We consider the traditional compressed sensing problem of recovering a sparse solution from undersampled data. We are in particular interested in the case where the measurements arise from a partial circulant matrix. This is motivated by practical physical setups that are usually implemented through convolutions. We derive a new optimization problem that stems from the traditional ℓ 1 minimization under constraints, with the added information that the matrix is taken by selecting rows from a circulant matrix. With this added knowledge it is possible to simulate the full matrix and full measurement vector on which the optimization acts. Moreover, as circulant matrices are well-studied it is known that using Fourier transform allows for fast computations. This paper describes the motivations, formulations, and preliminary results of this novel algorithm, which shows promising results.