Projector Quantum Monte Carlo Methods for Linear and Non-linear Wavefunction Ansatzes

Schwarz, Lauretta R.

doi:10.17863/cam.13792

Cited by 1 publication

(2 citation statements)

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“…In the last few years, increasing attention [7][8][9][10]64] has been paid to optimization methods that use only first derivatives to minimize a Lagrangian of the form…”

Section: Gradient Descent Methodsmentioning

confidence: 99%

“…These methods have recently been applied successfully to Hilbert space QMC. In this study, we work in real space and investigate a combination of Nesterov momentum with RMSprop as presented by the Booth group [7,64], a method using random step sizes from the Clark group [10], AMSGrad, recently used by the Sharma group [8,9], as well as the ADAM optimizer [68].…”

Section: Gradient Descent Methodsmentioning

confidence: 99%

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Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo

Otis

Neuscamman

2019

Phys. Chem. Chem. Phys.

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We present a comparison between a number of recently introduced low-memory wave function optimization methods for variational Monte Carlo in which we find that first and second derivative methods possess strongly complementary relative advantages. While we find that low-memory variants of the linear method are vastly more efficient at bringing wave functions with disparate types of nonlinear parameters to the vicinity of the energy minimum, accelerated descent approaches are then able to locate the precise minimum with less bias and lower statistical uncertainty. By constructing a simple hybrid approach that combines these methodologies, we show that all of these advantages can be had at once when simultaneously optimizing large determinant expansions, molecular orbital shapes, traditional Jastrow correlation factors, and more nonlinear many-electron Jastrow factors.

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“…In the last few years, increasing attention [7][8][9][10]64] has been paid to optimization methods that use only first derivatives to minimize a Lagrangian of the form…”

Section: Gradient Descent Methodsmentioning

confidence: 99%