Randomized sketching algorithms are a powerful tool for on-the-fly compression of matrices. In this talk we show how sketching can be used for approximate gradient and hessian-times-vector computations that are storage optimal.
This approach gives cutting-edge low memory algorithms to address the challenge of expensive storage in optimization problems with PDE constraints.
We also discuss implications for efficient adjoint computation/back-propagation.