41 citations · 161 across the 21 of their papers we have counts for
24 papers
Efficient Mass Matrix Estimation with Gaussian Cooling
Jake Hofgard, Michael Lindsey
We introduce an algorithm for efficiently preconditioning log-concave and log-smooth distributions that scales logarithmically with the condition number of the underlying distribut…
Tensorizing flows: a tool for variational inference
Yuehaw Khoo, Michael Lindsey, Hongli Zhao
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribut…
Fast randomized entropically regularized semidefinite programming
Michael Lindsey
We develop a practical approach to semidefinite programming (SDP) that includes the von Neumann entropy, or an appropriate variant, as a regularization term. In particular we solve…
Understanding and eliminating spurious modes in variational Monte Carlo using collective variables
Huan Zhang, Robert J. Webber, Michael Lindsey +2
The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years. However, as w…
Non-Hertz-Millis scaling of the antiferromagnetic quantum critical metal via scalable Hybrid Monte Carlo
Peter Lunts, Michael S. Albergo, Michael Lindsey
A key component of the phase diagram of many iron-based superconductors and electron-doped cuprates is believed to be a quantum critical point (QCP), delineating the onset of antif…
Generative modeling via tensor train sketching
YH. Hur, J. G. Hoskins, M. Lindsey +2
In this paper, we introduce a sketching algorithm for constructing a tensor train representation of a probability density from its samples. Our method deviates from the standard re…
Ensemble Markov chain Monte Carlo with teleporting walkers
Michael Lindsey, Jonathan Weare, Anna Zhang
We introduce an ensemble Markov chain Monte Carlo approach to sampling from a probability density with known likelihood. This method upgrades an underlying Markov chain by allowing…
Scalable semidefinite programming approach to variational embedding for quantum many-body problems
Yuehaw Khoo, Michael Lindsey
In quantum embedding theories, a quantum many-body system is divided into localized clusters of sites which are treated with an accurate `high-level' theory and glued together self…
Committor functions via tensor networks
Yian Chen, Jeremy Hoskins, Yuehaw Khoo +1
We propose a novel approach for computing committor functions, which describe transitions of a stochastic process between metastable states. The committor function satisfies a back…
Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlo
Robert J. Webber, Michael Lindsey
Variational Monte Carlo (VMC) is an approach for computing ground-state wavefunctions that has recently become more powerful due to the introduction of neural network-based wavefun…
Multiscale semidefinite programming approach to positioning problems with pairwise structure
Yian Chen, Yuehaw Khoo, Michael Lindsey
We consider the optimization of pairwise objective functions, i.e., objective functions of the form fo…
Towards sharp error analysis of extended Lagrangian molecular dynamics
Dong An, Lin Lin, Michael Lindsey
The extended Lagrangian molecular dynamics (XLMD) method provides a useful framework for reducing the computational cost of a class of molecular dynamics simulations with constrain…
Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting
Xiaojie Wu, Michael Lindsey, Tiangang Zhou +2
Density matrix embedding theory (DMET) is a powerful quantum embedding method for solving strongly correlated quantum systems. Theoretically, the performance of a quantum embedding…
Variational embedding for quantum many-body problems
Lin Lin, Michael Lindsey
Quantum embedding theories are powerful tools for approximately solving large-scale strongly correlated quantum many-body problems. The main idea of quantum embedding is to glue to…
Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation
Carlos Mejuto-Zaera, Leonardo Zepeda-Núñez, Michael Lindsey +3
We introduce a nested optimization procedure using semi-definite relaxation for the fitting step in Hamiltonian-based cluster dynamical mean-field theory (DMFT) methodologies. We s…
Semidefinite relaxation of multi-marginal optimal transport for strictly correlated electrons in second quantization
Yuehaw Khoo, Lin Lin, Michael Lindsey +1
We consider the strictly correlated electron (SCE) limit of the fermionic quantum many-body problem in the second-quantized formalism. This limit gives rise to a multi-marginal opt…
Projected Density Matrix Embedding Theory with Applications to the Two-Dimensional Hubbard Model
Xiaojie Wu, Zhi-Hao Cui, Yu Tong +3
Density matrix embedding theory (DMET) is a quantum embedding theory for strongly correlated systems. From a computational perspective, one bottleneck in DMET is the optimization o…
Sparsity pattern of the self-energy for classical and quantum impurity problems
Lin Lin, Michael Lindsey
We prove that for various impurity models, in both classical and quantum settings, the self-energy matrix is a sparse matrix with a sparsity pattern determined by the impurity site…
Bold Feynman diagrams and the Luttinger-Ward formalism via Gibbs measures. Part II: Non-perturbative analysis
Lin Lin, Michael Lindsey
Many-body perturbation theory (MBPT) is widely used in quantum physics, chemistry, and materials science. At the heart of MBPT is the Feynman diagrammatic expansion, which is, simp…
Bold Feynman diagrams and the Luttinger-Ward formalism via Gibbs measures. Part I: Perturbative approach
Lin Lin, Michael Lindsey
Many-body perturbation theory (MBPT) is widely used in quantum physics, chemistry, and materials science. At the heart of MBPT is the Feynman diagrammatic expansion, which is, simp…
Variational structure of Luttinger-Ward formalism and bold diagrammatic expansion for Euclidean lattice field theory
Lin Lin, Michael Lindsey
The Luttinger-Ward functional was proposed more than five decades ago to provide a link between static and dynamic quantities in a quantum many-body system. Despite its widespread…
Convergence of adaptive compression methods for Hartree-Fock-like equations
Lin Lin, Michael Lindsey
The adaptively compressed exchange (ACE) method provides an efficient way for solving Hartree-Fock-like equations in quantum physics, chemistry, and materials science. The key step…
Optimal transport via a Monge-Ampère optimization problem
Michael Lindsey, Yanir A. Rubinstein
We rephrase Monge's optimal transportation (OT) problem with quadratic cost--via a Monge-Ampère equation--as an infinite-dimensional optimization problem, which is in fact a convex…
On discontinuity of planar optimal transport maps
Otis Chodosh, Vishesh Jain, Michael Lindsey +2
Consider two bounded domains and in , and two sufficiently regular probability measures and supported on them. By Brenier's theorem, there exists a…