  In this paper, we focus on the nonconvex-strongly-convex bilevel optimization
problem (BLO). In this BLO, the objective function of the upper-level problem
is nonconvex and possibly nonsmooth, and the lower-level problem is smooth and
strongly convex with respect to the underlying variable $y$. We show that the
feasible region of BLO is a Riemannian manifold. Then we transform BLO to its
corresponding unconstrained constraint dissolving problem (CDB), whose
objective function is explicitly formulated from the objective functions in
BLO. We prove that BLO is equivalent to the unconstrained optimization problem
CDB. Therefore, various efficient unconstrained approaches, together with their
theoretical results, can be directly applied to BLO through CDB. We propose a
unified framework for developing subgradient-based methods for CDB. Remarkably,
we show that several existing efficient algorithms can fit the unified
framework and be interpreted as descent algorithms for CDB. These examples
further demonstrate the great potential of our proposed approach.
