A parallel quasi-Newton method for Gaussian data fitting
We describe a parallel method for unconstrained optimization based on the quasi-Newton descent method of Broyden, Fletcher, Goldfarb, and Shanno. Our algorithm is suitable for both single-instruction and multiple-instruction parallel architectures and has only linear memory requirements in the number of parameters used to fit the data. We also present the results of numerical testing on both single and multiple Gaussian data fitting problems.
quasi-newton gaussian data fitting optimization parallel algorithm