How can I solve log(normalcdf(a-x)-normalcdf(b-x)) with cvx

I’m solving an unconstraint cvx problem with object function “log(normalcdf(a-x)-normalcdf(b-x))” but cvx does not contain this function.

I am presuming that x is your CVX (optimization) varable, and that a and b are constants.

CVX does have an approximation, log_normcdf , which is concave, and therefore can be maximized. But your objective is the log of the integral from b-x to a-x of the standard Normal density, which {superseded by edit below} is neither convex nor concave. Your only hope of using this in CVX might be to formulate a concave series approximation if maximizing, or a convex series approximation if minimizing. I have no reason to believe there is necessarily any such approximation which is adequate for optimization purposes.

Edit: In reply to subsequent post, the integral in question is neither convex nor concave, but its log appears to be concave in x.

Thanks,but normalcdf(a-x)-normalcdf(b-x) is actually log concave (convolution of two log concave functions).So -log(normalcdf(a-x)-normalcdf(b-x)) is convex. Can I define this as my own convex function in cvx?

Sorry. It appears you are correct.

If you find a way of implementing this function in CVX, exactly or to a good approximation, please post it. You can look at cvx/functions and cvx/functions/@cvx to see the current implementation and comments for log_normcdf.

I think you may be able to try a similar approach. The 2nd order Taylor series about x = c is concave due to the (x-c)^2 term having a negative coefficient. So perhaps you can use a piecewise quadratic approximation about different expansion points. I leave the details to you. I don’t know what the accuracy will wind up being, depending on the distance between expansion points.

I’m certain you cannot represent this function exactly within CVX. To understand why, please see the FAQ that Mark linked to above. I generally do not recommend that users try to implement their own functions, especially if they require approximations. There’s a strong case to be made here that CVX is simply not designed to support problems such as yours.