Search Results for author:
Found 15 papers, 4 papers with code
An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian
Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations.
GDA-AM: On the effectiveness of solving minimax optimization via Anderson Acceleration
We also empirically show that GDA-AMsolves a variety of minimax problems and improves GAN training on several datasets
GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING
We also empirically show that GDA-AM solves a variety of minimax problems and improves GAN training on several datasets
Graph coarsening: From scientific computing to machine learning
The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning.
A non-perturbative approach to computing seismic normal modes in rotating planets
A Continuous Galerkin method-based approach is presented to compute the seismic normal modes of rotating planets.
Computational Physics Earth and Planetary Astrophysics Geophysics 86-08, 86-04, 85-04, 85-08, 85-10, 15A18, 65N25, 65N30
Find the dimension that counts: Fast dimension estimation and Krylov PCA
In this paper, we consider the problem of simultaneously estimating the dimension of the principal (dominant) subspace of these covariance matrices and obtaining an approximation to the subspace.
The Eigenvalues Slicing Library (EVSL): Algorithms, Implementation, and Software
This paper describes a software package called EVSL (for EigenValues Slicing Library) for solving large sparse real symmetric standard and generalized eigenvalue problems.
Numerical Analysis
Solving Most Systems of Random Quadratic Equations
For certain random measurement models, the proposed procedure returns the true solution $\bm{x}$ with high probability in time proportional to reading the data $\{(\bm{a}_i;y_i)\}_{1\le i \le m}$, provided that the number $m$ of equations is some constant $c>0$ times the number $n$ of unknowns, that is, $m\ge cn$.
Sampling and multilevel coarsening algorithms for fast matrix approximations
To tackle these problems, we consider algorithms that are based primarily on coarsening techniques, possibly combined with random sampling.
Solving Almost all Systems of Random Quadratic Equations
This paper deals with finding an $n$-dimensional solution $x$ to a system of quadratic equations of the form $y_i=|\langle{a}_i, x\rangle|^2$ for $1\le i \le m$, which is also known as phase retrieval and is NP-hard in general.
SMASH: Structured matrix approximation by separation and hierarchy
This paper presents an efficient method to perform Structured Matrix Approximation by Separation and Hierarchy (SMASH), when the original dense matrix is associated with a kernel function.
Numerical Analysis
Low-rank Label Propagation for Semi-supervised Learning with 100 Millions Samples
The success of semi-supervised learning crucially relies on the scalability to a huge amount of unlabelled data that are needed to capture the underlying manifold structure for better classification.
Fast estimation of approximate matrix ranks using spectral densities
In this paper, we present two computationally inexpensive techniques to estimate the approximate ranks of such large matrices.
Low rank approximation and decomposition of large matrices using error correcting codes
In this paper, we show how matrices from error correcting codes can be used to find such low rank approximations and matrix decompositions, and extend the framework to linear least squares regression problems.
Scaled Gradients on Grassmann Manifolds for Matrix Completion
This paper describes gradient methods based on a scaled metric on the Grassmann manifold for low-rank matrix completion.
