A quadratic Grassmann manifold optimization problem arising from quantum embedding methods
The paper analyzes minimizing a quadratic function over a Grassmann manifold, relevant for quantum embedding methods like bath orbital construction. It shows that while direct optimization is hard due to non-convexity, solving an auxiliary convex problem provides a global minimum. This convex solution also serves as an excellent starting point for other complex numerical methods.