Highlights d Optimal transport analysis recovers trajectories from 315,000 scRNA-seq profiles d Induced pluripotent stem cell reprogramming produces diverse developmental programs d Regulatory analysis identifies a series of TFs predictive of specific cell fates d Transcription factor Obox6 and cytokine GDF9 increase reprogramming efficiency

We propose a variant of the classical conditional gradient method (CGM) for sparse inverse problems with differentiable measurement models. Such models arise in many practical problems including superresolution, time-series modeling, and matrix completion. Our algorithm combines nonconvex and convex optimization techniques: we propose global conditional gradient steps alternating with nonconvex local search exploiting the differentiable measurement model. This hybridization gives the theoretical global optimality guarantees and stopping conditions of convex optimization along with the performance and modeling flexibility associated with nonconvex optimization. Our experiments demonstrate that our technique achieves state-of-the-art results in several applications.

No abstract

No abstract