem. So, I am sure it can be implemented using CVX. Can someone help?

Thanks

(Mark L. Stone) #2

Can you reformulate in terms of minimizing

-log(concave function)

for some concave function? Note that to follow CVX’s rules, the argument of log must be a concave function. Then the log will be a concave function.

Note that your title question is easily handled as
minimize(-log(x))

#3

Thank you Mark for your quick response.

I tried a lot to formulate it in the form of -log (concave fn.) but unfortunately I couldn’t.

The authors must have found some work around to make CVX accept the formulation and that’s what I am trying to figure out.

Sorry for the title. It should have been log (1+1/x). I have updated that.

(Dinh) #4

May be you should try to minimize -log(x/(1+x))

(Mark L. Stone) #5

@Marc , I don’t see you to get -log(x/(1+x)) accepted by CVX. Specifically, x/(1+x) is indeed concave for x > 0, but I don’t see how to get it accepted.

@mohamedmarzban ,log(1+1/x) is convex for x > 0, but I don’'t know how it can be entered in CVX, and suspect it can not. Perhaps you can email the article authors and inquire how they used CVX.

#6

Actually, I emailed the first author but he did not answer. Maybe I will email the second author.

Thanks.

(Michael C. Grant) #7

You should reach out to the authors to find out how they formulated their problem in CVX.

(Michael C. Grant) #8

Oops, sorry, I didn’t scroll down enough. I see you’ve already reached out to them.

Frankly, while I greatly appreciate all of the citations for CVX in academic papers, I find that this kind of situation happens quite often—authors do not publish the models they use, so there’s no way to reproduce their work.

(Dinh) #9

Sorry for the late answer. May be you can rewrite your problem in the following form

minimize y
such that log(1 + 1/x) \leq y

which is equivalent to

minimize y
such that exp(-y) + exp(-y-log(x)) \leq 1

The following code is working at least

cvx_begin
variables x y
minimize( y )
exp(-y) + exp(-y - log(x) ) <= 1
cvx_end

Log{convex} log(1+1/(1+x))