  We consider a recently proposed model to understand the rigidity transition
in confluent tissues and we derive the dynamical mean field theory (DMFT)
equations that describes several types of dynamics of the model in the
thermodynamic limit: gradient descent, thermal Langevin noise and active drive.
In particular we focus on gradient descent dynamics and we integrate
numerically the corresponding DMFT equations. In this case we show that
gradient descent is blind to the zero temperature replica symmetry breaking
(RSB) transition point. This means that, even if the Gibbs measure in the zero
temperature limit displays RSB, this algorithm is able to find its way to a
zero energy configuration. We include a discussion on possible extensions of
the DMFT derivation to study problems rooted in high-dimensional regression and
optimization via the square loss function.
