NonlinearSolve.jl: Other References You Can Turn to | HackerNoon

Table of Links

Abstract and 1. Introduction

2. Mathematical Description and 2.1. Numerical Algorithms for Nonlinear Equations

2.2. Globalization Strategies

2.3. Sensitivity Analysis

2.4. Matrix Coloring & Sparse Automatic Differentiation

3. Special Capabilities

3.1. Composable Building Blocks

3.2. Smart PolyAlgortihm Defaults

3.3. Non-Allocating Static Algorithms inside GPU Kernels

3.4. Automatic Sparsity Exploitation

3.5. Generalized Jacobian-Free Nonlinear Solvers using Krylov Methods

4. Results and 4.1. Robustness on 23 Test Problems

4.2. Initializing the Doyle-Fuller-Newman (DFN) Battery Model

4.3. Large Ill-Conditioned Nonlinear Brusselator System

5. Conclusion and References

5. Conclusion

Solving systems of nonlinear equations is a fundamental challenge that arises across many scientific domains. This paper presented NonlinearSolve.jl, a high-performance and robust open-source solver for nonlinear systems implemented natively in the Julia programming language. Through extensive numerical experiments on benchmark problems, real-world applications, and scalability tests, we have demonstrated the superior capabilities of NonlinearSolve.jl compared to existing software tools.

Key strengths of NonlinearSolve.jl include its flexible unified API for rapidly experimenting with different solver options, smart automatic algorithm selection for balancing speed and robustness, specialized non-allocating kernels for small systems, automatic sparsity exploitation, and support for Jacobian-free Krylov methods. These features enable NonlinearSolve.jl to reliably solve challenging nonlinear problems, including cases where standard solvers fail while attaining high performance through techniques.

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