This repo contains code for perform proximal gradient descent to solve sample average approximation of expected utility maximization problems with cardinality constraints. We show that, under mild conditions, the $l_1$-regularized problem is equivalent to the $l_0$-constrained problem.

We use Python 3 for our code. Please refer to requirements.txt, and use pip or conda to create a virtual environment with required packages installed.