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Fides.jl

Fides.jl is a Julia wrapper of the Python package Fides.py, which implements an Interior Trust Region Reflective algorithm for boundary constrained optimization problems based on [1, 2]. Fides targets problems on the form:

\[\min_{x \in \mathbb{R}^n} f(x) \quad \mathrm{subject \ to} \quad lb \leq x \leq ub\]

Where f is a continues at least twice-differentaible function, and lb and ub are the lower and upper bounds respectively.

Highlights

  • Boundary-constrained interior trust-region optimization.
  • Recursive reflective and truncated constraint management.
  • Full and 2D subproblem solution solvers.
  • Supports used provided Hessian, and BFGS, DFP, and SR1 Hessian approximations.
  • Good performance for parameter estimating Ordinary Differential Equation models [3].
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Installation

To install Fides.jl in the Julia REPL enter

julia> ] add Fides

or alternatively

julia> using Pkg; Pkg.add("Fides")

Fides is compatible with Julia version 1.10 and above. For best performance we strongly recommend using the latest Julia version.

Getting help

If you have any problems using Fides, here are some helpful tips:

  • Check the Fides Python documentation.
  • Post your questions in the #sciml-sysbio or #math-optimization channel on the Julia Slack.
  • If you have encountered unexpected behavior or a bug, please open an issue on GitHub.

Citation

If you found Fides useful in your work, please cite the following paper:

@article{2022fides,
  title={Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models},
  author={Fr{\"o}hlich, Fabian and Sorger, Peter K},
  journal={PLoS computational biology},
  volume={18},
  number={7},
  pages={e1010322},
  year={2022},
  publisher={Public Library of Science San Francisco, CA USA}
}

References

  1. Coleman, T. F., & Li, Y. (1994). On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds. Mathematical programming, 67(1), 189-224.
  2. Coleman, T. F., & Li, Y. (1996). An interior trust region approach for nonlinear minimization subject to bounds. SIAM Journal on optimization, 6(2), 418-445.
  3. Fröhlich, F., & Sorger, P. K. (2022). Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models. PLoS computational biology, 18(7), e1010322.