Note: interpreting iterative methods convergence with diffusion point of view
arXiv:1304.1760
Originality Synthesis-oriented
AI Analysis
Provides a conceptual framework for interpreting convergence of classical iterative methods, but is primarily explanatory and incremental.
The paper explains convergence speeds of iterative methods (power, Jacobi, Gauss-Seidel) for linear fixed-point problems using a fluid diffusion analogy, providing intuitive understanding of relative performance.
In this paper, we explain the convergence speed of different iteration schemes with the fluid diffusion view when solving a linear fixed point problem. This interpretation allows one to better understand why power iteration or Jacobi iteration may converge faster or slower than Gauss-Seidel iteration.