Worley Bradley
Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States.
J Magn Reson. 2016 Apr;265:90-8. doi: 10.1016/j.jmr.2016.02.003. Epub 2016 Feb 10.
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.
最大熵（MaxEnt）谱重建方法为非均匀采样数据集的谱估计提供了一个强大的框架。该框架内存在许多方法，通常基于MaxEnt目标函数中拉格朗日乘数的大小来定义。本文提出了一种算法，该算法利用加速一阶凸优化技术，根据最大熵原理快速、可靠地重建非均匀采样的核磁共振（NMR）数据集。这种算法——称为凸加速最大熵重建算法（CAMERA）——是一种新的谱重建方法，在恒定目标和恒定拉格朗日乘数模式下均表现出快速、可调的收敛性。描述了一种实现CAMERA的高性能、开源NMR数据处理工具，并在几个示例谱上与现有重建方法进行了简要比较。
