Constrained and NoNlinear Optimizer of Least Squares
Author JuliaSmoothOptimizers
21 Stars
Updated Last
10 Months Ago
Started In
March 2019

CaNNOLeS - Constrained and NoNlinear Optimizer of Least Squares

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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 SolverCore 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. A Regularization Method for Constrained Nonlinear Least Squares. Computational Optimization and Applications 76, 961–989 (2020). 10.1007/s10589-020-00201-2

Check CITATION.bib for bibtex.


  1. Follow HSL.jl's MA57 installation if possible. Otherwise LDLFactorizations.jl will be used.
  2. pkg> add CaNNOLeS


using CaNNOLeS, ADNLPModels

# 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],
  x -> [x[1] * x[2] - 1],
stats = cannoles(nls)

Bug reports and discussions

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