arXiv subjects

Kewang Chen

Publications and source records attributed to Kewang Chen.

5 recordsLinked to original sources

arXiv2026

Global Existence for General Systems of Isentropic Gas Dynamics via a Weighted Pressure Perturbation Approach

We establish the global existence of weak entropy solutions for 1D isentropic gas dynamics with general pressure laws ($\gamma > 1$). To address vacuum degeneracy, we introduce a novel structural regularization via a "Synchronized Dual Translation" strategy. This approach offers a distinct advantage over the flux-modification method of Lu (2007): while Lu's method induces a structural mismatch requiring restrictive constraints on $P'''$ to control regularization artifacts, our construction preserves structural isomorphism with the standard Euler equations. Consequently, the approximate entropies satisfy a homogeneous Generalized Euler-Poisson-Darboux equation. This allows us to prove strong convergence under natural asymptotic assumptions, effectively eliminating the need for the technical higher-order derivative constraints required in prior works.

math.AP↗

arXiv2025

The Dynamical Anatomy of Anderson Acceleration:From Adaptive Momentum to Variable-Mass ODEs

This paper provides a rigorous derivation and analysis of accelerated optimization algorithms through the lens of High-Resolution Ordinary Differential Equations (ODEs). While classical Nesterov acceleration is well-understood via asymptotic vanishing damping, the dynamics of Anderson Acceleration (AA) remain less transparent. This work makes significant theoretical contributions to AA by bridging discrete acceleration algorithms with continuous dynamical systems, while also providing practical algorithmic innovations. Our work addresses fundamental questions about the physical nature of Anderson Acceleration that have remained unanswered since its introduction in 1965. Firstly, we prove that AA can be exactly rewritten as an adaptive momentum method and, in the high-resolution limit, converges to a second-order ODE with Variable Effective Mass. Through a Lyapunov energy analysis, we reveal the specific instability mechanism of standard AA: unchecked growth in effective mass acts as negative damping, physically injecting energy into the system and violating dissipation constraints. Conversely, high-resolution analysis identifies an implicit Hessian-driven damping term that provides stabilization in stiff regimes. Leveraging these dynamical insights, we then propose Energy-Guarded Anderson Acceleration (EG-AA), an algorithm that acts as an inertial governor to enforce thermodynamic consistency. Morevoer, our convergence analysis, formulated via the Acceleration Gain Factor, proves that EG-AA improves upon gradient descent by maximizing the geometric contraction of the linear subspace projection while actively suppressing nonlinear approximation errors. Theoretical bounds confirm that EG-AA is no worse than standard AA, and numerical experiments demonstrate strictly improved convergence stability and rates in ill-conditioned convex composite problems compared to standard Anderson mixing.

math.OC↗

arXiv2023

A short report on preconditioned Anderson acceleration method

In this report, we present a versatile and efficient preconditioned Anderson acceleration (PAA) method for fixed-point iterations. The proposed framework offers flexibility in balancing convergence rates (linear, super-linear, or quadratic) and computational costs related to the Jacobian matrix. Our approach recovers various fixed-point iteration techniques, including Picard, Newton, and quasi-Newton iterations. The PAA method can be interpreted as employing Anderson acceleration (AA) as its own preconditioner or as an accelerator for quasi-Newton methods when their convergence is insufficient. Adaptable to a wide range of problems with differing degrees of nonlinearity and complexity, the method achieves improved convergence rates and robustness by incorporating suitable preconditioners. We test multiple preconditioning strategies on various problems and investigate a delayed update strategy for preconditioners to further reduce the computational costs.

math.NA↗

arXiv2022

Composite Anderson acceleration method with dynamic window-sizes and optimized damping

In this paper, we propose and analyze a set of fully non-stationary Anderson acceleration algorithms with dynamic window sizes and optimized damping. Although Anderson acceleration (AA) has been used for decades to speed up nonlinear solvers in many applications, most authors are simply using and analyzing the stationary version of Anderson acceleration (sAA) with fixed window size and a constant damping factor. The behavior and potential of the non-stationary version of Anderson acceleration methods remain an open question. Since most efficient linear solvers use composable algorithmic components. Similar ideas can be used for AA to solve nonlinear systems. Thus in the present work, to develop non-stationary Anderson acceleration algorithms, we first propose two systematic ways to dynamically alternate the window size $m$ by composition. One simple way to package sAA(m) with sAA(n) in each iteration is applying sAA(m) and sAA(n) separately and then average their results. It is an additive composite combination. The other more important way is the multiplicative composite combination, which means we apply sAA(m) in the outer loop and apply sAA(n) in the inner loop. By doing this, significant gains can be achieved. Secondly, to make AA to be a fully non-stationary algorithm, we need to combine these strategies with our recent work on the non-stationary Anderson acceleration algorithm with optimized damping (AAoptD), which is another important direction of producing non-stationary AA and nice performance gains have been observed. Moreover, we also investigate the rate of convergence of these non-stationary AA methods under suitable assumptions. Finally, our numerical results show that some of these proposed non-stationary Anderson acceleration algorithms converge faster than the stationary sAA method and they may significantly reduce the storage and time to find the solution in many cases.

math.NA↗

arXiv2022

Non-stationary Anderson acceleration with optimized damping

Anderson acceleration (AA) has a long history of use and a strong recent interest due to its potential ability to dramatically improve the linear convergence of the fixed-point iteration. Most authors are simply using and analyzing the stationary version of Anderson acceleration (sAA) with a constant damping factor or without damping. Little attention has been paid to nonstationary algorithms. However, damping can be useful and is sometimes crucial for simulations in which the underlying fixed-point operator is not globally contractive. The role of this damping factor has not been fully understood. In the present work, we consider the non-stationary Anderson acceleration algorithm with optimized damping (AAoptD) in each iteration to further speed up linear and nonlinear iterations by applying one extra inexpensive optimization. We analyze this procedure and develop an efficient and inexpensive implementation scheme. We also show that, compared with the stationary Anderson acceleration with fixed window size sAA(m), optimizing the damping factors is related to dynamically packaging sAA(m) and sAA(1) in each iteration (alternating window size $m$ is another direction of producing non-stationary AA). Moreover, we show by extensive numerical experiments that the proposed non-stationary Anderson acceleration with optimized damping procedure often converges much faster than stationary AA with constant damping or without damping.

math.NA↗