title: Convex accelerated maximum entropy reconstruction.
abstract: Maximum entropy (MaxEnt) spectral reconstruction methods provide a powerful framework for spectral estimation of nonuniformly sampled datasets. Many methods exist within this framework, usually defined based on the magnitude of a Lagrange multiplier in the MaxEnt objective function. An algorithm is presented here that utilizes accelerated first-order convex optimization techniques to rapidly and reliably reconstruct nonuniformly sampled NMR datasets using the principle of maximum entropy. This algorithm - called CAMERA for Convex Accelerated Maximum Entropy Reconstruction Algorithm - is a new approach to spectral reconstruction that exhibits fast, tunable convergence in both constant-aim and constant-lambda modes. A high-performance, open source NMR data processing tool is described that implements CAMERA, and brief comparisons to existing reconstruction methods are made on several example spectra.
authors.affiliation: Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States. Electronic address: bradley.worley@huskers.unl.edu.
authors.forename: Bradley
authors.initials: B
authors.lastname: Worley
keywords: CAMERA; MaxEnt; Multidimensional NMR; NESTA; Nonuniform sampling