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- Ruoxi Wang, Yingzhou Li, Michael W. Mahoney, Eric Darve
- ArXiv
- 2015

Kernel matrices are popular in machine learning and scientific computing, but they are limited by their quadratic complexity in both construction and storage. It is well-known that as one varies the kernel parameter, e.g., the width parameter in radial basis function kernels, the kernel matrix changes from a smooth low-rank kernel to a diagonally-dominant… (More)

- Yingzhou Li, Haizhao Yang, Lexing Ying
- Multiscale Modeling & Simulation
- 2015

This paper presents an efficient multiscale butterfly algorithm for computing Fourier integral operators (FIOs) of the form (Lf)(x) = R d a(x, ξ)e 2πıΦ(x,ξ) f (ξ)dξ, where Φ(x, ξ) is a phase function, a(x, ξ) is an amplitude function, and f (x) is a given input. The frequency domain is hierarchically decomposed into a union of Cartesian coronas. The… (More)

- Yingzhou Li, Haizhao Yang, Eileen R. Martin, Kenneth L. Ho, Lexing Ying
- Multiscale Modeling & Simulation
- 2015

The paper introduces the butterfly factorization as a data-sparse approximation for the matrices that satisfy a complementary low-rank property. The factorization can be constructed efficiently if either fast algorithms for applying the matrix and its adjoint are available or the entries of the matrix can be sampled individually. For an N × N matrix, the… (More)

- Yingzhou Li, Lexing Ying
- ArXiv
- 2016

The hierarchical interpolative factorization (HIF) offers an efficient way for solving or precon-ditioning elliptic partial differential equations. By exploiting locality and low-rank properties of the operators, the HIF achieves quasi-linear complexity for factorizing the discrete positive definite elliptic operator and linear complexity for solving the… (More)

This paper introduces the multidimensional butterfly factorization as a data-sparse representation of multidimensional kernel matrices that satisfy the complementary low-rank property. This factorization approximates such a kernel matrix of size N × N with a product of O(log N) sparse matrices, each of which contains O(N) nonzero entries. We also propose… (More)

- Yingzhou Li, Haizhao Yang
- SIAM J. Scientific Computing
- 2017

This paper introduces the interpolative butterfly factorization for nearly optimal implementation of several transforms in harmonic analysis, when their explicit formulas satisfy certain analytic properties and the matrix representations of these transforms satisfy a complementary low-rank property. A preliminary interpolative butterfly factorization is… (More)

- Yingzhou Li, Haizhao Yang
- ArXiv
- 2017

This paper proposes an efficient method for computing selected generalized eigenpairs of a sparse Hermitian definite matrix pencil (A, B). Based on Zolotarev's best rational function approximations of the signum function and conformal maps, we construct the best rational function approximation of a rectangular function supported on an arbitrary interval.… (More)

- Ruoxi Wang, Yingzhou Li, Eric Darve
- ArXiv
- 2017

Low-rank approximations are popular techniques to reduce the high computational cost of large-scale kernel matrices, which are of significant interest in many applications. The success of low-rank methods hinges on the matrix rank, and in practice, these methods are effective even for high-dimensional datasets. The practical success has elicited the… (More)

- Guoping Yong, Yingzhou Li, Wenlong She, Yiman Zhang
- Chemistry
- 2011

Five phosphorescent metal-anion radical coordination polymers based on a new anion radical ligand generated by in situ deprotonation of a stable zwitterionic radical are described. The N,O,N-tripodal anion radical ligand links metal cations, which leads to five isostructural coordination polymers, [M(3)(bipo(-.))(4)(L)(2)](n) (M=Cd or Mn,… (More)

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