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The computation of the dominant eigenpair for symmetric positive semidefinite matrices is fundamental in numerical optimization. This work shifts the paradigm from the classical Rayleigh quotient to an unconstrained difference formulation,…

Optimization and Control · Mathematics 2026-05-26 Xiaozhi Liu , Mengmeng Song , Yong Xia

We present a new flow-based formulation for identical parallel machine scheduling with a regular objective function and without idle time. The formulation is constructed with the help of a decision diagram that represents all job sequences…

Discrete Mathematics · Computer Science 2022-10-06 Daniel Kowalczyk , Roel Leus , Christopher Hojny , Stefan Røpke

In this work we recast parametrized time dependent optimal control problems governed by partial differential equations in a saddle point formulation and we propose reduced order methods as an effective strategy to solve them. Indeed, on one…

Numerical Analysis · Mathematics 2023-08-08 Maria Strazzullo , Francesco Ballarin , Gianluigi Rozza

A newly developed method for systematically improving the convergence of path integrals for transition amplitudes, introduced in Phys. Rev. Lett. 94 (2005) 180403, Phys. Rev. B 72 (2005) 064302, Phys. Lett. A 344 (2005) 84, and expectation…

Statistical Mechanics · Physics 2011-08-08 Danica Stojiljkovic , Aleksandar Bogojevic , Antun Balaz

Path integrals constitute powerful representations for both quantum and stochastic dynamics. Yet despite many decades of intensive studies, there is no consensus on how to formulate them for dynamics in curved space, or how to make them…

Statistical Mechanics · Physics 2022-04-27 Mingnan Ding , Xiangjun Xing

We introduce an analytical framework to calculate the values of key observables in a strongly disordered discrete time crystal (DTC) without fitting parameter. The perturbatively obtained closed-form formulae show quantitative agreement…

Disordered Systems and Neural Networks · Physics 2025-12-25 Yang-Ren Liu , Biao Huang

Path integral method in quantum mechanics provides a new thinking for barrier option pricing. For proportional step options, the option price changing process is similar to the one dimensional trapezoid potential barrier scattering problem…

Pricing of Securities · Quantitative Finance 2022-06-13 Qi Chen , Chao Guo

In the strong field molecular tunneling ionization theory of Tong et al. [Phys. Rev. A 66, 033402 (2002)], the ionization rate depends on the asymptotic wavefunction of the molecular orbital from which the electron is removed. The orbital…

Atomic Physics · Physics 2015-05-18 Song-Feng Zhao , Cheng Jin , Anh-Thu Le , T. F. Jiang , C. D. Lin

Previously, in an effective field theory formulated by us, we have carried out parameter-free calculations of a large number of low-energy two-nucleon properties. An experiment at the Institut Laue-Langevin is currently measuring…

Nuclear Theory · Physics 2009-10-31 T. -S. Park , K. Kubodera , D. -P. Min , M. Rho

We study a structure of holomorphic quantum contributions to the effective action for ${\cal N}={1/2}$ noncommutative Wess-Zumino model. Using the symbol operator techniques we present the one-loop chiral effective potential in a form of…

High Energy Physics - Theory · Physics 2009-11-10 A. T. Banin , I. L. Buchbinder , N. G. Pletnev

This paper provides a general framework for deriving effective material properties of one-dimensional, time-modulated systems of subwavelength resonators. It applies to subwavelength resonator systems with a general form of time-dependent…

Optics · Physics 2025-07-17 Habib Ammari , Jinghao Cao , Erik Orvehed Hiltunen , Liora Rueff

We present Tethered Monte Carlo, a simple, general purpose method of computing the effective potential of the order parameter (Helmholtz free energy). This formalism is based on a new statistical ensemble, closely related to the…

Statistical Mechanics · Physics 2009-08-11 L. A. Fernandez , V. Martin-Mayor , D. Yllanes

The asymptotic strong-coupling behavior as well as the exact critical exponents from scalar field theory even in the simplest case of $1+1$ dimensions have not been obtained yet. Hagen Kleinert has linked both critical exponents and strong…

High Energy Physics - Theory · Physics 2024-02-01 Abouzeid M. Shalaby

In QFT the effective potential is an important tool to study symmetry breaking phenomena. It is known that, in some theories, the canonical approach and the path-integral approach yield different effective potentials. In this paper we…

High Energy Physics - Theory · Physics 2009-11-05 E. N. Argyres , M. T. M. van Kessel , R. H. P. Kleiss

The precise description of quantum nuclear fluctuations in atomistic modelling is possible by employing path integral techniques, which involve a considerable computational overhead due to the need of simulating multiple replicas of the…

Chemical Physics · Physics 2017-03-23 Venkat Kapil , Jörg Behler , Michele Ceriotti

We derive two path integral estimators for the derivative of the quantum mechanical potential of mean force (PMF), which may be numerically integrated to yield the PMF. For the first estimator, we perform the differentiation on the exact…

Chemical Physics · Physics 2021-10-08 Dmitri Iouchtchenko , Kevin P. Bishop , Pierre-Nicholas Roy

We consider a class of systems with time-varying parameters, which are written as linear regressions with bounded disturbances. The task is to estimate such parameters under the condition that the regressor is finitely exciting (FE).…

Systems and Control · Electrical Eng. & Systems 2021-11-24 Anton Glushchenko , Konstantin Lastochkin

We demonstrate that the positive frequency modes for a complex scalar field in a constant electric field (Schwinger modes), in three different gauges, can be represented as exact Lorentzian worldline path integral amplitudes. Although the…

High Energy Physics - Theory · Physics 2021-11-23 Karthik Rajeev

The time evolution of a closed quantum system is connected to its Hamiltonian through Schroedinger's equation. The ability to estimate the Hamiltonian is critical to our understanding of quantum systems, and allows optimization of control.…

Reinforcement learning lies at the intersection of several challenges. Many applications of interest involve extremely large state spaces, requiring function approximation to enable tractable computation. In addition, the learner has only a…

Machine Learning · Computer Science 2021-05-11 Andrew Jacobsen , Alan Chan