Fast, robust approximate message passing
- URL: http://arxiv.org/abs/2411.02764v1
- Date: Tue, 05 Nov 2024 03:20:14 GMT
- Title: Fast, robust approximate message passing
- Authors: Misha Ivkov, Tselil Schramm,
- Abstract summary: We give a fast, spectral procedure for implementing approximate-message passing (AMP) algorithms robustly.
Our algorithm performs a spectral pre-processing step and mildly modifies the perturbeds of $mathcal A$.
- Score: 2.668787455520979
- License:
- Abstract: We give a fast, spectral procedure for implementing approximate-message passing (AMP) algorithms robustly. For any quadratic optimization problem over symmetric matrices $X$ with independent subgaussian entries, and any separable AMP algorithm $\mathcal A$, our algorithm performs a spectral pre-processing step and then mildly modifies the iterates of $\mathcal A$. If given the perturbed input $X + E \in \mathbb R^{n \times n}$ for any $E$ supported on a $\varepsilon n \times \varepsilon n$ principal minor, our algorithm outputs a solution $\hat v$ which is guaranteed to be close to the output of $\mathcal A$ on the uncorrupted $X$, with $\|\mathcal A(X) - \hat v\|_2 \le f(\varepsilon) \|\mathcal A(X)\|_2$ where $f(\varepsilon) \to 0$ as $\varepsilon \to 0$ depending only on $\varepsilon$.
Related papers
- Efficient Continual Finite-Sum Minimization [52.5238287567572]
We propose a key twist into the finite-sum minimization, dubbed as continual finite-sum minimization.
Our approach significantly improves upon the $mathcalO(n/epsilon)$ FOs that $mathrmStochasticGradientDescent$ requires.
We also prove that there is no natural first-order method with $mathcalOleft(n/epsilonalpharight)$ complexity gradient for $alpha 1/4$, establishing that the first-order complexity of our method is nearly tight.
arXiv Detail & Related papers (2024-06-07T08:26:31Z) - Partially Unitary Learning [0.0]
An optimal mapping between Hilbert spaces $IN$ of $left|psirightrangle$ and $OUT$ of $left|phirightrangle$ is presented.
An iterative algorithm for finding the global maximum of this optimization problem is developed.
arXiv Detail & Related papers (2024-05-16T17:13:55Z) - Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix
Factorization [54.29685789885059]
We introduce efficient $(1+varepsilon)$-approximation algorithms for the binary matrix factorization (BMF) problem.
The goal is to approximate $mathbfA$ as a product of low-rank factors.
Our techniques generalize to other common variants of the BMF problem.
arXiv Detail & Related papers (2023-06-02T18:55:27Z) - Krylov Methods are (nearly) Optimal for Low-Rank Approximation [8.017116107657206]
We show that any algorithm requires $Omegaleft(log(n)/varepsilon1/2right)$ matrix-vector products, exactly matching the upper bound obtained by Krylov methods.
Our lower bound addresses Open Question 1WooWoo14, providing evidence for the lack of progress on algorithms for Spectral LRA.
arXiv Detail & Related papers (2023-04-06T16:15:19Z) - An Optimal Algorithm for Strongly Convex Min-min Optimization [79.11017157526815]
Existing optimal first-order methods require $mathcalO(sqrtmaxkappa_x,kappa_y log 1/epsilon)$ of computations of both $nabla_x f(x,y)$ and $nabla_y f(x,y)$.
We propose a new algorithm that only requires $mathcalO(sqrtkappa_x log 1/epsilon)$ of computations of $nabla_x f(x,
arXiv Detail & Related papers (2022-12-29T19:26:12Z) - Sketching Algorithms and Lower Bounds for Ridge Regression [65.0720777731368]
We give a sketching-based iterative algorithm that computes $1+varepsilon$ approximate solutions for the ridge regression problem.
We also show that this algorithm can be used to give faster algorithms for kernel ridge regression.
arXiv Detail & Related papers (2022-04-13T22:18:47Z) - Low-Rank Approximation with $1/\epsilon^{1/3}$ Matrix-Vector Products [58.05771390012827]
We study iterative methods based on Krylov subspaces for low-rank approximation under any Schatten-$p$ norm.
Our main result is an algorithm that uses only $tildeO(k/sqrtepsilon)$ matrix-vector products.
arXiv Detail & Related papers (2022-02-10T16:10:41Z) - Approximate Maximum Halfspace Discrepancy [6.35821487778241]
We consider the range space $(X, mathcalH_d)$ where $X subset mathbbRd$ and $mathcalH_d$ is the set of ranges defined by $d$ halfspaces.
For each halfspace $h in mathcalH_d$ define a function $Phi(h)$ that measures the "difference" between the fraction of red and fraction of blue points which fall in the range $h$.
arXiv Detail & Related papers (2021-06-25T19:14:45Z) - Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization [51.23789922123412]
We study online learning with bandit feedback (i.e. learner has access to only zeroth-order oracle) where cost/reward functions admit a "pseudo-1d" structure.
We show a lower bound of $min(sqrtdT, T3/4)$ for the regret of any algorithm, where $T$ is the number of rounds.
We propose a new algorithm sbcalg that combines randomized online gradient descent with a kernelized exponential weights method to exploit the pseudo-1d structure effectively.
arXiv Detail & Related papers (2021-02-15T08:16:51Z) - Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication
Time [14.990725929840892]
We show an algorithm that runs in time $widetildeO(T(N, d) log kappa / mathrmpoly (varepsilon))$, where $T(N, d)$ is the time it takes to multiply a $d times N$ matrix by its transpose.
Our runtime matches that of the fastest algorithm for covariance estimation without outliers, up to poly-logarithmic factors.
arXiv Detail & Related papers (2020-06-23T20:21:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.