Towards Optimal Algorithms for Recovering Low-Dimensional Models with Linear Convergence Rates: A Projected Gradient Descent Approach

This paper proposes a framework for designing optimal algorithms to recover low-dimensional models from linear measurements, focusing on projected gradient descent (PGD) algorithms and introducing a novel restricted Lipschitz condition for projections to guarantee linear convergence rates.