Algorithm 856: APPSPACK 4.0: asynchronous parallel pattern search for derivative-free optimization
ACM Transactions on Mathematical Software (TOMS)
Nonmonotone derivative-free methods for nonlinear equations
Computational Optimization and Applications
A generating set search method using curvature information
Computational Optimization and Applications
A derivative-free nonmonotone line-search technique for unconstrained optimization
Journal of Computational and Applied Mathematics
Sprouting search-an algorithmic framework for asynchronous parallel unconstrained optimization
Optimization Methods & Software
Sequential Penalty Derivative-Free Methods for Nonlinear Constrained Optimization
SIAM Journal on Optimization
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In this paper, starting from the study of the common elements that some globally convergent direct search methods share, a general convergence theory is established for unconstrained minimization methods employing only function values. The introduced convergence conditions are useful for developing and analyzing new derivative-free algorithms with guaranteed global convergence. As examples, we describe three new algorithms which combine pattern and line search approaches.