NOMAD is a C++ implementation of the Mesh Adaptive Direct Search algorithm (MADS), designed for difficult blackbox optimization problems. These problems occur when the functions defining the objective and constraints are the result of costly computer simulations.
Charles Audet talks about blackbox optimization
- Blackbox, nonsmooth optimization
- Nonlinear constrained optimization
- Single or biobjective problems
- Derivative-free optimization
- Continuous, integer & categorical variables
- MADS algorithm
- Matlab and Python interfaces
- Parallelism with MPI
- LGPL license
- Designed for real problems
Comments, feedback and bug reports: email@example.com
|2021-07||4.1.0||VNS Search, static surrogate, poll direction ORTHO N+1 NEG, Windows|
|2015-08||3.8||Granular variables, surrogate library, robust optimization, Python interface|
|2015-03||3.7||Anisotropic scaling, shared object libraries, performance improvements|
|2014-09||3.6||Matlab interface, evaluations in blocks|
How to cite NOMAD
- [Version 4] C. Audet, S. Le Digabel, V. Rochon Montplaisir and C. Tribes. NOMAD version 4: Nonlinear optimization with the MADS algorithm. arXiv:2104.1167, 2021.
- [Version 3] S. Le Digabel. Algorithm 909: NOMAD: Nonlinear Optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 37(4):44:1–44:15, 2011.
- [Website] C. Audet, S. Le Digabel, C. Tribes and V. Rochon Montplaisir. The NOMAD project. Software available at https://www.gerad.ca/nomad.
References for the theory and algorithms
- C. Audet and J. E. Dennis, Jr. Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization, 17(1):188–217, 2006.
- C. Audet and W. Hare. Derivative-Free and Blackbox Optimization. Springer Series in Operations Research and Financial Engineering, Springer International Publishing, 302 pages, December 2017.