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The standard regression tree method applied to observations within clusters poses both methodological and implementation challenges. Effectively leveraging these data requires methods that account for both individual-level and sample-level…

Methodology · Statistics 2025-03-05 Jeremiah Allis , Xin Jin , Riddhi Ghosh

The time-dependent generator coordinate method (TDGCM) is a powerful method to study the large amplitude collective motion of quantum many-body systems such as atomic nuclei. Under the Gaussian Overlap Approximation (GOA), the TDGCM leads…

Computational Physics · Physics 2018-04-04 D. Regnier , N. Dubray , M. Verrière , N. Schunck

The calculation of molecular excited states is critically important to decipher a plethora of molecular properties. In this manuscript, we develop an equation of motion formalism on top of a bi-exponentially parametrized ground state…

Chemical Physics · Physics 2024-01-09 Anish Chakraborty , Pradipta Kumar Samanta , Rahul Maitra

The synthetic control method (SCM) is widely used for constructing the counterfactual of a treated unit based on data from control units in a donor pool. Allowing the donor pool contains more control units than time periods, we propose a…

Econometrics · Economics 2026-05-19 Chengwang Liao , Zhentao Shi , Yapeng Zheng

The generalized negative binomial distribution (GNB) is a new flexible family of discrete distributions that are mixed Poisson laws with the mixing generalized gamma (GG) distributions. This family of discrete distributions is very wide and…

Methodology · Statistics 2018-09-18 Andrey K. Gorshenin , Victor Yu. Korolev

A new maximum approximate likelihood (ML) estimation algorithm for the mixture of Kent distribution is proposed. The new algorithm is constructed via the BSLM (block successive lower-bound maximization) framework and incorporates manifold…

Computation · Statistics 2017-09-15 Hien D. Nguyen

A new theoretical method is proposed to describe the ground and excited cluster states of atomic nuclei. The method utilizes the equation-of-motion of the Gaussian wave packets to generate the basis wave functions having various cluster…

Nuclear Theory · Physics 2019-07-03 R. Imai , T. Tada , M. Kimura

The constrained gradient method (CGM) has recently been proposed to solve convex optimization and monotone variational inequality (VI) problems with general functional constraints. While existing literature has established convergence…

Optimization and Control · Mathematics 2025-11-24 Danqing Zhou , Hongmei Chen , Shiqian Ma , Junfeng Yang

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018); Sci.…

The variational quantum eigensolver (VQE) algorithm, designed to calculate the energy of molecular ground states on near-term quantum computers, requires specification of symmetries that describe the system, e.g. spin state and number of…

Quantum Physics · Physics 2020-06-18 Gabriel Greene-Diniz , David Muñoz Ramo

Coarse-grained (CG) molecular dynamics simulations extend the length and time scale of atomistic simulations by replacing groups of correlated atoms with CG beads. Machine-learned coarse-graining (MLCG) has recently emerged as a promising…

Chemical Physics · Physics 2025-06-25 Leon Klein , Atharva Kelkar , Aleksander Durumeric , Yaoyi Chen , Frank Noé

Quantum kernel methods offer significant theoretical benefits by rendering classically inseparable features separable in quantum space. Yet, the practical application of Quantum Machine Learning (QML), currently constrained by the…

Machine Learning · Computer Science 2026-02-03 Philipp Altmann , Maximilian Mansky , Maximilian Zorn , Jonas Stein , Claudia Linnhoff-Popien

Variational-Quantum-Eigensolver (VQE) method has been known as the method of chemical calculation using quantum computers and classical computers. This method also can derive the energy levels of excited states by…

Quantum Physics · Physics 2021-09-07 Hikaru Wakaura , Takao Tomono

We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific…

Machine Learning · Computer Science 2021-02-22 Robert Mansel Gower , Nicolas Loizou , Xun Qian , Alibek Sailanbayev , Egor Shulgin , Peter Richtarik

A framework based on iterative coordinate minimization (CM) is developed for stochastic convex optimization. Given that exact coordinate minimization is impossible due to the unknown stochastic nature of the objective function, the crux of…

Machine Learning · Statistics 2020-03-13 Sudeep Salgia , Qing Zhao , Sattar Vakili

Variational approaches, such as variational Monte Carlo (VMC) or the variational quantum eigensolver (VQE), are powerful techniques to tackle the ground-state many-electron problem. Often, the family of variational states is not invariant…

Quantum Physics · Physics 2023-10-10 Javier Robledo Moreno , Jeffrey Cohn , Dries Sels , Mario Motta

The conjugate gradient (CG) method, a standard and vital way of minimizing the energy of a variational state, is applied to solve several problems in Skyrmion physics. The single-Skyrmion profile optimizing the energy of a two-dimensional…

Disordered Systems and Neural Networks · Physics 2017-12-12 Jung Hoon Han , Manhyung Han

Large-scale calculation based on the multi-configuration Skyrme density functional theory is performed for the light N=Z even-even nucleus, 12C. Stochastic procedures and the imaginary-time evolution are utilized to prepare many Slater…

Nuclear Theory · Physics 2012-12-04 Y. Fukuoka , T. Nakatsukasa , Y. Funaki , K. Yabana

This paper proposes the method to optimize restriction and prolongation operators in the two-grid method. The proposed method is straightforwardly extended to the geometric multigrid method (GMM). GMM is used in solving discretized partial…

Numerical Analysis · Mathematics 2018-06-18 Alexandr Katrutsa , Talgat Daulbaev , Ivan Oseledets

Motion planning is an essential aspect of autonomous systems and robotics and is an active area of research. A recently-proposed sampling-based motion planning algorithm, termed 'Generalized Shape Expansion' (GSE), has been shown to possess…

Robotics · Computer Science 2021-02-24 Adhvaith Ramkumar , Vrushabh Zinage , Satadal Ghosh