Software documentation
High Level functions
| gpopt | Sequential or batch optimization using GP algorithms |
| bfgs_search_prior | Optimize prior hyper-parameter with respect to the pseudo-likelihood using the BFGS algorithm |
Chaining procedures
| chaining_tree | Compute the chaining tree given the distance matrix |
| chaining_ucb | Compute the chaining UCB given the distance matrix |
| enet_greedy | Compute an $\epsilon$-net given the distance matrix |
GP computing
| gp_dist | Canonical GP distance $d^2(x,y)=V[f(x)-f(y)\mid X_t, Y_t]$ |
| gp_downdate | Posterior $\mu(x_i)$ and $\sigma^2(x_i)$ given $X_t\setminus\{x_i\}$ |
| gp_inf | Bayesian system resolution for computing posterior of GP given observations |
| gp_inf_update | Bayesian system update with new observations |
| gp_lik | Negative log likelihood |
| gp_loolik | Negative log pseudo-likelihood |
| gp_pred | Posterior mean and variance of GP given the kernel matrices and the Bayesian inferance |
| gp_sample | Sample a Gaussian process in a hypercube and perform Bayesian inferance |
Predefined basis and kernel functions
| basis_cst | Constant mean function |
| basis_none | Zero mean function |
| kernel_matern | Matern covariance function for $ u \in\{1/2,3/2,5/2\}$ |
| kernel_se | Squared exponential covariance function |
| kernel_se_normiso | Isotropic squared exponential covariance function |
Utils
| cholpsd | Upper Cholesky decomposition of psd matrix |
| cummax | Cumulative maximum as used in simple regrets |
| solve_chol | Linear system resolution $MX=Y$ given upper Cholesky decomposition $M=R^\top R$ |