Sourcebook of parallel computing
Algorithm 856: APPSPACK 4.0: asynchronous parallel pattern search for derivative-free optimization
ACM Transactions on Mathematical Software (TOMS)
Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination
INFORMS Journal on Computing
Sprouting search-an algorithmic framework for asynchronous parallel unconstrained optimization
Optimization Methods & Software
Parallel Stochastic Global Optimization Using Radial Basis Functions
INFORMS Journal on Computing
A parallel, asynchronous method for derivative-free nonlinear programs
ICMS'06 Proceedings of the Second international conference on Mathematical Software
A Low-rate Data Transfer Technique for Compressed Voice Channels
Journal of Signal Processing Systems
Simultaneous optimization and uncertainty quantification
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
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In this paper we prove global convergence for asynchronous parallel pattern search. In standard pattern search, decisions regarding the update of the iterate and the step-length control parameter are synchronized implicitly across all search directions. We lose this feature in asynchronous parallel pattern search since the search along each direction proceeds semiautonomously. By bounding the value of the step-length control parameter after any step that produces decrease along a single search direction, we can prove that all the processes share a common accumulation point and, if the function is continuously differentiable, that such a point is a stationary point of the standard nonlinear unconstrained optimization problem.