CaNNOLeS - Constrained and NoNlinear Optimizer of Least Squares

CaNNOLeS is a solver for equality-constrained nonlinear least-squares problems, i.e., optimization problems of the form

min ¹/₂‖F(x)‖²      s. to     c(x) = 0.

It uses other JuliaSmoothOptimizers packages for development. In particular, NLPModels.jl is used for defining the problem, and SolverTools for the output. It also uses HSL.jl's MA57 as main solver, but you can pass linsolve=:ldlfactorizations to use LDLFactorizations.jl.

Cite as

Orban, D., & Siqueira, A. S. (2019). A Regularization Method for Constrained Nonlinear Least Squares (Cahier du GERAD No. G-2019-17). Montréal, QC, Canada: GERAD. doi:10.13140/RG.2.2.11974.52809

Bibtex:

@article{Orban2019Regularization,
  doi = {10.13140/rg.2.2.11974.52809},
  url = {http://rgdoi.net/10.13140/RG.2.2.11974.52809},
  author = {Orban,  Dominique and Siqueira,  Abel Soares},
  language = {en},
  title = {A Regularization Method for Constrained Nonlinear Least Squares},
  publisher = {Unpublished},
  year = {2019}
}

Installation

1. Follow HSL.jl's MA57 installation.
2. pkg> add https://github.com/JuliaSmoothOptimizers/CaNNOLeS.jl

Example

using CaNNOLeS, NLPModels

# Rosenbrock
nls = ADNLSModel(x -> [x[1] - 1; 10 * (x[2] - x[1]^2)], [-1.2; 1.0], 2)
stats = cannoles(nls)

# Constrained
nls = ADNLSModel(x -> [x[1] - 1; 10 * (x[2] - x[1]^2)], [-1.2; 1.0], 2
                 c=x->[x[1] * x[2] - 1], lcon=[0.0], ucon=[0.0])
stats = cannoles(nls)