Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
This work addresses efficiency challenges in large-scale optimization for researchers and practitioners, offering a more data-efficient approach than classical methods, though it is incremental in refining dimensionality reduction techniques.
The paper tackles the problem of speeding up Euclidean maximization and diversity measure computations via randomized dimensionality reduction, showing that a target dimension of O(λ_X) suffices to preserve solution quality, where λ_X is the doubling dimension, and provides empirical validation of speedups.
Randomized dimensionality reduction is a widely-used algorithmic technique for speeding up large-scale Euclidean optimization problems. In this paper, we study dimension reduction for a variety of maximization problems, including max-matching, max-spanning tree, max TSP, as well as various measures for dataset diversity. For these problems, we show that the effect of dimension reduction is intimately tied to the \emph{doubling dimension} $λ_X$ of the underlying dataset $X$ -- a quantity measuring intrinsic dimensionality of point sets. Specifically, we prove that a target dimension of $O(λ_X)$ suffices to approximately preserve the value of any near-optimal solution,which we also show is necessary for some of these problems. This is in contrast to classical dimension reduction results, whose dependence increases with the dataset size $|X|$. We also provide empirical results validating the quality of solutions found in the projected space, as well as speedups due to dimensionality reduction.