Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem. Here the solution of a parameterized lower-level problem binds variables that appear in the objective of an upper-level problem. The lower-level problem typically appears as an argmin or argmax optimization problem. Many techniques have been proposed to solve bi-level optimization problems, including gradient descent, which is popular with current end-to-end learning approaches. In this technical report we collect some results on differentiating argmin and argmax optimization problems with and without constraints and provide some insightful motivating examples.
['Stephen Gould', 'Basura Fernando', 'Anoop Cherian', 'Peter Anderson', 'Rodrigo Santa Cruz', 'Edison Guo']
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization