Solving a nonlinear constrained optimization involving CDF and expectation of normal distribution

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I would like to know if it is possible to solve the following nonlinear constrained optimization problem and find how the optimal solution varies with $C$ and $\beta$:

$\max_{x,y}\beta F(x)-d(2-F(x)-F(y)),s.t$

$\int_{-\infty}^{x}uf(u)du+\int_{-\infty}^{y}vf(v)dv\leqslant C$,

where $F(.)$ and $f(.)$ are CDF and PDF of standard normal distributions, respectively, and $d(.)$ is assumed to be sufficiently convex so that interior solution exists.

Thanks!