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SIAM Journal on Scientific Computing

A Preconditioned Riemannian Conjugate Gradient Method to Compute Ground States of Spin-1 Bose–Einstein Condensates

Wen Tao Huang, Zilin Yang, Qinglin Tang

Published Apr 1, 2026• 0 citations
Publisher

Abstract

Abstract. In this paper, we propose a preconditioned Riemannian conjugate gradient method for computing ground states of spin-1 Bose-Einstein condensates, which can be reformulated as an optimization problem with the mass and magnetization constraints. The energy functional and constraints can be discretized using Fourier pseudospectral schemes, thereby transforming the problem into an optimization task on a manifold. We derive vector transports by differentiating three existing retractions and propose an initial step size selection strategy based on the second-order approximation of the energy function for the proposed Riemannian optimization algorithm. In addition, a preconditioner is derived and used. To further accelerate the convergence, we combine the proposed algorithm with a multigrid method. Numerical experiments demonstrate the efficiency and accuracy of the proposed method while confirming the usefulness of our preconditioning technique and the effectiveness of the proposed strategy for initial step size selection.

DOI
10.1137/25m1729277

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This paper addresses the computation of ground states of multicomponent Bose-Einstein condensates, defined as the global minimiser of an energy functional on an infinite-dimensional generalised oblique manifold. We establish the existence of the ground state, prove its uniqueness up to scaling, and characterise it as the solution to a coupled nonlinear eigenvector problem. By equipping the manifold with several Riemannian metrics, we introduce a suite of Riemannian gradient descent and Riemannian Newton methods. Metrics that incorporate first- or second-order information about the energy are particularly advantageous, effectively preconditioning the resulting methods. For a Riemannian gradient descent method with an energy-adaptive metric, we provide a qualitative global and quantitative local convergence analysis, confirming its reliability and robustness with respect to the choice of the spatial discretisation. Numerical experiments highlight the computational efficiency of both the Riemannian gradient descent and Newton methods.