Performance Profiles of Line-search Algorithms for Unconstrained Optimization
نویسنده
چکیده
The most important line-search algorithms for solving large-scale unconstrained optimization problems we consider in this paper are the quasi-Newton methods, truncated Newton and conjugate gradient. These methods proved to be efficient, robust and relatively inexpensive in term of computation. In this paper we compare the Dolan-Moré [11] performance profile of line-search algorithms implemented in the following software packages: Limited quasi-Newton LBFGS(m=3) by Nocedal [21], Liu and Nocedal [17]; Truncated Newton TN by Nash [19], Nash and Nocedal [ 20]; Conjugate gradient CONMIN by Shanno[22, 23], Shanno and Phua [24], CG_DESCENT by Hager and Zhang [13,14], SCALCG by Andrei [1-4] and NDHSDY by Andrei [6].
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