  This paper explores two recent methods for learning rate optimisation in
stochastic gradient descent: D-Adaptation (arXiv:2301.07733) and probabilistic
line search (arXiv:1502.02846). These approaches aim to alleviate the burden of
selecting an initial learning rate by incorporating distance metrics and
Gaussian process posterior estimates, respectively. In this report, I provide
an intuitive overview of both methods, discuss their shared design goals, and
devise scope for merging the two algorithms.
