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Low-Rank Approximation with 1/\epsilon^{1/3} Matrix-Vector Products

04 Oct
Tuesday, 10/04/2022 4:00pm to 5:00pm
Computer Science Building, Room 140
Theory Seminar

Abstract: In this talk, we consider iterative methods based on Krylov subspaces for low-rank approximation under any Schatten-$p$ norm. Here, given access to a matrix $A$ through matrix-vector products, an accuracy parameter $\epsilon$, and a target rank $k$, the goal is to find a rank-$k$ matrix $Z$ with orthonormal columns such that $\| A (I - ZZ^\top ) \|_{S_p} \leq (1+\epsilon)\min_{U^\top U = I_k }\| A (I - U U^\top)\|_{S_p}$, where $\| M \|_{S_p}$ denotes the $\ell_p$ norm of the the singular values of~$M$. For the special cases of $p=2$ (Frobenius norm) and $p = \infty$ (Spectral norm), Musco and Musco (NeurIPS 2015) obtained an algorithm based on Krylov methods that uses $\tilde{O}(k/\sqrt{\epsilon})$ matrix-vector products, improving on the na\"ive $\tilde{O}(k/\epsilon)$ dependence obtainable by the power method.

Our main result is an algorithm that uses only $\tilde{O}(kp^{1/6}/\epsilon^{1/3})$ matrix-vector products, and works for \emph{all}, not necessarily constant, $p \geq 1$. For $p = 2$ our bound improves the previous $\tilde{O}(k/\epsilon^{1/2})$ bound to $\tilde{O}(k/\epsilon^{1/3})$. Since the Schatten-$p$ and Schatten-$\infty$ norms of any matrix are the same up to a $1+ \epsilon$ factor when $p \geq (\log d)/\epsilon$, our bound recovers the result of Musco and Musco for $p = \infty$. Further, we prove a matrix-vector query lower bound of $\Omega(1/\epsilon^{1/3})$ for \emph{any} fixed constant $p \geq 1$, showing that surprisingly $\tilde{\Theta}(1/\epsilon^{1/3})$ is the optimal complexity for constant~$k$.

To obtain our results, we introduce several new techniques, including optimizing over multiple Krylov subspaces simultaneously, and pinching inequalities for partitioned operators. Our lower bound for $p \in [1,2]$ uses the Araki-Lieb-Thirring trace inequality, whereas for $p>2$, we appeal to a norm-compression inequality for aligned partitioned operators.

Based on joint work with Ken Clarkson and David Woodruff.

Bio: Ainesh Bakshi is a postdoctoral fellow at MIT, with a joint position between Math and CS. He recently obtained his PhD from CMU, under the advisement of Pravesh Kothari and David Woodruff. His research interests include theoretical machine learning, optimization via the sum-of-squares hierarchy, randomized numerical linear algebra and high-dimensional probability.

The CICS Theory Seminar is free and open to the public. If you are interested in giving a talk, please email Cameron Musco or Rik Sengupta. Note that in addition to being a public lecture series, this is also a one-credit graduate seminar (CompSci 891M) that can be taken repeatedly for credit.