Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99341
Title: Multi-directional parallel algorithms for unconstrained optimization
Authors: Phua, P.K.-H. 
Keywords: BFGS up date
Nonlinear optimization
Parallel optimization
Quasi-Newton
SR1 update
Issue Date: 1996
Source: Phua, P.K.-H. (1996). Multi-directional parallel algorithms for unconstrained optimization. Optimization 38 (2) : 107-125. ScholarBank@NUS Repository.
Abstract: Parallel algorithms for solving unconstrained nonlinear optimization problems are presented. These algorithms are based on the quasi-Newton methods. At each step of the algorithms, several search directions are generated in parallel using various quasi-Newton updates. Our numerical results show significant improvement in the number of iterations and function evaluations required by the parallel algorithms over those required by the serial quasi-Newton updates such as the SR1 method or the BFGS method for many of the test problems.
Source Title: Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/99341
ISSN: 02331934
Appears in Collections:Staff Publications

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