A View of Algorithms for Optimization without Derivatives 1

By M. Powell. 2007

254 citations recorded by the source corpus.

Subfields: Mathematical Optimization, Numerical Methods & Scientific Computing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mathematical Optimization

1. David Eriksson
2. Michael Pearce
3. Ryan D. Turner
4. Jacob V. Gardner
5. Matthias Poloczek

In Numerical Methods & Scientific Computing

1. David Eriksson
2. Michael Pearce
3. Ryan D. Turner
4. Jacob V. Gardner
5. Matthias Poloczek

Citations in the corpus

• Scalable Global Optimization via Local Bayesian Optimization (2019, methodology): “CMA-ES and BOBYQA outperform the other BO methods.”
• Bayesian Optimization for Adaptive Experimental Design: A Review (2020, unknown): “For more information, see http://creativecommons.org/licenses/by/4.0/ 13937 multi-start derivative free local optimiser e.g. COBYLA [36], or evolutionary algorithms e.g. ISRES [37], or Lipschitzian methods such as DIRECT [34].”