Symbolic Discovery of Iterative Algorithms: A Continuous Latent Space Bayesian Optimization Framework

In this paper, we consider the automated discovery of iterative optimization algorithms.

We formulate the algorithm discovery task as a discrete optimization problem and search for new update functions using latent space Bayesian Optimization.

The proposed framework first learns a continuous representation of the discrete space of update functions using variational autoencoders, transforming the algorithm discovery task from a discrete to a continuous search problem.

The continuous representation is subsequently used to search for new algorithms using Bayesian optimization.

Application to two case studies shows that the proposed approach can discover new update functions in symbolic form without any assumptions on the functional form of the update function.

Moreover, the computational time required to discover the new update functions is lower than existing mathematical programming-based approaches.