A parallel quasi-Newton method for Gaussian data fitting

✍ Scribed by Paul Caprioli; Mark H. Holmes

• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 271 KB
• Volume: 24
• Category: Article
• ISSN: 0167-8191
• DOI: 10.1016/s0167-8191(98)00055-6

✦ Synopsis

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 ®t the data. We also present the results of numerical testing on both single and multiple Gaussian data ®tting problems.