Near-linear convergence of the Random Osborne algorithm for Matrix Balancing

We revisit Matrix Balancing, a pre-conditioning task used ubiquitously for computing eigenvalues and matrix exponentials. Since 1960, Osbornes algorithm has been the practitioners algorithm of choice and is now implemented in most numerical software packages. However, its theoretical properties are not well understood. Here, we show that a simple random variant of Osbornes algorithm converges in near-linear time in the input sparsity. Specifically, it balances $Kinmathbb{R}_{geq 0}^{ntimes n}$ after $O(mepsilon^{-2}logkappa)$ arithmetic operations, where $m$ is the number of nonzeros in $K$, $epsilon$ is the $ell_1$ accuracy, and $kappa=sum_{ij}K_{ij}/(min_{ij:K_{ij} eq 0}K_{ij})$ measures the conditioning of $K$. Previous work had established near-linear runtimes either only for $ell_2$ accuracy (a weaker criterion which is less relevant for applications), or through an entirely different algorithm based on (currently) impractical Laplacian solvers. We further show that if the graph with adjacency matrix $K$ is moderately connected--e.g., if $K$ has at least one positive row/column pair--then Osbornes algorithm initially converges exponentially fast, yielding an improved runtime $O(mepsilon^{-1}logkappa)$. We also address numerical precision by showing that these runtime bounds still hold when using $O(log(nkappa/epsilon))$-bit numbers. Our results are established through an intuitive potential argument that leverages a convex optimization perspective of Osbornes algorithm, and relates the per-iteration progress to the current imbalance as measured in Hellinger distance. Unlike previous analyses, we critically exploit log-convexity of the potential. Our analysis extends to other variants of Osbornes algorithm: along the way, we establish significantly improved runtime bounds for cyclic, greedy, and parallelized variants.