Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization
This work addresses efficiency and scalability issues in optimization for researchers and practitioners dealing with high-dimensional problems, though it is incremental as it builds on existing methods.
The paper tackles the problem of large-scale black-box optimization by proposing the Limited-Memory Matrix Adaptation Evolution Strategy (LM-MA-ES), which reduces time and storage complexity from O(n^2) to O(n log(n)) while maintaining state-of-the-art performance on benchmarks and revealing vulnerabilities in a random forest classifier.
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popular method to deal with nonconvex and/or stochastic optimization problems when the gradient information is not available. Being based on the CMA-ES, the recently proposed Matrix Adaptation Evolution Strategy (MA-ES) provides a rather surprising result that the covariance matrix and all associated operations (e.g., potentially unstable eigendecomposition) can be replaced in the CMA-ES by a updated transformation matrix without any loss of performance. In order to further simplify MA-ES and reduce its $\mathcal{O}\big(n^2\big)$ time and storage complexity to $\mathcal{O}\big(n\log(n)\big)$, we present the Limited-Memory Matrix Adaptation Evolution Strategy (LM-MA-ES) for efficient zeroth order large-scale optimization. The algorithm demonstrates state-of-the-art performance on a set of established large-scale benchmarks. We explore the algorithm on the problem of generating adversarial inputs for a (non-smooth) random forest classifier, demonstrating a surprising vulnerability of the classifier.