Misc.#

These optimizers did not seem to naturally fall into a category. Brief descriptions are listed below.

‘NaiveIsoEMNA’#

Estimation of Multivariate Normal Algorithm This algorithm is quite efficient in a parallel context, i.e. when the population size is large.

‘TBPSA’#

Test-based population-size adaptation, for noisy problems where the best points will be an average of the final population.

‘NaiveTBPSA’#

Test-based population-size adaptation Where the best point is the best point, no average across final population.

‘NoisyBandit’#

Noisy bandit simple optimization

‘PBIL’#

Population based incremental learning “Implementation of the discrete algorithm PBIL” https://www.ri.cmu.edu/pub_files/pub1/baluja_shumeet_1994_2/baluja_shumeet_1994_2.pdf

‘PSO’#

Standard Particle Swarm Optimization, but no randomization of the population order.

‘SQP’#

Scipy Minimize Base See: https://docs.scipy.org/doc/scipy-1.1.0/reference/generated/scipy.optimize.minimize.html Note: does not support multiple jobs at once.

‘SPSA’#

The First order SPSA algorithm, See: https://en.wikipedia.org/wiki/Simultaneous_perturbation_stochastic_approximation Note: does not support multiple jobs at once.

‘SplitOptimizer’#

Combines optimizers, each of them working on their own variables. By default uses CMA and RandomSearch’s

‘cGA’#

Implementation of the discrete Compact Genetic Algorithm (cGA) https://pdfs.semanticscholar.org/4b0b/5733894ffc0b2968ddaab15d61751b87847a.pdf

‘chainCMAPowell’#

A chaining consists in running algorithm 1 during T1, then algorithm 2 during T2, then algorithm 3 during T3, etc. Each algorithm is fed with what happened before it. This ‘chainCMAPowell’ chains first ‘CMA’ then the ‘Powell’ optimizers. Note: does not support multiple jobs at once.

Experimental Variants#

Nevergrad also comes with a number of Experimental variants, to see all of the different options run:

import nevergrad as ng
import nevergrad.optimization.experimentalvariants
print(sorted(ng.optimizers.registry.keys()))

Note: you do not have to run this import to select any of these options, this is just to see the different options.