# understanding bayesian optimization: what is meant by dimension

What is meant by the dimension in bayesian optimization? In many papers it is stated that the dimension should be lower than 20 (then there are papers which are solving high-dimensional problem). But, is this the dimension of data set, or the number of variables to be optimized? For example, I have a data set with 100 features and I want to train a neural network and want to find the best learning rate and momentum term (i.e. 2 parameters to optimize).

edit: Here it is written in second sentence: https://www.groundai.com/project/a-tutorial-on-bayesian-optimization/1

I suppose 20 dimensions just means optimization problem for some parameter vector $$\theta$$ that has 20 coordinates such that $$\theta=(\theta_1,...,\theta_{20}) \in \mathbb R^{20}$$. They are considering problems of dimension less that 20 so they are solving for less than $$20$$ values. As the article you refer to say the solution is in $$\mathbb R^d$$ with $$d<20$$.